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Rib support design and the coal mass rating relationship
No full textThis paper aims to explain the relationship between rib conditions, abutment fracture and coal mass rating (CMR). Understanding the relationship between the three factors provides data for rib support design. The primary function of rib support is to control the failed coal within the abutment zones along the excavation boundary. Several factors influence the shape of the rib abutment zones, including the mining depth, abutment stress, coal strength, roadway orientation and CMR.Borescoping, underground inspections, UCS testing and monitoring instrumentation provided the data needed to understand the rib conditions relating to the abutment fracture characteristics. This data was then used to establish a correlation between CMR and abutment fracture characteristics, offering a potential application of the findings in the design of rib support. The investigation's findings were numerically modelled, revealing the impact of changing CMR values on rib abutment fractures.The data defined three rib abutment zones: primary, secondary and tertiary. The primary rib abutment zone forms the skin of the excavation boundary and is associated with the lateral movement of the fractured coal. The secondary abutment zone resides behind the primary abutment zone and is associated with fractured coal, lacking significant lateral movement in normal conditions. The tertiary abutment zone is defined by a diagonal fracture extending away from the toe or crest of the secondary abutment fracture.The potential roadway development rib support solution is achieved by extending the length of the support element beyond the secondary abutment fracture, securing the primary and secondary abutment zones. The occurrence of the tertiary abutment zone was found to become a potential release plane in areas of high drivage or in response to abutment loads from secondary extraction, which plays a crucial role in the overall rib support solution.</p
Organic and biofouling in membrane distillation for wastewater treatment
No full textThe abstract for this item has not been populated.</p
Study on mechanical and tribological performances of novel V-containing AlCrFeNiV high-entropy alloys and related self-lubricating composites
No full textHigh-entropy alloys have been regarded as a groundbreaking development in the field of materials science since their discovery, as they possess extraordinary properties that surpass those of conventional engineering materials. Their exceptional properties are the consequence of their unique multi-element composition and high configurational entropy, which stabilize simple solid solution phases and reduce the formation of brittle intermetallic compounds, making them suitable for extreme environments. The addition of vanadium to HEA aims to enhance their thermal stability, facilitate the dispersion of nanoparticles, increase their strength at elevated temperatures, reduce their susceptibility to corrosion, and confer additional features such as magnetism and radiation resistance.This work analyzes Al0.5CrFeNiVx-based HEA and their composites' phase composition, microstructure, mechanical characteristics, high-temperature tribological behavior, and wear processes. Arc-melted Al0.5CrFeNiV0.5 HEA exhibits a hardness of 520 HV and a compressive strength of 2476 MPa at room temperature, both of which remain stable up to 600 °C. Its friction coefficient remains steady at 0.4–0.5 over a temperature range from room temperature to 800 °C. The wear of the HEA increases monotonically with temperature and accelerates above 600 °C due to oxidation and surface deterioration.The study further explored the effects of varying vanadium (V) content on Al0.5CrFeNiVx (x = 0.25-1.0) HEA. Increasing V concentration changed the microstructure from a single-phase body-centered cubic (BCC) to a dual-phase BCC/Laves structure with V-rich precipitates and dark secondary phases. This structural transition improved solid solution strengthening and phase stability, increasing hardness from 520 HV at V0.25 to 608 HV at V1.0 and compressive strength from 2476 MPa to 3241 MPa. Although mechanical improvements were made, fracture strain decreased from 34% to 22%, demonstrating that V0.5 and V0.75 compositions provide a balanced strength-ductility trade-off for high-temperature applications. V also made complex oxide layers, mostly vanadium oxides, a protective coating that increased thermal stability and wear resistance. Notably, at 700 °C, the alloy with V1.0 exhibited the lowest wear rate (18 × 10−5 mm³/Nm) and friction coefficient due to oxidative melt wear triggered by the low melting point of V2O5.</p
Harnessing the Power of Plants: Using Phytochemicals to Improve Cognitive Function and Brain Health
No full textThe abstract for this item has not been populated.</p
Vection is enhanced by visual oscillation based on four-stroke apparent motion
