Science

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  • (2022) Zhang, Qi
    Thesis
    As a dominant terrestrial ecosystem of the Earth, forest environments play profound roles in ecology, biodiversity, resource utilization, and management, which highlights the significance of forest characterization and monitoring. Some forest parameters can help track climate change and quantify the global carbon cycle and therefore attract growing attention from various research communities. Compared with traditional in-situ methods with expensive and time-consuming field works involved, airborne and spaceborne remote sensors collect cost-efficient and consistent observations at global or regional scales and have been proven to be an effective way for forest monitoring. With the looming paradigm shift toward data-intensive science and the development of remote sensors, remote sensing data with higher resolution and diversity have been the mainstream in data analysis and processing. However, significant heterogeneities in the multi-source remote sensing data largely restrain its forest applications urging the research community to come up with effective synergistic strategies. The work presented in this thesis contributes to the field by exploring the potential of the Synthetic Aperture Radar (SAR), SAR Polarimetry (PolSAR), SAR Interferometry (InSAR), Polarimetric SAR Interferometry (PolInSAR), Light Detection and Ranging (LiDAR), and multispectral remote sensing in forest characterization and monitoring from three main aspects including forest height estimation, active fire detection, and burned area mapping. First, the forest height inversion is demonstrated using airborne L-band dual-baseline repeat-pass PolInSAR data based on modified versions of the Random Motion over Ground (RMoG) model, where the scattering attenuation and wind-derived random motion are described in conditions of homogeneous and heterogeneous volume layer, respectively. A boreal and a tropical forest test site are involved in the experiment to explore the flexibility of different models over different forest types and based on that, a leveraging strategy is proposed to boost the accuracy of forest height estimation. The accuracy of the model-based forest height inversion is limited by the discrepancy between the theoretical models and actual scenarios and exhibits a strong dependency on the system and scenario parameters. Hence, high vertical accuracy LiDAR samples are employed to assist the PolInSAR-based forest height estimation. This multi-source forest height estimation is reformulated as a pan-sharpening task aiming to generate forest heights with high spatial resolution and vertical accuracy based on the synergy of the sparse LiDAR-derived heights and the information embedded in the PolInSAR data. This process is realized by a specifically designed generative adversarial network (GAN) allowing high accuracy forest height estimation less limited by theoretical models and system parameters. Related experiments are carried out over a boreal and a tropical forest to validate the flexibility of the method. An automated active fire detection framework is proposed for the medium resolution multispectral remote sensing data. The basic part of this framework is a deep-learning-based semantic segmentation model specifically designed for active fire detection. A dataset is constructed with open-access Sentinel-2 imagery for the training and testing of the deep-learning model. The developed framework allows an automated Sentinel-2 data download, processing, and generation of the active fire detection results through time and location information provided by the user. Related performance is evaluated in terms of detection accuracy and processing efficiency. The last part of this thesis explored whether the coarse burned area products can be further improved through the synergy of multispectral, SAR, and InSAR features with higher spatial resolutions. A Siamese Self-Attention (SSA) classification is proposed for the multi-sensor burned area mapping and a multi-source dataset is constructed at the object level for the training and testing. Results are analyzed by different test sites, feature sources, and classification methods to assess the improvements achieved by the proposed method. All developed methods are validated with extensive processing of multi-source data acquired by Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR), Land, Vegetation, and Ice Sensor (LVIS), PolSARproSim+, Sentinel-1, and Sentinel-2. I hope these studies constitute a substantial contribution to the forest applications of multi-source remote sensing.

