Science

Publication Search Results

Now showing 1 - 10 of 55
  • (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.

  • (2022) Zhao, Runqing
    Thesis
    Emerging modes of air transport such as autonomous airport shuttle and air taxi are potentially efficient alternatives to current transport practices such as bus and train. This thesis examines bus shuttle service within an airport and air metro as two examples of network design. Within an airport, the bus shuttle serves passengers between the terminals, train stations, parking lots, hotels, and shopping areas. Air metro is a type of pre-planned service in urban air mobility that accommodates passengers for intra- or inter-city trips. The problems are to optimise the service, and the outputs including the optimal fleet size, dispatch pattern and schedule. Based on the proposed time-space networks, the service network design problems are formulated as mixed integer linear programs. The heterogeneous multi-type bus fleet case and stochastic demand case are extended for the airport shuttle case, while a rolling horizon optimisation is adopted for the air metro case. In the autonomous airport inter-terminal bus shuttle case, a Monte Carlo simulation-based approach is proposed to solve the case with demand stochasticity, which is then further embedded into an "effective" passenger demand framework. The "effective" demand is the summation of mean demand value and a safety margin. By comparing the proposed airport shuttle service to the current one, it is found that the proposed service can save approximately 27% of the total system cost. The results for stochastic problem suggest estimating the safety margin to be 0.3675 times of the standard deviation brings the best performance. For the second case, the service network design is extended with a pilot scheduling layer and simulation is undertaken to compare the autonomous (pilot-less) and piloted service design. The results suggest that an autonomous air metro service would be preferable if the price of an autonomous aircraft is less than 1.6 times the price of a human-driven one. The results for rolling horizon optimisation suggest to confirm the actual demand at least 45 minutes prior to departure. Based on data from the Sydney (Australia) region, the thesis provides information directly relevant for the service network design of emerging modes of air transport in the city.

  • (2021) Alohali, Ruaa Tawfiq A
    Thesis
    The Arabian basin was subject to several tectonic events, including Lower Cambrian Najd rifting, the Carboniferous Hercynian Orogeny, Triassic Zagros rifting, and the Early/Cretaceous and Late/Tertiary Alpine orogenic events. These events reactivated Precambrian basement structures and affected the structural configuration of the overlying Paleozoic cover succession. In addition to a 2D seismic array and several drill well logs, a newly acquired, processed 3D seismic image of the subsurface in part of the basin covering an area of approximately 1051 km2 has been provided to improve the understanding of the regional tectonic evolution associated with these deformation events. In this study, a manual interpretation is presented of six main horizons from the Late Ordovician to the Middle Triassic. Faults and folds were also mapped to further constrain the stratigraphic and structural framework. Collectively, this data is used to build a geological model of the region and develop a timeline of geological events. Results show that a lower Paleozoic sedimentary succession between the Late Silurian to the Early Permian was subject to localised tilting, uplift, and erosion during the Carboniferous Hercynian Orogeny, forming a regional unconformity. Subsequent deposition occurred from the Paleozoic to the Mesozoic, producing a relatively thick, conformable, upper succession. The juxtaposition of the Silurian rocks and Permian formations allows a direct fluid flow between the two intervals. Seismic analysis also indicated two major fault generations. A younger NNW-striking fault set with a component of reverse, east-side-up displacement affected the Lower Triassic succession and is most likely related to the Cretaceous and Tertiary Alpine Events that reactivated the Najd fault system. These fault structures allow vertical migration that could act as conduits to form structural traps. Manual mapping of fault structures in the study area required significant time and effort. To simplify and accelerate the manual faults interpretation in the study area, a fault segmentation method was developed using a Convolutional Neural Network. This method was implemented using the 3D seismic data acquired from the Arabian Basin. The network was trained, validated, and tested with samples that included a seismic cube and fault images that were labelled manually corresponding to the seismic cube. The model successfully identified faults with an accuracy of 96% and an error rate of 0.12 on the training dataset. To achieve a more robust model, the prediction results were further enhanced using postprocessing by linking discontinued segments of the same fault and thus, reducing the number of detected faults. This method improved the accuracy of the prediction results of the proposed model using the test dataset by 77.5%. Additionally, an efficient framework was introduced to correlate the predictions and the ground truth by measuring their average distance value. This technique was also applied to the F3 Netherlands survey, which showed promising results in another region with complex fault geometries. As a result of the automated technique developed here, fault detection and diagnosis were achieved efficiently with structures similar to the trained dataset and has a huge potential in improving exploration targets that are structurally controlled by faults.

  • (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.