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OPUS: Open Ulethbridge Scholarship

Open ULeth Scholarship (OPUS) is the University of Lethbridge's open access research repository. It contains a collection of materials related to research and teaching produced by the academic community.

Self-archiving your research in OPUS is one way to meet Open Access policies of granting agencies. It is important to retain your final, post-peer-reviewed drafts for submission to OPUS, as this is often the only version publishers will allow to be archived. Click here for information on the U of L Open Access Policy.

Check here for more information about OPUS.

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    Beekseekseenaiks kii Naamskiiks: knowledge transfer in Blackfoot community based collaborative research
    (Lethbridge, Alta. : University of Lethbridge, Dept. of Anthropology, 2026) Hungry Wolf Cardinal, Star; University of Lethbridge. Faculty of Arts and Science; Asselin, Jodie; Lee-Yaw, Julie A.
    Academic discourse with Blackfoot Niitsitapiiks (Real People) is a way to create relationships based on knowledge transfer. The emergent life force, or collective method, becomes a living body of knowledge or a “treaty” (Little Bear in talks, Atwood et al 2023). This thesis is a contribution to the multidisciplinary intercultural dialectical discourse that is collective methodology. Combining data and oral history from living Blackfoot people this project exhibits community led research and how contributors can guide a change of focus. Oral history among the Blackfoot tied to sacred ancestral knowledge about the flux (Leroy Little Bear in all works and talks, as well as Peats 1996 Blackfoot Physics, which Leroy also helped with), transfers (often involving intricate material culture), identity, and the land are all changing and changeable. In “Genesis of the Cosmos” LaViolette explained this further, “The ancient creation science conceives all physical form, animate or inanimate, to be sustained by an undercurrent of process, a flux of vital energy that is present in all vital space.” (1995, p.7) It is my privilege to translate meanings, particularly as a Niitsitapi who has been taught that everything conceivable is in motion and changing, even meanings. Questions about Beekseekseenaiks ki Naamskiiks (Snakes, Lizards, Salamanders) became discussions about Niitsitapiisin or Blackfoot protocol, and ethics around knowledge acquisition, translation, production, dissemination and transfer. This work adds another strand of synthesis to the relationship between Blackfoot and non-Blackfoot people, traditional knowledge and English academic writing, and between Native and Western science.
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    Constrained systems and Dirac bracket quantization
    (Lethbridge, Alta. : University of Lethbridge, Dept. of Physics and Astronomy, 2026) Maher, John; University of Lethbridge. Faculty of Arts and Science; Dasgupta, Arundhati
    The aim of this thesis is to further analyze the usage and utility of the Dirac Bracket quantization in the development of new models of quantum gravity. The primary model of study was introduced by Ita III et al. (August 2021, arXiv:2108.13210), in which a particle is constrained to circular motion in 2-dimensions. This is achieved by comparing the results obtained through i) canonical quantization with first-class constraints; and ii) quantization of second-class constraints via promotion of the Dirac bracket to the commutator. Under the constraints described by Ita III et al., the auxiliary condition of the second-class system leads to the restriction that the radial momentum be proportional to the inverse of the radius. Through the first-class constraint quantization scheme, we find the wavefunction’s solution to be a Bessel function in the radial coordinates. After evaluating the expectation values, the results are plotted, revealing that the momentum-inverse-radius proportionality holds in the classical limit but is not valid for small radii. This result is contrasted with the quantization obtained from the second-class constraints and the Dirac bracket. We conclude that the method of second-class constraints and the Dirac bracket yield the physically correct quantization, in which the operator representations are automatically imposed at the quantum level.
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    Detection of lameness in feedlot cattle using deep learning enhanced mobile application
    (Lethbridge, Alta. : University of Lethbridge, Dept. of Neuroscience, 2026) Kaur, Sandeep; University of Lethbridge. Faculty of Arts and Science; Sutherland, Robert J.