  • (2021) Ly, Kongmeng
    Thesis
    The management of transboundary river basins across developing countries, such as the Lower Mekong River Basin (LMB), is frequently challenging given the development and conservation divergences of the basin countries. Driven by needs to sustain economic performance and reduce poverty, the LMB countries are embarking on significant land use changes in the form hydropower dams, to fulfill their energy requirements. This pathway could lead to irreversible changes to the ecosystem of the Mekong River, if not properly managed. This thesis aims to explore the potential effects of changes in land use —with a focus on current and projected hydropower operations— on the Lower Mekong River network streamflow and instream water quality. To achieve this aim, this thesis first examined the relationships between the basin land use/land cover attributes, and streamflow and instream water quality dynamics of the Mekong River, using total suspended solids and nitrate as proxies for water quality. Findings from this allowed framing challenges of integrated water management of transboundary river basins. These were used as criteria for selecting eWater’s Source modelling framework as a management tool that can support decision-making in the socio-ecological context of the LMB. Against a combination of predictive performance metrics and hydrologic signatures, the model’s application in the LMB was found to robustly simulate streamflow, TSS and nitrate time series. The model was then used for analysing four plausible future hydropower development scenarios, under extreme climate conditions and operational alternatives. This revealed that hydropower operations on either tributary or mainstream could result in annual and wet season flow reduction while increasing dry season flows compared to a baseline scenario. Conversely, hydropower operation on both tributary and mainstream could result in dry season flow reduction. Both instream TSS and nitrate loads were predicted to reduce under all three scenarios compared to the baseline. These effects were found to magnify under extreme climate conditions, but were less severe under improved operational alternatives. In the LMB where hydropower development is inevitable, findings from this thesis provide an enhanced understanding on the importance of operational alternatives as an effective transboundary cooperation and management pathway for balancing electricity generation and protection of riverine ecology, water and food security, and people livelihoods.

  • (2022) Cao, Jun
    Thesis
    This thesis focuses on the development and applications of magnetic resonance electrical properties tomography (MREPT), which is an emerging imaging modality to noninvasively obtain the electrical properties of tissues, such as conductivity and permittivity. Chapter 2 describes the general information about human research ethics, MRI scanner, MR sequence and the method of phase-based MREPT implemented in this thesis. Chapter 3 examines the repeatability of phase-based MREPT in the brain conductivity measurement using balanced fast field echo (bFFE) and turbo spin echo (TSE) sequences, and investigate the effects of compressed SENSE, whole-head B_1 shimming and video watching during scan on the measurement precision. Chapter 4 investigates the conductivity signal in response to short-duration visual stimulus, compares the signal and functional activation pathway with that of BOLD, and tests the consistency of functional conductivity imaging (funCI) with visual stimulation across participants. Chapter 5 extends the use of functional conductivity imaging to somatosensory stimulation and trigeminal nerve stimulation to evaluate the consistency of functional conductivity activation across different types of stimuli. In addition, visual adaptation experiment is performed to test if the repetition suppression effect can be observed using funCI. Chapter 6 explores if resting state conductivity networks can be reliably constructed using resting state funCI, evaluates the consistency of persistent homology architectures, and compares the links between nodes in the whole brain. Chapter 7 investigates the feasibility of prostate conductivity imaging using MREPT, and distinctive features in the conductivity distribution between healthy participants and participants with suspected abnormalities.

  • (2022) Shahriari, Siroos
    Thesis
    Time series models are used to model, simulate, and forecast the behaviour of a phenomenon over time based on data recorded over consistent intervals. The digital era has resulted in data being captured and archived in unprecedented amounts, such that vast amounts of information are available for analysis. Feature-rich time-series datasets are one of the data sets that have become available due to the expanding trend of data collection technologies worldwide. With the application of time series analysis to support financial and managerial decision-making, the development and advancement of time series models in the transportation domain are unavoidable. As a result, this thesis redefines time series models for transportation planning use with the following three aims: (1) To combine parametric and bootstrapping techniques within time series models; (2) to develop a time series model capable of modelling both temporal and spatial dependencies in time-series data; and (3) to leverage the hierarchical Bayesian modelling paradigm to accommodate flexible representations of heterogeneity in data. The first main chapter introduces an ensemble of ARIMA models. It compares its performance against conventional ARIMA (a parametric method) and LSTM models (a non-parametric method) for short-term traffic volume prediction. The second main chapter introduces a copula time series model that describes correlations between variables through time and space. Temporal correlations are modelled by an ARMA-GARCH model which enables a modeller to describe heteroscedastic data. The copula model has a flexible correlation structure and is used to model spatial correlations with the ability to model nonlinear, tailed and asymmetric correlations. The third main chapter provides a Bayesian modelling framework to raise awareness about using hierarchical Bayesian approaches for transport time series data. In addition, this chapter presents a Bayesian copula model. The combination of the two models provides a fully Bayesian approach to modelling both temporal and spatial correlations. Compared with frequentist models, the proposed modelling structures can incorporate prior knowledge. In the fourth main chapter, the fully Bayesian model is used to investigate mobility patterns before, during and after the COVID-19 pandemic using social media data. A more focused analysis is conducted on the mobility patterns of Twitter users from different zones and land use types.