No full textIllusions of self-motion (vection) can be improved by adding global visual oscillation to patterns of optic flow. Here we examined whether adding apparent visual oscillation (based on four-stroke apparent motion—4SAM) also improves vection. This apparent vertical oscillation was added to self-motion displays simulating constant velocity leftward self-motion. Our psychophysical experiment found that adding 4SAM oscillation to this optic flow significantly shortened the onset latency, and increased the rated strength, of our participants’ vection. Interestingly, we found that the vection onset latencies in this 4SAM oscillation condition were similar to those produced when “real” oscillation was instead added to the optic flow—even though adding “real” oscillation (based on the global and continuous displacement of dots over time) generally resulted in stronger vection experiences. These results show vection can be enhanced by both “real” and apparent 4SAM visual stimuli indicating self-acceleration. They also confirm that global visual displacements are not required to generate these oscillation-based advantages for vection
Geochemistry of Historical Fires Recorded in Sediments in Southeastern Australia
No full textFire has played a key role in shaping the Australian landscape and biodiversity for millennia; however, as climate change continues to alter the fire regime, understanding how this may impact landscape-scale events remains unknown. At present, our records of past fire events are historically limited or cannot accurately account for changes in fire characteristics, such as severity and intensity. In order to more accurately predict the behaviour of future fire events, there is an urgent need to develop new techniques that can significantly extend our existing fire record. Therefore, the aim of this thesis was to develop two novel techniques, boron (B) isotopes and Fourier Transform Infrared (FTIR) spectroscopy, to determine their suitability as proxies. Three key objectives were addressed to achieve these aims and determine the suitability of the two techniques as proxies for past fire events: 1) To link changes in the B isotope ratio and the FTIR spectra against a fire of known severity to determine their suitability as proxies; 2) Apply these techniques to sites of known fire history to determine their ability to record multiple fire events; 3) Formulate a >100-year record of fire severity and intensity and compare it with existing palaeoclimate records to determine how fire characteristics have changed through time. Sediment cores were collected from fire-prone landscapes in southeastern Australia and analysed for changes in the FTIR spectra and the B isotope ratio.Boron isotopes showed sensitivity to fire severity, where severity was defined as the degree of canopy consumption, and mineral ash composition. High-severity fires were accompanied by increases in the δ11B value (>2SD greater than the mean) due to increased leaching of ashed leaf material enriched in the heavier isotope. Negative excursions in the δ11B value (>2SD less than the mean) were hypothesised to record increased leaching from burnt woody material enriched in 10B. These excursions were associated with erosion events, where rainfall was required to leach B from ash, facilitating adsorption onto clay minerals. In the FTIR spectra, the aromatic/aliphatic ratio showed sensitivity to high-intensity fire events, where fire intensity was defined as the rate of heat transfer from the fire. Higher aromatic/aliphatic ratio values were associated with higher-intensity fire events, suggesting increased high-intensity fire frequency in the last 200 years compared to the last 3000 years. This was hypothesised to stem from the complex interactions between climate, people and vegetation change. Combining the records from the two techniques, it was suggested that a negative excursion in the δ11B value accompanied by a positive excursion in the aromatic/aliphatic ratio was the result of a more significant contribution of bark to the mineral ash fraction, which typically requires higher temperatures for thermal decomposition compared to leaves. Both techniques were found to be suitable proxies for detecting past fire characteristics, extending our existing fire record by decades to millennia. The results provide valuable insights into past fire characteristics, allowing for improved management strategies to prevent future landscape-scale bushfires. It is recommended that future studies apply these techniques to mineral-dominated sediments in a range of environments with varying climate conditions and vegetation communities to determine trends in the fire regime at national to global scales.</p
Watching Web Series: Audience Engagement in the Web Series Ecology
No full textThe abstract for this item has not been populated.</p
Synthesis of N-Heterocycles via Group 10 Catalysis
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Exploring Data-driven Innovation Capabilities and Their Effects in the Digital Economy
