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MEMS-based nanopositioning for on-chip high-speed scanning probe microscopy
No full textNanopositioners constitute a crucial component of numerous emerging scientific instruments due to their ability to produce displacements with nanometer or sub-nanometer precision. In particular, these devices are predominantly used in scanning probe microscopes (SPMs) to position samples beneath the probe. The positioning precision of an incorporated nanopositioner directly affects the imaging quality of these microscopes. Since SPMs are mechanical microscopes, their imaging frame rate also depends on the speed of the implemented nanopositioner. In this research, the micro electromechanical system (MEMS) technology, as an alternative to macroscale technology, is used to realize high-speed on-chip nanopositioners for SPMs. Atomic force microscopes (AFMs), as an important subset of SPMs, are also used to test the capability of these nanopositioners in imaging. A comprehensive study is conducted on previously reported MEMS nanopositioners proposed for different applications. Various actuation and sensing techniques, which are viable to be incorporated in MEMS nanopositioners, are presented and their characteristics are thoroughly discussed. The design concerns relevant to electrostatic MEMS nanopositioners are discussed using analytical models. These models are later used as the baseline for designing novel electrostatic MEMS nanopositioners. These nanopositioners are fabricated and fully characterized as explained in different chapters in this thesis. All proposed nanopositioners are also used within an AFM for imaging. In addition, closed-loop feedback controllers are implemented for a number of the nanopositioners to attain raster and non-raster scans with a superior tracking performance. Both serial and parallel kinematic mechanisms are attempted for the implementation of the proposed nanopositioners. Various performance parameters relevant to the use of either of these mechanisms are investigated using experimental results. Displacement sensing bandwidth in some of the proposed nanopositioners is identified as a restricting factor to achieve higher scanning speeds. Hence, a novel high-bandwidth on-chip sensing mechanism for measuring stage displacement is introduced. The sensor is implemented in a single-degree-of-freedom MEMS nanopositioner as a test bench. Both analytical models and experimental data are provided for the sensor to investigate its static and dynamic features. To achieve a wider bandwidth, the on-chip configuration of the sensor is also modified, and the modified version is implemented in a novel two-degree-of-freedom MEMS nanopositioner. The performance results obtained from the proposed nanopositioners highlight their potential utility in a miniaturized high-speed AFM. The proposed nanopositioners demonstrate outstanding characteristics in terms of bandwidth, cross coupling rejection, and displacement range. All designs have the potential to be used in high accuracy positioning applications, particularly in on-chip AFMs.</p
Semantic-aware intelligent log analytics
No full textLarge-scale software-intensive systems often produce a large volume of logs to record runtime status and events for troubleshooting purposes. Logs play an important role in the maintenance and operation of software systems, which allow engineers to better understand the system's behaviours and diagnose problems. The rich information included in log data enables a variety of software reliability management tasks, such as anomaly detection, root cause analysis, and failure prediction. As the scale and complexity of software systems increase, traditional log analytics approaches becomes time-consuming and error-prone due to the rapid growth of log data volume and the complexity of log data semantics. In this thesis, we propose intelligent approaches for semantic-aware log analytics to effectively utilize log data in software reliability management. Firstly, we conduct an empirical study on log-based anomaly detection with deep learning. Log-based anomaly detection plays a vital role in software reliability management. Recently, many deep learning models have been proposed to automatically detect system anomalies based on log data and achieve high detection accuracy. To achieve a profound understanding of how far we are from solving the problem of log-based anomaly detection, we conduct an in-depth analysis of five state-of-the-art deep learning-based models for detecting system anomalies. We obtain five insightful findings and make these methods open-source for easy reuse and further study. Secondly, we propose to a novel deep learning-based approach that detect system anomalies from raw log messages. Existing anomaly detection approaches require to convert raw log messages into structured