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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 Study on hydrogen storage performance of as-milled Ti-V-Cr-Fe-Mn high entropy alloys(Thesis, The University of Waikato, 2025-07-16)This study aims to fabricate and optimize BCC-based high-entropy hydrogen storage alloys through mechanical alloying. The research systematically investigates the effects of processing parameters (including milling time, ball-to-powder ratio, rotation speed, and process control agent (PCA) addition), alloy composition, and heat treatment on the phase structure, thermodynamic stability, and hydrogen storage performance of the fabricated alloys. Furthermore, the study compares the microstructure and hydrogen storage properties of alloys fabricated by different methods (mechanical alloying and arc melting). Firstly, the study optimizes the mechanical alloying parameters, revealing the critical roles of milling time, ball-to-powder ratio, and rotation speed in forming BCC structures and nanocrystalline grains in Ti-V-Cr-Mn-Fe alloys. The regulatory effects of PCA addition on powder yield and particle size are also analyzed. Subsequently, the impact of composition on hydrogen storage properties, including hydrogen absorption/desorption kinetics, thermodynamic behavior, and cycling stability, are explored by varying the Ti content and Mn/Cr ratios. It is found that increasing Ti content enhances the proportion of C14 Laves phases, while increasing Mn content effectively suppresses Laves phase formation, thereby increasing the BCC phase fraction and improving hydrogen storage kinetics. Additionally, the role of heat treatment is examined. Microstructural evolution analysis reveals the phase transformation behavior among BCC, FCC, and Laves phases under different heat treatment conditions and their effects on hydrogen storage capacity. Specifically, as the temperature increases, the BCC structure first decomposes into a BCC + FCC dual-phase structure, followed by the precipitation of the Laves-2 phase within the FCC phase. After high-temperature treatment, the lattice constant of the BCC phase decreases, and the synergistic effect of the Laves and FCC phases results in a slight reduction in the hydrogen absorption and desorption capacity of the alloy. Finally, by comparing different fabricating process, the differences in microstructure and hydrogen storage performance of Ti25V35(CrMnFe)40 alloys prepared by these methods are investigated. The results suggested that Mechanical alloying significantly enhances initial the activation performance and hydrogen absorption kinetics of the as-milled alloys are improved compared to the counterparts of as-cast alloys.Item Māori-focused course content in undergraduate psychology programmes in Aotearoa New Zealand(Journal Article, Christchurch New Zealand Psychological Society, 2025)Item 15 yr of interstellar neutral hydrogen observed with the interstellar boundary explorer(Journal Article, IOP, 2025-05-01)The interactions of our heliosphere with the surrounding local interstellar medium (LISM) lead to a range of observable phenomena such as energetic neutral atoms (ENAs) from the boundary regions of the heliosphere and the influx of interstellar neutrals (ISNs) into the inner solar system. Hydrogen is the dominant neutral species in the LISM, but due to ionization and radiation pressure, only a fraction of the ISN H atoms reach the inner solar system close to Earth. Monitoring this signal therefore provides observational constraints on our assumptions of the LISM and the solar-activity-dependent loss processes inside the heliosphere. The IBEX-Lo instrument on board the Interstellar Boundary Explorer has been the only instrument so far to measure ISN H atoms directly, together with ISN D, He, Ne, O, and ENAs in the energy range from tens of eV to 2 keV. This study covers 15 yr of IBEX-Lo ISN H observations, i.e., more than one solar cycle and includes two solar minima when the ISN H signal in IBEX-Lo is strongest. Despite the very intense ISN He signal, the ISN H signal can be retrieved with appropriate knowledge of the instrument, choice of optimum observation season, and supporting modeling. The retrieved ISN H signal shows a clear anticorrelation with solar activity. The resulting ISN H maps are available in orbit format and in ecliptic coordinates and will be the basis for future more detailed comparison with heliosphere models.Item Social media and the evolution of vaccine preferences during the COVID-19 pandemic: Discrete choice experiment(Journal Article, JMIR Publications Inc., 2025)Vaccine information and misinformation are spread through social media in ways that may vary by platform. Understanding the role social media plays in shaping vaccine preferences is crucial for policymakers and researchers. Objective This study aims to test whether social media use is associated with changes in vaccine preferences during the COVID-19 pandemic in New Zealand, and whether trust in sources of information has a moderating role. Methods Our data consist of a balanced panel of 257 web-based respondents in New Zealand in August 2020, October-November 2020, and March-April 2021. We use a novel approach with stated choice panel data to study transitions between different vaccine preference groups. We analyze the associations between these transitions and social media use. We classify respondents as resistant (never chose a vaccine), hesitant (chose a vaccine between 1 and 5 times), and provaccine (chose a vaccine 6 out of 6 times) in each wave of data. Results We found a positive or neutral association between social media use and vaccine uptake. Facebook, Twitter (pre-2022), and TikTok users who are provaccine are less likely to become hesitant or resistant. Facebook and Instagram users who are hesitant are more likely to become pro. Some social media platforms may have a more positive association with vaccine uptake preferences for those who do not trust the government. Conclusions The paper contributes to the wider literature, which shows social media can be associated with reinforcing both pro and antivaccination sentiment, and these results depend on where individuals get their information from and their trust in such sources.Publication Validating federated learning performance in practice: An agricultural edge hardware testbed analysis(Thesis, The University of Waikato, 2025)Smart agriculture, driven by the Internet of Things (IoT) and artificial intelligence, generates vast amounts of data from distributed sensors and devices on farms. Traditional centralised machine learning approaches struggle in rural settings due to intermittent connectivity, limited bandwidth, and data privacy concerns. Federated learning (FL) has emerged as a promising approach to address these challenges by collaboratively training models directly on edge devices, for example farm sensors and edge computers, without sending raw data off-site. This thesis presents the design, development, and evaluation of a standards-driven, hardware-based federated learning testbed for smart agriculture. The testbed consists of six NVIDIA Jetson Nano edge computing nodes. A primary objective was to evaluate and compare modern, open-source FL frameworks in a realistic edge environment. Therefore, we specifically tested the FLIGHT framework. FLIGHT was selected for its notable features, including a Function-as-a-Service (FaaS) architecture facilitating serverless deployment and native support for hierarchical topologies relevant to distributed farm networks. We developed a reproducible methodology for deploying and benchmarking FLIGHT on these resource-constrained devices, using the Fashion-MNIST image classification dataset as a proxy for agricultural sensor data to compare performance against known benchmarks and simulation expectations. Key contributions include: 1) the physical testbed itself; 2) an open and repeatable experimental framework centered around FLIGHT; and 3) a comprehensive performance analysis under realistic network conditions and device constraints, providing data to bridge the gap between simulation results and practical deployment challenges. The results demonstrate that the federated model achieves competitive accuracy while significantly reducing raw data transfer. Findings indicate that careful configuration of FL can mitigate the impact of limited connectivity, and that even low-power devices can collaboratively train useful models within reasonable time-frames. These insights validate the viability of FL in smart farming scenarios. The developed testbed and its accompanying benchmarking methodology lay a foundation for future research and deployment of FL in agriculture, bridging the gap between theoretical simulations and real-world farm deployments. This work’s significance lies in providing both a practical tool for researchers to rigorously evaluate FL strategies in edge environments and guidance for stakeholders aiming to deploy privacy-preserving AI in agriculture at scale.
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