  • (2022) Wang, Shuangyue
    Thesis
    Two-dimensional transition metal dichalcogenide (TMD) nanocrystals (NCs) exhibit unique optical and electrocatalytic properties. However, the growth of uniform and high-quality NCs of monolayer TMD remains a challenge. Until now, most of them are synthesized via solution-based hydrothermal process or ultrasonic exfoliation method, in which the capping ligands introduced from organic solution often quench the optical and electrocatalytic properties of TMD NCs. Moreover, it is difficult to homogeneously disperse the solution-based TMD NCs on a substrate for device fabrication since the dispersed NCs can easily aggregate. Here, we put forward a novel CVD method to grow closely-spaced TMD NCs and explored the growth mechanism and attempts on the size control. Their applications acting as electrocatalysts and adhesion layer for Au film deposition have been also well displayed. Through the whole chapters of this thesis, the following aspects are highlighted: 1. MoS2 and other TMD nanocrystals have been grown on the c-plane sapphire. The surface oxygen vacancies determine the density of TMD nanocrystals. The MoS2 nanocrystals demonstrate excellent hydrogen evolution reaction and surface-enhanced Raman scattering performance owing to the abundant edges. 2. Deep insights into the growth of MoS2 nanograins have been explored. The surface step edges and lattice structures of the underlying sapphire substrates have a significant influence on the growth behaviors. The step edges could modulate the aggregation of MoS2 nanograins to form unidirectional triangular islands. The Raman spectra of MoS2 demonstrate a linear relationship with the crystal size of MoS2. 3. The orientation of sapphire substrate has an of importance effect on the critical size of MoS2 nanocrystals. The MoS2 nanocrystals have the smallest size on the r-plane sapphire, besides, the MoS2 on r-plane sapphire demonstrates the sintering-resistance feature, which is attributed to the edge-pinning effect when MoS2 edges are anchored on the sapphire surface. 4. The MoS2 nanocrystalline layer was utilized as the adhesion layer for Au film depositing on a sapphire substrate. The Au films on MoS2 displayed superior transmittance and electrical conductivity as well as outstanding thermal stability, which lay in the strong binding of Au film with MoS2 nanocrystalline layer.

  • (2022) Kaur, Sandeep
    Thesis
    Advances in molecular biology data collection, leading to the accumulation of large amounts of diverse data, call for novel computational approaches to enable their effective analysis. This thesis explored the application of visual-analytics-driven bioinformatics approaches to four biomolecular data-driven challenges. For analysing time-series omic and multiomic data, a novel method, Minardo-Model, was developed. Minardo-Model can identify key events (e.g. phosphorylation) from such time-series data and temporally order them. To visualise the inferred order of events, two novel visualisation approaches, event maps and event sparklines, were developed. Minardo-Model was tested using two time-series datasets and in both cases, the event orderings derived by this method correlated with prior knowledge. To streamline the use of experimental 3D protein structures for analysing sequence variants, a novel method was developed and integrated into Aquaria. For variants specified in the HGVS notation, the method identifies and displays a best matching structure. Additionally, for each variant specified, all structures spanning the variant, and containing the exact variant (missense only), along with sequence features retrieved from external resources, are summarised. The developed approach was used to analyse variants in human ACE2, and SARS-CoV-2 spike, revealing novel insights. For pathogenic bacterial isolates characterised using multilevel genome typing (MGT), the MGTdb web service was developed. MGTdb, enables upload of isolates as sequence reads or extracted alleles, which are processed and assigned the MGT-identifiers. The features of MGTdb, such as interactive visualisation tools, data download and export to external software, enable epidemiological exploration in the context of the local or global database of isolates. The usability of MGTdb was successfully demonstrated through three case studies. For identifying insertion sequences (IS) from short-read sequencing data, a novel method, WiIS, was developed. WiIS was tested on Bordetella pertussis isolates, for which both short-read (test data) and long-read sequences (ground truth) were available - WiIS was found to have high precision and recall. It also outperformed other published tools in identifying IS in B. pertussis genomes. The novel bioinformatics methods developed in this thesis enable novel analysis of a wide variety of data thus providing insight into various biomolecular processes.