data, which might be error-prone due to the semantic misunderstanding problem from log data. To tackle this challenge, we propose NeuralLog to extract the semantic meaning of raw log messages and represents them as semantic vectors. These representation vectors are then used to detect anomalies through a Transformer-based classification model, which can capture the contextual information from log sequences. Experimental results on four real-world datasets confirm the effectiveness of our proposed method. Thirdly, we propose a semantic-aware log parsing method powered by prompt-based few-shot learning. Log parsing, which extract log templates associated with dynamic parameters, is considered as the first step of many log-based reliability management methods. Existing log parsing methods extract the common part as log templates using statistical features and often fail to identify the correct templates and parameters because they often overlook the semantic meaning of log messages and require domain-specific knowledge for different log datasets. To address the limitations of existing methods, we propose LogPPT to capture the semantic information of log messages to identify log events and parameters based on a few labelled log data. Experimental results on 16 real-world datasets show that LogPPT is effective and efficient for log parsing. Fourthly, we propose to pre-train a language model with semantic awareness using heterogeneous log data to unify many log analytics tasks into a single framework through. Existing approaches for intelligent log analytics are specifically designed for a certain type of tasks and cannot generalise to other tasks. Therefore, we propose PreLog, a pre-trained model with contrastive learning. PreLog is pre-trained on a large amount of log data with two log-specific objective and is generalised to downstream tasks. Extensive experimental results show that PreLog achieves better or comparable results in comparison with state-of-the-art, task-specific methods. Finally, we explore the application of ChatGPT, the current cutting-edge large language model (LLM), to perform log parsing without model training. Experimental results show that ChatGPT can achieve good results for log parsing with appropriate prompts, especially with few-shot prompting. Our findings indicate that applying LLMs to log analytics is a promising direction. We outline several challenges and opportunities for LLMs-based log analytics as well as discuss the potential future works. In summary, this thesis targets the design of semantic-aware approaches toward intelligent log analytics. Comprehensive experiments on public datasets demonstrate the effectiveness of our proposed methods
ERCC2 mutations alter the genomic distribution pattern of somatic mutations and are independently prognostic in bladder cancer
No full textExcision repair cross-complementation group 2 (ERCC2) encodes the DNA helicase xeroderma pigmentosum group D, which functions in transcription and nucleotide excision repair. Point mutations in ERCC2 are putative drivers in around 10% of bladder cancers (BLCAs) and a potential positive biomarker for cisplatin therapy response. Nevertheless, the prognostic significance directly attributed to ERCC2 mutations and its pathogenic role in genome instability remain poorly understood. We first demonstrated that mutant ERCC2 is an independent predictor of prognosis in BLCA. We then examined its impact on the somatic mutational landscape using a cohort of ERCC2 wild-type (n = 343) and mutant (n = 39) BLCA whole genomes. The genome-wide distribution of somatic mutations is significantly altered in ERCC2 mutants, including T[C>T]N enrichment, altered replication time correlations, and CTCF-cohesin binding site mutation hotspots. We leverage these alterations to develop a machine learning model for predicting pathogenic ERCC2 mutations, which may be useful to inform treatment of patients with BLCA
Examining consumer motives that affect consumption intentions for religious recommended foods
No full textReligious recommended foods (RRFs) hold a significant place in the diets of many individuals, serving not only as sustenance but also as a means of cultural and spiritual connection. This study focuses on the motivations driving the consumption of RRFs among Muslims living in Australia, recognising the increasing importance of understanding consumer behaviour in this market. RRFs are increasingly becoming a growing market worth 14 billion USD dollars globally, which presents an opportunity for economic growth, particularly in countries like Australia, where they are readily available. Despite the burgeoning market size, the underlying drivers of RRFs consumption remain poorly understood. Therefore, this study aims to fill this gap by investigating the factors influencing the consumption intentions of RRFs among Muslims living in Australia.