No full textIn the era of ongoing digital transformation, industries are increasingly relying on data-driven innovation (DDI) for sustainable growth. From nearly accurate decision-making to achieving competitive advantage, DDI takes the lead in every aspect of businesses across all sectors. However, a considerable number of firms operating in the digital economy have yet to generate optimum results from their DDI projects due to the lack of requisite knowledge and capabilities. Despite the paramount implications of DDI in the digital economy, only a few studies have attempted to present a comprehensive theoretical framework for architecting and developing DDI. This gap in literature has significant implications for businesses where innovation is considered core business and necessary to stay competitive. Because of limited theoretical development in the field, there is a dearth of empirical investigations on the dimensions of data-driven innovation capability (DDIC) and their effects on business outcomes. Comprehensive theorization is the necessary first step to capture the complexity of DDI and provide a sound conceptual basis for empirical validation to enable businesses to pursue strategic market agility and competitive performance.Drawing upon several well-accepted theories from the management and marketing literature, specifically, the resource-based view (RBV), dynamic capability (DC), and market orientation theories, this thesis addresses these research gaps by carrying out systematic literature reviews, thematic analysis, semi-structured interviews (n=35), pretest (n=30), pilot study (n=108), and two rounds of cross-sectional surveys (n=312, n=448) of DDI managers across digital marketplaces in Australia as a research context. The findings of the study establish data-driven innovation capability (DDIC) and data-driven environmental innovation capability (DDEIC) as third-order multidimensional models. DDIC consists of three second-order dimensions (i.e., market-orientation capability, infrastructure capability, and innovation capability) and six sub-dimensions (i.e., data, technology, customer orientation, competitor orientation, knowledge and training & development). DDEIC is comprised of four second-order dimensions (i.e., platform capability, management capability, market capability, and environmental capability) and twelve sub-dimensions (data, technology, ethics, customer orientation, competitor orientation, cross-functional integration, data-driven culture, organization learning, training & development, internal environment, external environment, and environmental practices). The study findings confirm the influence of DDIC on firms’ strategic market agility and strategic competitive performance as well as the impact of DDEIC on firms’ sustainable innovation and new data product performance. Based on the theoretical underpinning and empirical results, the thesis makes novel contributions in advancing theories, practices, and policy implications through three peer-reviewed publications in high-impact journals: (all A-ranked journals) and one submitted research paper in the Journal of Product Innovation Management (A* ranked journal).Study 1 presents a conceptual framework of firms’ data-driven innovation capability in the digital economy by conducting a systematic literature review, and thematic analysis of the extant data-driven innovation literature. Based on the exploration of previous research and relevant theories, this study recognizes three fundamental dimensions (i.e., management capabilities, infrastructure capabilities, and talent capabilities) and seven underlying subdimensions (customer orientation, competitor orientation, cross-functional integration, data, technology, technical knowledge, and data-driven business model knowledge) of data-driven innovation capability. Drawing from a resource-based view, dynamic capability, and market orientation, the study develops a data-driven innovation capability model.Study 2 empirically tests and validates the conceptual framework of data-driven innovation capability based on a systematic literature review, thematic analysis, and survey data collected from data-driven innovation managers (n=312) of multiple Australian industries. Utilizing the PLS-SEM technique, the results confirm data-driven innovation capability as a higher-order hierarchical model having three second-order dimensions, including market-orientation capability, infrastructure capability, and innovation capability, and six subdimensions. The study findings also recognize the significant positive relationship between data-driven innovation capability and strategic competitive performance having a key mediating role of strategic market agility.Study 3 empirically tests and validates the conceptual model of data-driven environmental innovation capability (DDEIC) by carrying out systematic literature reviews, thematic analysis, semi-structured interviews (n=35), pretest (n=30), pilot study (n=108), and survey of managers (n=448) of Australian fast fashion industries. The findings establish data-driven environmental innovation capability as a third-order multidimensional model comprised of four