  • (2022) Nguyen, Minh Triet
    Thesis
    Singlet fission is a photo-physical process that generates two triplet excitons from one singlet exciton and can potentially enhance efficiency in photovoltaic systems. The combination of photovoltaics and singlet fission is a novel field for solar energy conversion when there is much interest in renewable, non-destructive, and continuously available energy sources. Singlet fission can also overcome thermalization losses in photovoltaics, which happens in traditional cells when the incident photon energy is higher than the silicon bandgap energy, using a carrier multiplication mechanism. This thesis will design, construct, and characterize photovoltaic devices incorporating singlet fission materials to study singlet fission in practical application. The research focuses on materials characterization, spin dynamics, and electron transfers between acene and the semiconductor layer in Au/TiO2 ballistic cells, and the incorporation of singlet fission layers on silicon-based cell structures. In detail, a set of investigations was developed and summarized by implementing singlet fission materials into a state-of-the-art ballistic photovoltaic device and silicon-based solar cell. The studies demonstrate proof of concept and rationally explain the process. The first part of the thesis investigates thin films of pentacene, TIPS-pentacene, and tetracene via crystallinity, morphology, absorption, and thickness characterization. Additionally, Au and TiO2 layers in Schottky device structures were optimized to achieve the best performance for energy transfer from an applied dye layer (merbromin). The drop-casted dye layer influences the device performance by increasing short-circuit current and open-circuit voltage, demonstrating the ability of charge transfer between the device and the applied film. This device structure provides a test bed for studying charge and energy transfer from singlet fission films. The latter part of the thesis describes several investigations to understand singlet fission in a thin film using this architecture. Magneto-photoconductivity measurements were primarily used to observe the spin dynamics via photoconductivity under an external magnetic field. Control experiments with bare Au/TiO2 devices showed no observable magneto-photoconductivity signal. In contrast, devices with pentacene and tetracene singlet fission layers showed a strong magnetoconductivity effect caused by ballistic electron transfer from the singlet fission layer into the TiO2 n-type semiconductor through an ultra-thin gold layer inserted between the layers. A qualitatively different behavior is seen between the pentacene and tetracene, which reveals that the energy alignment plays a crucial part in the charge transfer between the singlet fission layer and the device. The last section investigates the application of pentacene and tetracene evaporated thin-films as sensitizer layers to a silicon-based solar cell. The optimized Si cell structure with the annealing treatment improved the cell's performance by increasing short-circuit current and open-circuit voltage. The deposition of pentacene and tetracene as sensitizer layers into the device showed some results but posed several challenges that need to be addressed. As the current-voltage and external quantum efficiency measurements were taken, it was observed that material interfaces need to be designed to fully achieve the singlet fission of the acene layer into the Si devices.