The research adopted a mixed-methods sequential design comprising qualitative interviews followed by a quantitative survey. The qualitative phase involves semi-structured interviews with 20 Muslim adults, which provides an in-depth understanding of the main factors guiding the consumption of RRFs. The qualitative study revealed considerations of food quality, health benefits and religious adherence toward RRFs consumption. Of particular interest, nostalgic emotions emerge as a significant influence, connecting consumers to past experiences and cultural traditions. Building upon these qualitative findings and existing literature, a theoretical framework is developed. The framework integrates an extended version of the Theory of Planned Behaviour (TPB) with value-expressive theory and the theory of consumption value to examine the validity and accuracy of this model in predicting consumer attitudes and intentions to purchase RRFs. Subsequently, a cross-sectional online survey of 506 valid responses from Muslims across Australia was analysed using partial least squares structural equation modelling (PLS-SEM). The quantitative results affirmed the validity of the extended TPB in predicting RRFs consumption behaviour, with food quality and health concerns emerging as significant determinants of attitudes and intentions towards RRFs. Additionally, religiosity and nostalgia show significant influence on consumers’ attitudes and intentions to purchase RRFs, highlighting the interplay of religious values and emotional connections in shaping RRFs consumption behaviour. Overall, these findings contribute to a deeper understanding of the personal, situational and social motivations driving the consumption of RRFs, shedding light on the fundamental role of country of origin, health concerns, religiosity and nostalgic feelings in influencing the attitudes and intentions to purchase RRFs. The results of this thesis provide actionable insights for marketing strategies, suggesting avenues for enhancing consumer attitudes towards RRFs through targeted messaging that emphasises country of origin, health benefits, religious values and nostalgic appeal. Further, the theoretical framework developed provides directions for future research in the realm of the consumption of RRFs, facilitating continued empirical advancements in this domain
Addictive eating in adults: an investigation into treatment, experience, and the feasibility of a personality-targeted intervention
No full textAddictive eating is an area of research that continues to increase despite the topic being somewhat divisive in the scientific community. The rise in obesity with overconsumption of processed foods, altered food environments, and increased incidence of mental health diagnoses, mainly anxiety, depression, post-traumatic stress disorder, and eating disorders, prompted the investigation of causal factors for food addiction or addictive-like eating behaviours. The aim of this thesis was to research current treatment options for addictive eating and at-risk population groups. This research would then guide the development of an intervention based on behaviour change theories and successful evidence-based models in other addictive behaviours to form a possible treatment option for addictive eating in adults. This thesis is highly novel as it applies principles of substance use and related addictive disorders treatment to the eating domain. Chapter 2 outlines the current literature around food addiction, diagnosis, mental health and eating disorders, personality traits, foods associated with food addiction, dietary assessment methods, and food addiction treatment. Chapter 3 reports on the findings of a scoping review undertaken of online support options for adults with self-reported food addiction. Key results from the 13 online treatment options for food addiction showed that very few support options included health professionals (such as dietitians and psychologists) (n=4), despite nine treatment options including the use of food plans. Twelve of the available treatments had the option of phone or online support delivery; however, none of the treatment options included evaluating the service they provided. The inclusion of scientific evidence or behaviour change theories was minimal. A scoping review (Chapter four) was undertaken to determine what is known about assessing and reporting dietary intakes in military and veteran populations, who are identified as being vulnerable to overeating. The majority of included studies used one dietary assessment method (n = 76, 85%), with fewer using multiple methods (n = 13, 15%). The most frequent methodology used was food frequency questionnaires (FFQ) (n = 40, 45%) followed by 24-hour recalls (n = 8, 9%) and food records (n = 8, 9%). The main dietary outcomes reported were macronutrients: carbohydrate, protein, fat, and alcohol, with total energy intake reported in n = 59 (66%). Fifty-four (61%) of studies reported a comparison with country-specific dietary guidelines, and 14 (16%) reported a comparison with the country-specific military guidelines. Studies compared to dietary guidelines highlighted the inadequate dietary intake of these populations, particularly overeating processed foods. A pilot study was developed using key information from Chapter 3 and an adaptation from a previous effective