second-order dimensions such as platform capability, management capability, market capability, and environmental capability and their underlying subdimensions. The results also prove the direct significant effect of data-driven environmental innovation capability on new data product performance as well as the partial mediating effect of sustainable innovation.Study 4, drawing upon a systematic literature review and thematic analysis as well as utilizing dynamic capability and market orientation theories, unveils a standardized process for architecting and developing data-driven innovation in the digital economy. The findings recommend that data-driven firms implement a systematic seven-step model, including product conceptualization, data acquisition, refinement, storage, retrieval, distribution, presentation, and market feedback to build analytics-based data products.Overall, the study contributes to the literature by proposing and empirically testing multidimensional hierarchical models of data-driven innovation capability and data-driven environmental innovation capability in the digital economy as well as providing evidence for their significant positive impacts on firms’ organizational (i.e., strategic market agility, strategic competitive performance, new data product performance) and environmental outcomes (i.e., sustainable innovation). Additionally, the findings unveil a synchronized seven-step process for designing and developing data-driven innovation in the digital market providing valuable theoretical as well as practical guidance for competitive business strategy.</p
Spatial statistical inference from a decision-theoretic viewpoint with application to non-Gaussian environmental data
No full textIn this thesis, I address some challenges related to spatial-statistical inference on noisy, incomplete, non-Gaussian, and potentially large environmental spatial datasets. Established theory in spatial statistics relies on the twin pillars of Gaussian spatial processes and the ubiquitous squared-error loss function to construct models and subsequently distil them into optimal predictions and uncertainty measures (e.g., kriging and kriging variances). The appropriateness of both Gaussian spatial models and the squared-error loss function is brought into question in many environmental science problems for the following reasons. First, environmental spatial data are frequently non-Gaussian (e.g., skewed and positive). Second, from a decision-theoretic viewpoint, the squared-error loss function may be inappropriate because it implies that the consequences for under-predicting and over-predicting the process by the same amount are the same whereas, in many applications, one kind of error can carry much more serious consequences than the other. This thesis presents novel methodology to model non-Gaussian spatial data and to handle the associated decision problem of optimal spatial prediction for non-Gaussian spatial processes in the presence of an asymmetric loss structure.On loss functions, I develop spatial-statistical inference on positive-valued spatial processes by replacing squared-error loss in the spatial-prediction problem with the family of asymmetric Cressie-Read power-divergence loss functions. I investigate the consequences of the replacement by characterising the resulting optimal spatial predictor, its properties, and associated uncertainty quantification. In addition, I develop a new method to characterise loss function asymmetry for positive-valued spatial processes; I illustrate methods for calibrating the power-divergence loss function to the decision problem at hand; and I present useful closed-form results for log-Gaussian spatial models, which are commonly used to analyse skewed, positive-valued spatial data. An application is given to a real dataset of soil zinc contamination in a floodplain of the Meuse River in the Netherlands. On modelling non-Gaussian spatial processes, I use spatial copulas for modelling flexibility. Copula-based models can capture non-Gaussian marginal behaviour as well as non-Gaussian spatial dependence structures. However, spatial copula models lack a general formulation of a hierarchical spatial-statistical modelling framework to enable noisy, incomplete, and large spatial data to be used for prediction of a latent scientific spatial process. I establish a fully Bayesian hierarchical spatial-statistical modelling framework for spatial copula models that enables inference on a latent scientific process from noisy, incomplete, non-Gaussian and large spatial data. Other technical innovations are presented, such as elliptical spatial copulas with structured covariance matrices for efficient computations with large spatial datasets, and a modified hierarchical-statistical model structure that handles the change-of-support between different spatial resolutions. These innovations are then applied to spatially predict a dataset of non-Gaussian, remotelysensed atmospheric methane concentrations over a region of coal-mining activity in the Bowen Basin in Queensland, Australia.</p