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Research Commons is the University of Waikato's open access research repository, housing research publications and theses produced by the University's staff and students.
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Publication Paper trains(Thesis, The University of Waikato, 2024)In the end, it keeps coming back to that. How we’ve got to move past each other. I tell your story in a way that somehow still makes it all about me. I count the trains, burn Bible paper. I’m left inconsolable on the dance floor. I try to paint a bowl of fruit and it always ends up being a self-portrait. I suppose that’s life. You reach your hands out for others, but they’re still your hands. A train is late to the station. Dogs howl in the night. A kitchen tap drips without sound. Lungs fill with lake water. Books remain unread. Words, unspoken. Paper Trains is a narrative-driven collection of poems exploring how people move through grief and the places it can take them. After losing her best friend, the narrator of these poems latches on to everything she can to keep herself afloat. Bad omens, old jackets, blue paper cranes. We follow her journey as she navigates both devastating loss and the unrest of her early 20s. There are ways to stay busy: house parties, coffee shops, home renovation, self-pity, gardening. She moves through spaces to delay moving on. We witness the effects that death has on her relationships with others and the burdens that she must now carry. This is a tale of violence, anger, and isolation. Of forgetting and remembering again. Betrayal. Begrudging hope. Superstitions and bad life choices. Trains always running late.Publication Computational Nanobiosensing – Drawing Analogies Between Optimisation and Nanobiosensing for Smart Tumour Targeting(Thesis, The University of Waikato, 2024)Nanotechnology has been rapidly developing for early diagnosis and treatment of cancer, with nanoparticles being a large focus. However, traditional drug delivery mechanisms are passive and inefficient, with only 0.7% of nanoparticles reaching the tumour through blood vasculature. In vivo computation, also known as computational nanobiosensing (CONA), replaces nanoparticles with swarms of externally manipulable nanorobots whose movement is controlled by an external actuating system. The biological problem of smart tumour targeting is viewed from the computational perspective as an optimisation problem: nanorobot swarms (computational agents) explore the blood vasculature of high-risk tissue (search space) to locate the tumour (global optimum). Tumour biological gradient fields (BGF) create a fitness landscape, which can be analysed with fitness landscape analysis (FLA) to select and tune appropriate search algorithms for in vivo computation. Key limitations of previous work for in vivo computation are a lack of realistic BGFs that reflect the tumour microenvironment to test search algorithms on; and for FLA, no available measures that consider physical constraints of the in vivo environment. Two realistic tumour BGF models were created using COMSOL Multiphysics software (CFD Module), one highly vascularised, the other less vascularised. The vascular architecture was based on in vivo blood vessel networks in healthy and tumour regions, and blood velocity was used as a BGF. Blood velocity was found to be lowest in the tumour region, not exceeding 100 µm/s, confirming its applicability as a BGF for search algorithm testing. Three new FLA measures were created and validated with numerical simulations on two possible tumour vascular landscapes. These measures addressed the physical constraints of discrete search space, unidirectional blood flow, and nanorobot steering imperfections when using a uniform magnetic field. The less vascularized landscape was found to be more discrete, more heterogeneous, and contain a smaller countercurrent frequency of search direction. This indicated it would be more challenging to solve for than the highly vascularised landscape. These advancements of the CONA framework allow the in vivo search environment to be better visualised and understood for algorithmic development, as well as provide realistic BGFs to test these search algorithms on.Item Mini-batching with fused training and testing for data streams processing on the Edge(Conference Contribution, ACM, 2024)Edge Computing (EC) has emerged as a solution to reduce energy demand and greenhouse gas emissions from digital technologies. EC supports low latency, mobility, and location awareness for delay-sensitive applications by bridging the gap between cloud computing services and end-users. Machine learning (ML) methods have been applied in EC for data classification and information processing. Ensemble learners have often proven to yield high predictive performance on data stream classification problems. Mini-batching is a technique proposed for improving cache reuse in multi-core architectures of bagging ensembles for the classification of online data streams, which benefits application speedup and reduces energy consumption. However, the original mini-batching presents limited benefits in terms of cache reuse and it hinders the accuracy of the ensembles (i.e., their capacity to detect behavior changes in data streams). In this paper, we improve mini-batching by fusing continuous training and test loops for the classification of data streams. We evaluated the new strategy by comparing its performance and energy efficiency with the original mini-batching for data stream classification using six ensemble algorithms and four benchmark datasets. We also compare mini-batching strategies with two hardware-based strategies supported by commodity multi-core processors commonly used in EC. Results show that mini-batching strategies can significantly reduce energy consumption in 95% of the experiments. Mini-batching improved energy efficiency by 96% on average and 169% in the best case. Likewise, our new mini-batching strategy improved energy efficiency by 136% on average and 456% in the best case. These strategies also support better control of the balance between performance, energy efficiency, and accuracy.Item Time-evolving data science and artificial intelligence for Advanced Open Environmental Science (TAIAO) programme(Conference Contribution, International Joint Conferences on Artificial Intelligence (IJCAI), 2024)New Zealand's unique ecosystems face increasing threats from climate change, impacting biodiversity and posing challenges to safety, livelihoods, and well-being. To tackle these complex issues, advanced data science and artificial intelligence techniques can provide unique solutions. Currently, in its fourth year of a seven-year program, TAIAO focuses on methods for analyzing environmental datasets. Recognizing this urgency, the open-source TAIAO platform was developed. This platform enables new artificial intelligence research for environmental data and offers an open-access repository to enhance reproducibility in the field. This paper will showcase four environmental case studies, artificial intelligence research, platform implementation details, and future development plans.Item The development of pre-service teachers’ competence to teach mental calculation strategies(Journal Article, AOSIS, 2024)Background: There is a concern in South Africa that pre-service teachers do not have the required knowledge to teach mathematics in primary school and to develop learners’ number sense. In this study, pre-service teachers taught the mental strategy of bridging through ten through a structured teaching sequence from the Mental Starters Assessment Project (MSAP) materials as a work-integrated learning opportunity. Aim: We ask the question: How do the MSAP materials support pre-service teachers in competently teaching mental mathematics? Setting: Thirty-eight Bachelor of Education (Foundation Phase) third-year preservice teachers from an Eastern Cape university participated in this study. Methods: Participants taught the strategy during their Teaching Practice, quantitatively analysed the results of their classes and reflected on the experience in a questionnaire and focus group interviews. Results: Results indicate that the teachers were relatively successful in their teaching of the strategy; however, all indicated that they taught the sequence for a more extended period than recommended. Qualitative responses provide evidence of the teachers’ development in their knowledge of learners and their characteristics, general pedagogical knowledge, pedagogical content knowledge and knowledge of educational contexts. Conclusion: This study offers evidence of the professional learning of pre-service teachers that resulted from taking an integrated approach to facilitating a mathematics teaching methodology course through requiring a work-integrated learning component. Contribution: We argue that such an approach is necessary for pre-service teachers to be adequately prepared for the challenges of teaching mathematics in the South African classroom.
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