  • (2023) Dela Cruz, Michael Leo
    Thesis
    Biodegradable implant materials are more appropriate for temporary support applications compared with their inert counterparts since the former requires no removal surgery because they naturally degrade and eventually dissolve completely during healing. Iron and its alloys are a possible substitute for the commercial magnesium biodegradable implants because of their superior mechanical properties and slower corrosion rates. The addition of manganese and silicon in iron imparts another interesting property to the material–the shape memory effect. There is copious research on the structure and properties of the biodegradable face centred cubic (FCC) Fe-30Mn-6Si shape memory alloy (SMA) that exhibits the reversible FCC austenite to hexagonal close packed (HCP) ε-martensite transformation. However, recent advances in additive manufacturing of metals, brought by the development of the laser powder bed fusion (LPBF) technique, warrant the need for an investigation on the adaptability of the technique in fabricating this alloy composition. The LPBF technique is limited by the need for specialty raw material powder, and this thesis extends the application of the technique in fabricating the Fe-30Mn-6Si shape memory alloy (SMA) from homogenised powder precursors. More so, LPBF processing of Fe-30Mn-6Si alloy from either pre-alloyed powder or blended powder has not been reported. To successfully fabricate a Fe-30Mn-6Si LPBF product, the influence of key LPBF processing parameters on product quality was identified as a major challenge. This was addressed by investigating the influence of laser power, laser scan speed, laser re-scanning, and their equivalent input energy on the relative density and defect formation. A relative density of over 99% with few processing defects was achieved using the optimised parameters of 175 W laser power, 400 mm/s scan speed, and no re-scanning. The influence of these parameters on the solidification microstructure was also investigated using key techniques, such as X-ray diffraction (XRD) and scanning electron microscopy (SEM) in conjunction with energy dispersive spectroscopy (EDS) and electron backscatter diffraction (EBSD). Further, the simulated thermal profile of the melt pool region as a function of process parameters via single scan track experiments was calculated using the finite element method (FEM). These data were used to explain the key microstructural features observed in the as-solidified microstructure of the LPBF alloy as a function of the processing parameters. The mechanical properties of the LPBF alloy were then assessed by hardness and tensile testing and then compared with a reference alloy produced by arc melting. The hardness of the LPBF as-built alloy was ∼20% higher than the reference alloy. To identify the factors affecting the increased hardness of the former, the influence of grain size and morphology, crystallographic texture, phase constituents (mainly austenite and martensite), and residual strain were investigated. The hardness of the reference alloy was affected mainly by the grain size and residual strain, but for the LPBF-built alloy, the relative volume fractions of austenite and martensite strongly influenced the hardness. Meanwhile, the tensile properties of the LPBF alloy, such as the yield stress, ultimate tensile stress, and ductility, were adversely affected by the internal defects present, such that high temperature homogenisation and hot isostatic pressing (HIP) post-process treatments were investigated to improve these properties. The homogenisation and HIP treatments increased both the tensile strength and ductility of the LPBF-built alloy. Homogenisation altered the grain morphology by promoting recrystallisation and grain growth, and this increased the tensile strength by ∼80%. The hardness, however, decreased due to a reduction in the volume fraction of HCP martensite in the FCC austenitic microstructure. HIP retained some of the columnar microstructure generated by the LPBF process, marginally increased the density, and increased the tensile strength by ∼65%. The improvement in tensile properties through these post-process treatments allowed for the measurement of LPBF alloy’s shape memory behaviour, whereby a tensile recovery strain of 2% was achieved for the HIP-treated alloy. Finally, the biocorrosion behaviour of the LPBF-processed and HIP-treated alloy was investigated, whereby the in vitro corrosion potential and current density of the alloy were determined to be -769 mV and 5.6 μA/cm2, respectively, indicating a reasonable corrosion rate for this material. Overall, this thesis enabled the first demonstration of the shape memory effect in an LPBF-built Fe-based alloy fabricated from homogenised powder, an alloy which also exhibits biodegradable properties.