    This thesis presents a deep learning framework for automated lameness detection in beef cattle using video-based gait analysis. Lameness is a major issue in livestock production, affecting animal welfare and productivity. Traditional visual assessment methods are subjective and difficult to scale in commercial feedlot environments, motivating the need for automated detection. A hybrid CNN-LSTM model is proposed to capture spatial and temporal gait features. The CNN (InceptionV3) extracts frame-level features such as posture, limb positioning, and back curvature, while the LSTM models temporal dependencies across video sequences to learn gait dynamics. The model was trained and evaluated on expert-annotated video data collected under controlled conditions. To improve interpretability, explainable AI techniques including Grad-CAM, saliency maps, and temporal heatmaps were used to identify regions influencing predictions. Logistic regression was applied for feature-level interpretation of gait abnormalities such as limping, head bobbing, and arched back. The system achieves 92.45% accuracy with an inference time of 389ms. A cross-platform ASP.NET MAUI mobile application enables video capture and server-based inference for deployment. This work demonstrates a scalable and interpretable approach for automated lameness detection using deep learning and mobile technology.
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    How do microglia, β-amyloid, and phosphorylated-tau interact in mouse models of Alzheimer's disease?
    (Lethbridge, Alta. : University of Lethbridge, Dept. of Neuroscience, 2026) Lapointe, Valérie E.; University of Lethbridge. Faculty of Arts and Science; Sutherland, Robert J.
    Alzheimer’s disease (AD) is a progressive neurodegenerative disease and the most common form of dementia. It is characterized by gradual memory loss and cognitive decline that significantly impair daily life. With population aging, the prevalence and societal burden of AD are steadily increasing. Neuropathologically, AD is defined by the accumulation of two hallmark proteins in the brain: amyloid-β (Aβ), which forms extracellular plaques, and pathological tau, which accumulates intracellularly as neurofibrillary tangles, together with neurodegeneration. Although Aβ accumulates early in AD, the extent of insoluble amyloid plaque deposition correlates only weakly with cognitive impairment. In contrast, tau pathology follows a stereotypical spatial and temporal progression across brain regions, and the extent of tau pathology and neurodegeneration is more closely associated with cognitive decline. Neuroinflammation is increasingly recognized as a key contributor to AD pathogenesis. Microglia, the resident immune cells of the brain, mediate these inflammatory responses and influence both the progression and regional spread of tau and amyloid pathology. At the same time, astrocytes further modulate these processes through interactions with neurons and microglia. These glial-driven mechanisms are closely linked to neurodegeneration and behavioural outcomes. Despite substantial advances, critical gaps remain in understanding how amyloid, tau, and neuroinflammation interact to drive disease progression, in part due to limitations of traditional transgenic models that rely on protein overexpression. This thesis addresses these gaps by investigating tau-driven pathology, pharmacological modulation of microglial activation, and the combined effects of amyloid and human tau in physiologically relevant mouse models of AD. Behavioural assessments and immunohistochemical analysis were employed to evaluate neuroinflammation, protein pathology, and associated functional outcomes.
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    AI-assisted evaluations and EDI: how temperature settings influence reliability, bias, and inclusion
    (Canadian Evaluation Society, 2026) Shapiro, Sidney; Khalid, A. K. M. Iftekhar
    This practice note examines the role of generative artificial intelligence (GenAI) temperature settings in program evaluation, particularly their impact on response consistency, variability, and implicit bias. Temperature settings influence the consistency and variability of generated outputs, affecting how evaluators interpret and use AI-assisted content. Lower temperature settings produce more deterministic responses, which can enhance reliability but may inadvertently reinforce dominant narratives, potentially limiting diverse perspectives. Conversely, higher temperature settings introduce variability, fostering creativity and increasing the risk of bias and inconsistency. Given the growing integration of AI-assisted methods in evaluation, this paper explores the implications of temperature settings for equitable and inclusive analysis. By examining temperature settings through an evaluation lens, this paper provides insights into mitigating bias, improving methodological rigor, and ensuring alignment with Equity, Diversity, and Inclusion (EDI) principles. The discussion offers practical guidance for evaluators on calibrating AI parameters to enhance transparency, fairness, and reliability in data-driven decision-making. This practice note aligns these technical considerations with established evaluation principles, including rigor, credibility, and culturally responsive and equitable practice.