From Plagiarism to Postplagiarism: Navigating the GenAI Revolution in Higher Education

Higher education is undergoing a seismic shift with the advent of Generative AI (GenAI) technologies. In this session we explore the transformative impact of GenAI on teaching, learning, and assessment practices in a rapidly evolving academic environment. Join us as we explore challenges and opportunities presented by GenAI, examining how it reshapes our understanding of academic integrity, student agency, and authentic assessment. Dr. Sarah Elaine Eaton will discuss innovative strategies for integrating GenAI into educational practices while maintaining the core values of academic integrity, critical thinking, and original scholarship. This webinar is essential for educators, administrators, and policymakers who are grappling with the implications of AI in higher education and seeking proactive approaches to harness its potential. Learning Outcomes: By the end of this webinar, participants will be able to: 1. Understand the concept of post-plagiarism as an impact of GenAI on traditional concepts of plagiarism and academic misconduct in higher education. 2. Identify strategies to foster student agency and critical thinking skills in an AI-augmented learning environment. 3. Formulate approaches to uphold and promote academic integrity in the context of widespread GenAI use in higher education. Recommended citation: Eaton, S. E. (2025, January 29). From Plagiarism to Postplagiarism: Navigating the GenAI Revolution in Higher Education Centre for Artificial Intelligence Ethics, Literacy, and Integrity (CAIELI): Generative AI Workshops, University of Calgary, Calgary, Canada. https://hdl.handle.net/1880/12064

Modelling and Validation of a Cable-Assisted Robotic System for Machining Application

In the rapidly evolving landscape of manufacturing processes, robotic systems have gained prominence for their precision and efficiency. A cable-assisted robotic system (CARS) tailored for machining operations, offering enhanced stiffness is presented in this thesis. This thesis aims to understand CARS dynamic behaviour by experimentally examining the system under various conditions. These conditions include equilibrium and imbalance states, varying the robot postures, and varying the pulley locations. Understanding the influence of these variables aids in determining the feasibility of such a system. Additionally, the influence of the cables on the systems is examined. Cable types are varied from solid to stranded, as well as the number of cables and the size of the cables. The cable tension is also manipulated and the effects are investigated. The cables act as massless redundant links providing the robot with additional stiffness, however the extent to which additional stiffness is applied needs to be determined. CARS is also examined in a quasi-static state. The quasi-static state differs from the static, impacting the stability limits at the low frequencies and giving a better understanding of the dynamics of the system for machining applications. Finally, CARS is tested in machining by performing chatter tests under the use of different cables. Furthermore, this thesis presents a novel mathematical model for CARS, consisting of a semi-empirical cable model integrated with a serial robot model. The cable properties are identified experimentally, while the robot properties are determined using optimization techniques. The model is derived using the Euler-Lagrange method, and is validated by simulating CARS response and comparing it to the results obtained through experimentation. Finally, the model is optimized using a genetic algorithm to determine the cable configuration that maximizes the dynamic stiffness. These findings have implications for robotic machining applications and broader industrial robotics

Rice Panicle Segmentation using Multi-Stage Pseudo-labeling and Active Learning

Global food demand is projected to rise significantly by mid-century, necessitating more efficient agricultural practices to enhance crop monitoring and yield estimation. As a staple food for over half of the world’s population, rice plays a pivotal role in global food security. Accurate and scalable monitoring of rice growth stages—particularly panicle detection and segmentation—is critical for estimating yield potential and iden-tifying stress responses. Traditional manual phenotyping methods are labor-intensive and time-consuming, making automated approaches increasingly essential. Rice panicle segmentation poses unique segmentation challenges due to occlusion, morphological variability, and complex field conditions. Progress is hindered by limited annotated datasets, inconsistent environmental conditions, and the high cost of manual labeling. This research aims to alleviate these challenges by proposing accurate and label-efficient deep learning frameworks tailored for rice panicle segmentation under real-world agricultural settings. A novel dataset was developed using drone-based video acquisition in rice fields located in Fatehgarh Sahib, Punjab, India. Two commonly cultivated rice varieties, PB-126 and PB-128, were captured at their final growth stage using 4K-resolution imaging. Video frames were processed into 512×512-pixel patches and manually annotated to create precise ground truth segmentation masks. To improve model generalization and bridge the gap between controlled and field environments, a complementary synthetic dataset was also generated. This dual dataset enables future research in rice phenotyping and related agricultural applications. Two complementary deep learning approaches were developed. The first is a multi-stage pseudo-labeling pipeline based on a customized U-Net architecture with an EfficientNet-B3 encoder. This pipeline pro-gressively improves segmentation performance through synthetic pretraining, fine-tuning on real data, and iterative incorporation of high-confidence pseudo-labels. The second approach is an active learning frame-work that significantly reduces manual annotation by selecting the most uncertain samples—identified via entropy-based uncertainty estimation—for annotation and retraining. To ensure stable and robust training under limited labeled data, the active learning strategy integrates a hybrid loss function, momentum-based optimization, and a cosine annealing learning rate scheduler with warm restarts. Both frameworks were rigorously evaluated on internal and external test sets to assess performance across varied field conditions. The pseudo-labeling pipeline achieved Dice scores of 0.7531 on internal data and 0.6945 on external data, surpassing the benchmark nnU-Net model. The active learning framework also delivered strong performance, with Dice scores improving from 0.6546 to 0.7071 (internal) and 0.6241 to 0.6628 (external) using just 500 manually labeled patches. Ablation studies revealed that learning rate scheduling contributed significantly to performance gains than encoder modifications, emphasizing the critical role of training dynamics in data-scarce environments. This research introduces methodological innovations and develops a valuable rice panicle dataset. The segmentation strategies presented here are transferable to other crops and agricultural tasks such as wheat spike detection, maize tassel segmentation, barley head counting, and fruit or leaf analysis. This transfer-ability extends the impact of the work to high-throughput phenotyping, where scalable, automated solutions are essential to meeting global food security goals. Future research can expand on these findings by including additional rice growth stages and varieties, em-ploying self-supervised pretraining or domain adaptation for better cross-field generalization, and optimizing models for real-time deployment on drones or edge devices. A hybrid strategy that combines pseudo-labeling and active learning also holds promise for maximizing segmentation accuracy while minimizing annotation costs in large-scale agricultural monitoring. This thesis lays a solid foundation for AI-driven crop segmenta-tion and contributes to the development of scalable, sustainable, and data-efficient agricultural practices