study for alcohol misuse. The development and adaptation of the program are outlined in Chapter 5. A feasibility study known as “FoodFix” using a randomised control trial design of a personality-targeted, motivational interviewing intervention to adults above the healthy weight range with symptoms of addictive eating behaviours was undertaken. The aim of the FoodFix intervention was to determine feasibility and acceptability in adults with addictive eating to improve health by reducing symptoms of addictive eating. FoodFix also featured a semi-structured interview component to collect qualitative data from participants. This was undertaken in session one of the FoodFix intervention. This data was used for a qualitative study, described in Chapter 6. Through data coding and thematic analysis, the two themes to emerge from the analysis were compulsion and control. The theme of compulsion had three sub-themes: Not being able to resist, justification then regret, and social forces. The theme of control also had three subthemes of controlling actions around food, weight, and in time. The findings of this study will help to inform further development of targeted interventions for food addiction and guidance for larger scale qualitative research in the experience of food addiction. In particular, qualitative research is imperative in future research due to the importance of co-design frameworks in the mental health setting. Key results from the secondary data analysis of the dietary outcomes of FoodFix are reported in Chapter 7. This chapter highlights that dietary behaviour is amenable to change in those with addictive eating. However, future treatment for addictive eating should ensure attention is given to improving participant diet quality alongside decreases in discretionary or processed food consumption to improve health outcomes and quality of life. This thesis identifies and discusses a need for easily accessible evidence-based interventions to support those with food addiction and highlights the importance of individualised treatment. It contributes new knowledge to whole dietary intake of adults with addictive eating engaged in a feasibility intervention and the possibility of a reduction in overeating highly processed foods via personality-targeted behaviour change methods. This thesis also contributes unique ideas around the strong desire for control of food intake experienced by those with addictive eating
The pursuit of the ecstatic truth: a qualitative practice-based enquiry of a feature-length film
No full textI am a filmmaker conducting a qualitative practice-based enquiry and reflecting on writing, producing, directing, filming and editing an 89-minute film, The World’s Best Film (2021). I demonstrate how qualitative audience research through focus groups and surveys can be used by film practitioners to better understand their craft and to investigate complex phenomena that may elude observation and analysis through traditional industry test screenings. In the making of my film, I was inspired by the call to action of storyteller Werner Herzog and his articulation of the existence of a ‘deeper strata of truth in cinema’ reachable only through a filmmaker’s use of invention, imagination, fabrication, and stylisation. Through making my film I journeyed to discover this provocative and profound illumination of meaning and pursued the enigmatic ‘ecstatic truth’ by ‘agitating reality’. I was confronted with the complexity of understanding the effects of this pursuit on audiences and how it could be observed through informal test screenings. Through autoethnography, informed by my 20 years of filmmaking experience, I reflect on the creation of key cinematic moments from my film where I used what I term ‘ecstatic craft’ to attempt to elicit profound responses for audiences. I recount the experience of producing 13 different experiments in ecstatic craft and explore how each segment in a character-driven anthology film differed in its application of this craft to reach higher plains of understanding in the audience’s engagement. I articulate examples of the audience’s experience of ecstatic truth through individual layers, ‘ecstatic flashes’ and how these contribute to their cumulative understanding of a film’s core meaning. I triangulate my experience pursuing and articulating this elusive and complex ‘strata’ of truth through audience surveys and focus groups to assess how audiences engaged with individual moments, laced with ecstatic craft, to determine how an audience engages with my pursuit of the ecstatic truth. I offer new insights into how filmmakers can pursue their own ecstatic craft and contribute to their own ‘grammar of images’. I also reflect on the efficacy of my methods, their ethics, and upon areas for further research this thesis has opened
Analytic and numerical solution of free boundary fluid flow through porous media