  • (2023) Broadbent, Gail
    Thesis
    To obviate significant and growing road vehicle greenhouse gas (GHG) emissions contributing to climate change, transitioning to battery electric vehicles (BEV) is urgently required to maximise fleet emissions reductions soonest, deploying the most suitable available technology. Many countries have implemented policies to incentivise electric vehicle (EV) uptake, which have been well studied. This thesis undertakes novel research by employing a case study of New Zealand to examine consumer responses to EV policies implemented in 2016, plus two mooted policies. Questionnaires and interviews surveyed private motorists from a demand perspective, capturing quantitative and qualitative data to assess attitudes, values, and perceptions of EVs, awareness of government policies, and to reveal those most popular. Employing a unique innovation, four motorist groups (segmented by attitude to EVs, which influences adoption rates) were compared. As additional novelty the role of communication channels, including print media, in influencing consumer behaviour was investigated. Results revealed New Zealand’s conventional motorists, in contrast with EV owners, had low policy awareness, confirming international findings. EV Positives, the next-most ‘EV ready’ segment, favoured policies designed to reduce EV purchase price and increase nationwide charger deployment. Concordant with social marketing research, governments should focus on such buyers’ preferences. Furthermore, to improve BEV readiness, disseminating updated information about EVs via multiple communication channels could shift perceptions of EVs from ‘expensive and inconvenient’ to ‘fun and economical’. Thus, two key concepts namely purchase price-parity and charging infrastructure availability, were incorporated into models specifically for Australia, where policies are limited, to investigate the feasibility of transitioning Australia’s road vehicle fleet to electromobility to achieve net-zero emissions by 2050. A national scale, integrated, macro-economic, system dynamics model (iSDG Australia) was used innovatively to project Australia’s future road transport demand, vehicle mix, energy consumption and GHG emissions. Firstly, the model applied numerous ‘adoption target’ scenarios comparing them to Business-as-Usual; secondly, various combinations of policy options were modelled to project potential outcomes and implementation costs. Based on the assumptions, results suggest emissions reductions are maximised by the fastest passenger vehicle fleet transition to BEVs, entailing declining but ongoing transformational government policy support to achieve net-zero by 2050.

  • (2023) Zillur Rahman, Kazi Mohammad
    Thesis
    Current healthcare infection surveillance rarely monitors the distribution of antimicrobial resistance (AMR) in bacteria beyond clinical settings in Australia and overseas. This results in a significant gap in our ability to fully understand and manage the spread of AMR in the general community. This thesis explores whether wastewater-based monitoring could reveal geospatial-temporal and demographic trends of antibiotic-resistant bacteria in the urban area of Greater Sydney, Australia. Untreated wastewater from 25 wastewater treatment plants sampled between 2017 and 2019 consistently contained extended-spectrum β-lactamases-producing Enterobacteriaceae (ESBL-E) isolates, suggesting its endemicity in the community. Carbapenem-resistant Enterobacteriaceae (CRE), vancomycin-resistant enterococci (VRE), and methicillin-resistant Staphylococcus aureus (MRSA) isolates were occasionally detected. Demographic and healthcare infection-related factors correlated with the ESBL-E load, and demographic variables influenced the VRE load. In contrast, the healthcare infection-related factor mainly drove the CRE load. These findings demonstrate the potential of wastewater-based surveillance to understand the factors driving AMR distribution in the community. The subsequent thesis work covers the genomic characterisation of selected ESBL-E and CRE wastewater isolates to reveal their nature, origin, and underlying resistance mechanisms. Phylogenetic analysis showed that Escherichia coli isolates were related to high-risk human-associated pandemic clones and non-human-associated clones. The Klebsiella pneumoniae and K. variicola isolates were related to globally disseminated and emerging human-associated clones, and some were detected for the first time in Australia. Genomic analysis also indicated novel resistance mechanisms against nitrofurantoin in E. coli, and against piperacillin/tazobactam and ticarcillin/clavulanic acid in Klebsiella isolates. The virulence gene content indicated that some E. coli and Klebsiella isolates were likely associated with infections, while the asymptomatic carriage was suggested for other isolates. These results demonstrate a clear potential for wastewater-based surveillance to monitor the emergence and dissemination of resistance in non-clinical isolates, and in particular, isolates from the community and non-human sources. The findings of this study can complement healthcare infection surveillance to inform management strategies to mitigate the emergence and dissemination of AMR and important human pathogens in the general community.