Working with Undergraduate Students

This blog post documents my work with undergraduate researchers and provides tips for faculty members who are considering supervising undergraduate student researchers

Exploring Physiologic Markers in the Identification of Vasovagal Syncope

Background: Vasovagal Syncope (VVS) is a common cardiovascular disorder, with at least 40% of individuals experiencing at least one episode in their life and about 20% of adults experiencing recurrent episodes. Traditional diagnostic methods such as the Head-Up Tilt (HUT) test and Implantable Cardiac Monitors (ICM) have significant limitations, including limited availability, high costs, and intensive resource requirements. A novel wearable, beat-to-beat blood pressure (BP) monitor is being developed to address these issues. This device, worn on the ear, samples BP at 50 Hz, stores and streams data, and features a rechargeable battery lasting up to 30 hours. This innovative solution will offer a cost-effective, user-friendly alternative for VVS management, potentially improving diagnostic accuracy and patient outcomes. Aims: We aimed to investigate the potential of a wearable beat-to-beat BP monitor in diagnosing vasovagal syncope. The thesis is structured around the following objectives: Objective 1: Provide a detailed narrative review of vasovagal syncope physiology, the Bezold-Jarisch reflex, and the need for a wearable beat-to-beat BP monitor. Objective 2: Validate the short-term blood pressure fluctuations during scripted physiological changes with a wearable beat-to-beat BP monitor. Methods: The narrative review explores physiological mechanisms underlying VVS, highlighting serotonin's role. For Objective 2 data were collected from 14 participants during activities such as rhythmic breathing and hand grips to validate the ear BP monitor. Spectral and coherence analyses assessed synchronization between the ear monitor and the Modelflow system, with non-parametric methods evaluating statistical differences. Results: The narrative review identified key markers for monitoring VVS. The narrative review identified key markers for monitoring vasovagal syncope (VVS), which guided the validation of the ear BP monitor. Data was successfully collected from 14 out of 20 participants, comprising 47% male and 53% female subjects, with an average age of 24.29 ± 5.61 years. The device demonstrated high coherence values (>0.95) across all activities, indicating strong synchronization with the Modelflow system. No significant differences were observed in systolic BP measurements, with p-values of 0.37 for low-frequency components and 0.18 for high-frequency components, confirming the ear BP monitor's ability to capture BP fluctuations during dynamic physiological changes accurately. Further analysis identified the fundamental heartbeat frequency as 1.4 Hz, with harmonics at 2.8 Hz, 4.2 Hz, and beyond. The coherence of these harmonics highlighted the consistency of the heartbeat-induced waveform shapes, indicating that the arterial blood pressure measurements from the Modelflow and EarBP devices were stable and reliable. Coherence across frequencies revealed comparable signal synchronization between the two devices, with significant coherence observed at the fundamental frequency and its harmonics. Coherence values were tightly clustered near the median across all conditions, with the Valsalva maneuver showing slightly more significant variability due to lower coherence values in some participants. These results collectively validate the ear BP monitor as a reliable tool for arterial pressure measurement and synchronized waveform analysis. Conclusion: This thesis advances the understanding of vasovagal syncope (VVS) physiology and validates using a novel ear-based blood pressure monitor. The narrative review identified key physiological markers and mechanisms, including the Bezold-Jarisch reflex and serotonin signaling, central to VVS episodes. The validation study demonstrated high accuracy and synchronization of the ear BP monitor with established standards, offering a non-invasive, wearable alternative for real-time blood pressure monitoring. While promising, further research is required to expand its validation across diverse populations and address potential limitations such as motion artifacts. These findings support the use of an ear BP monitor as an innovative tool with the potential to diagnose vasovagal syncope