No full textFree boundary problems (FBP) arise in many applications, including in the flow of porous media. They involve the solution of a partial differential equation (PDE) that has an unknown boundary. The unknown boundary is called a free boundary, and it should be determined as part of the solution. In general, the boundary conditions that are applied to the free boundary are overspecified. The Boundary Element Method (BEM) is a numerical technique used to solve PDEs that appear in mathematics, physics, and engineering. This method works by dividing the boundaries of the problem into smaller parts, which reduces the dimension of the problem and allows for the application of Green's theorem. BEM is particularly well-suited for solving Laplace's equation. BEM transforms the issue from a PDE with boundary conditions to an integral equation, resulting in a linear system that can be efficiently calculated. We can solve for the free boundary by constructing an iterative approach. This technique is thoroughly described, and some problem calculations are included. This thesis presents numerical results obtained by using the BEM to simulate fluid flow through homogeneous isotropic porous media. The governing equation for this flow is Laplace's equation, which is modeled by Darcy's law. The boundary conditions for this system include both Dirichlet and Neumann conditions, and the free surface is subject to both types of boundary conditions. The conformal mapping technique is used to make the numerical resolution of fluid flow issues easier through porous media. By transforming the intricate fluid domain into a simpler one, exact solutions can be attained for the simplified domain, which can then be used to modify the boundary element method and enhance the precision of the numerical outcomes. The two methods of conformal mapping, iterative and direct, are used to locate the free surface in fluid flow problems. These techniques involve transforming the solution from a complex shape to a simpler one, where the solution to Laplace's equation is solved using the boundary element method. The solution is then mapped back to the original geometry, allowing the free surface to be identified. Also, we have used the separation of variables method, which is a useful tool for solving mathematical models of fluid flow through porous media, which are described by PDEs. This approach simplifies complex equations, making it easier to determine solutions and gain a better understanding of the behavior of the fluid
Perceptions and knowledge of using a low-salt diet for preventing hypertension among Chinese populations in Australia: A mixed-methods study
No full textHigh salt consumption is a significant risk factor for hypertension. However, many people in the world consume higher than the World Health Organization’s recommended amount of five grams a day (5g/day). Chinese is one of the five main ancestries in Australia. Several studies have reported salt consumption among the general populations in Australia and China of two to three times the recommended amount. However, no known empirical research has focused on exploring the habitual salt-related health behaviours of Chinese Australians. To explore the salt consumption habits of Chinese Australians and the factors that influence their perceptions about maintaining a low-salt diet to prevent hypertension. An explanatory sequential mixed-methods design was selected based on paradigmatic underpinnings. The Health Belief Model (HBM) was used to guide the research process. The study consisted of an adaptation of the Determinants of Salt-Restriction Behaviour Questionnaire (DSRBQ) study, a cross-sectional study (n = 188) and eight semi-structured interviews. The DSRBQ was successfully adapted to the Chinese-Australian context. Habitual salt-related practices, including personal taste preferences in food, posed the most significant barrier to making changes to salt-related behaviours. This was followed by perceived health benefits and threats, hidden salt in food products, food literacy skills, and social and peer influence. Results also showed that inadequate knowledge of the health complications arising from excessive salt consumption led to low adherence to the interventions and, thereby, an under-appreciation of the seriousness of hypertension. Practical cooking methods, low-salt food choices and positive influences from family and peers were perceived to be effective strategies by Chinese Australians. The thesis concludes that the internal and external prompts that could trigger courses of action were inadequate. An implication of these findings is that there might be missing (or weak) links between some components in the HBM which might reduce its capability for predicting the individual salt-related behaviours associated with health outcomes. The insights gained from this study may be of assistance in improving the predictability of HBM and in supporting further development of the adapted DSRBQ as a rapid assessment tool for this population group
Spectroscopy-based chemometric approaches for the determination of antibiotics in soils
No full textDetecting and quantifying organic contaminants, including pesticides, polycyclic aromatic hydrocarbons (PAHs), and pharmaceuticals usually present in the environment, are challenging tasks, primarily because of the complex analytical difficulties associated with these compounds. Traditional methods are labour-intensive and generally use toxic solvents. Non-destructive spectroscopy techniques, besides promoting sustainable analytical practices, offer opportunities to reduce toxic chemicals' environmental footprint. This thesis addresses developing the quantification procedure using chemometrics models and also investigates the non-destructive spectroscopy method, FTIR (Fourier transform infrared), which is more eco-friendly, combined with chemometrics. It aims to provide faster and greener methods for detecting antibiotics in soil matrices. The core focus of this research is a comprehensive Partial Least Squares (PLS) analysis of three quinolone antibiotics, Gatifloxacin, Ofloxacin, and Lomefloxacin (G, O, and L), within a diverse spectrum of soil matrices, ranging from the simple (KBr) to complex combinations (sand, clay, sand/clay, and clay/humic acid). Systemic FTIR-DRIFTS data collection has been done from individual quinolones and their mixtures across each matrix. The specific objectives involve gathering FTIR data for individual and mixed antibiotics in various soil matrices, creating and validating chemometric models tailored to each soil matrix, analysing experimental results, assessing model performance with different soil matrix calibrations, and understanding its behaviour with combined and comprehensive soil matrix calibrations. The first study comprises five separate experiments conducted in five different soil matrices (KBr, Sand, Clay, Sand & Clay and Clay & humic acid). For each experiment, a total of 54 spectral data sets were collected. These data sets were obtained from varying concentrations (ranging from 0.1% to 10%) of individual antibiotics (G, O, and L), as well as mixtures of these antibiotics using FTIR spectroscopy. These data sets have been subjected to PLS analysis. The PLS model shows promising performance in detecting G, O, and L in various soil matrices, with high R-squared (>0.95) indicating strong fits. The second study included applying sets of spectra from each sample soil matrix to all soil matrix calibrations from previous experiments. Therefore, 75 PLS analyses have been done for G, O, and L in five soil matrix samples (KBr, Sand, Clay, Sand & Clay, and Clay & humic acid). This comprehensive approach made it possible to thoroughly examine how different calibrations influenced the results. The study revealed a clear trend: when the sample and calibration matrices matched like both KBr (KBr/KBr) and both sand (S100/S100), the prediction models functioned exceptionally well with high R-squared and low RMSE values. In contrast, when the sample and calibration matrices differed (e.g., KBr/S100), the models struggled, resulting in lower R-squared values. The final study aimed to enhance analyte detection accuracy within diverse soil matrices. The first part introduced innovative calibration methods involving a combination of spectral data from different soil matrix sample sets. In the second part, a comprehensive calibration model was created by merging data from all five soil matrices. This comprehensive approach enabled the analysis of samples across different soil types and compositions, enhancing the model's versatility. The study's results emphasised that PLS models performed best when the calibration matrices closely matched the analysed soil matrices. In many cases, the comprehensive calibration model achieved impressive R-squared and RMSE values, indicating its effectiveness in detecting quinolone compounds
Evaluating the effectiveness of the MASTER coaching program as a coach development tool for improving coaching practices of football coaches and improving a range of player outcomes
No full textThe overarching aim of this thesis is to design and evaluate a positive games-based coach education intervention for use in community level sport. To do so, this thesis by publication presents a series of studies related to the MASTER Coach Education Program in Football, which was developed to address gaps in the sports coaching literature and for the improvement of community level sports coaching. To gather evidence on best practice in sports coaching, in phase one, a systematic review of the literature in organised sport was undertaken to examine the impact of positive and negative sports coaching practices, and game-based pedagogy in schools. The findings from Phase 1 were used to inform Phase 2, which involved the design, development, implementation and evaluation of the MASTER Coach Education Program in football. Diagrammatically the thesis journey is displayed at Annexure L. Phase 1 The scoping review showed coach behaviours and practices do influence the athlete’s perception of the coach, motivation and performance, and perception of the sport. The systematic review provided evidence for the feasibility of a games-based intervention to deliver a range of outcomes to adolescent athletes. As a result, the authors proposed a definition of the previously undefined term “positive sports coaching”. Phase 2 The rationale and study protocol are included to determine intervention fidelity.
Secondly, the thesis’s primary aim investigated whether coach’s who receive the MASTER Coach Education program reported improvements in several coach related categories. Significant effects were observed for the time devoted to playing-form activities [22.63% (95% CI (9.07-36.19), P=0.002, d=1.78), P=0.002, d=1.78]. Further, intervention benefits also observed for the several secondary outcomes for athletes including game skills and several wellbeing measures. Finally, a published book chapter provides valuable contextual information for the effectiveness of the MASTER Coach Education RCT in the Australian football landscape