Management of Agitation in Emergency Medical Services for Older Adults: A Multi Methods Exploration in Alberta, Canada

Background: Agitation is a commonly encountered challenge with emergency medical services (EMS), which can be difficult to manage, especially in older adults (aged 65+ years). Objectives: The objectives of this thesis were to first understand EMS providers experiences when managing agitated older adults and using physical or chemical restraints. Second, to explore the barriers and facilitators to the management of agitation in older adults faced by EMS providers. Third, to explore the barriers and facilitators to the reduction of restraint use in older adults by EMS providers. Methods: An online survey link was emailed to EMS providers (~6000) in Alberta to explore experiences of agitation management and restraint use. Using semi-structured qualitative Zoom interviews, we explored the barriers and facilitators to agitation management in EMS. Results: A total of 162 survey responses and 30 interviews were collected. EMS providers reported a lack of adequate training and support to manage agitation, however, experienced providers reported comfort with using non-restraint agitation management strategies. Restraints were emphasized as a last resort, especially for older adults, due to the potential for harm. EMS provider burnout, physical, and moral injury were commonly reported. Conclusions: This is the first multi-methods comprehensive understanding of restraint use for the management of agitation in older adults within EMS from the EMS provider perspective

Biochemical Investigations of Bibenzyl Cannabinoids from the Liverwort Radula

Phytocannabinoids, plant-derived molecules characterized by a resorcinyl core structure and its bioactivity for binding and activating endocannabinoid receptors, were believed to be exclusive to Cannabis sativa until the discovery of perrottetinene in the genus Radula. This bibenzyl phytocannabinoid exhibits unique pharmacological effects in mammalian models, highlighting its potential value. However, its biosynthetic pathway remains elusive to date. Using the well-studied phytocannabinoid biosynthetic pathway of C. sativa as a reference, key candidate enzymes involved in perrottetinene biosynthesis were identified. Of primary interest in this study is a putative type III polyketide synthase (PKS) responsible for assembling the core phytocannabinoid structure. Type III PKSs catalyze the formation of many valuable plant metabolites, including flavonoids, curcumin, and cannabidiol. Seven candidate Radula PKS genes were identified through homology and transcriptomic analysis and functionally characterized in in vivo conditions by heterologous expression in Saccharomyces cerevisiae strains engineered to produce various acyl-CoA precursors. Enzymatic activity with both native and non-native substrates was assessed using liquid chromatography mass-spectrometry (LC-MS). The characterization of these novel PKSs provides a foundation for the bioengineering of perrottetinene and synthetic cannabinoids with potential pharmaceutical and industrial applications. To further facilitate gene characterization and metabolic engineering, a CRISPR-Cas9 multiplex genome editing platform was developed in S. cerevisiae. This system, termed 4×4, enables the sequential integration of up to 16 unique DNA fragments by four sequential integrations of a set of four genes. The platform allows for the rapid construction of complex biosynthetic pathways, making it a powerful tool for bioengineering the perrottetinene biosynthetic pathway and for leveraging yeast as a cell factory for the production of high-value metabolites

Patterns in Aquatic Macroinvertebrate Community Composition Among Athabasca Oil Sands Wetlands

Wetlands have appeared in reclaimed Athabasca oil sands landscapes, but their status as compared to the original landscape is not yet well characterized. Relative to many other wetlands in the region, wetlands forming in reclaimed landscapes are characterized by elevated surface water salinity and other contaminants of concern, such as naphthenic acids, which are toxic to many organisms. In addition to these stressors, surface water permanence, indicated by vegetation classes, strongly influences aquatic macroinvertebrate community composition in wetlands, but this relationship has not previously been investigated among reclaimed Athabasca oil sands wetlands. As part of the Boreal Wetland Reclamation Assessment Program, I assessed aquatic macroinvertebrate family richness, density, dominance patterns, and functional representation across 66 wetlands varying in surface water quality, permanence, and age since formation. The major environmental stressors related to aquatic macroinvertebrate community composition include indicators of water quality and surface water permanence, but not wetland age. Wetlands in reclaimed landscapes experiencing multiple elevated stressors are dominated by Diptera (true flies), whereas other wetlands support a greater diversity of families from many taxonomic clades. Further, aquatic macroinvertebrate family richness was higher in more permanent wetlands classes, with more temporary classes largely lacking Amphipoda and Ephemeroptera (mayflies), likely because they require longer developmental periods and lack desiccation-resistant life stages. By identifying thresholds and key environmental drivers, this research provides insight into the conditions necessary to support diverse and resilient macroinvertebrate communities in oil sands reclamation

Brain-first versus Body-first: Exploring aspects of gut microbiota in Parkinson’s disease

Parkinson’s disease (PD) is pathologically characterized by the irreversible aggregation of misfolded α-synuclein protein, forming what is known as Lewy Pathology, which is observed in multiple regions of the brain and gut. The “brain-first” and “body-first” hypothesis proposes distinct patterns of Lewy pathology distribution in the early stages of the disease, which categorizes clinical sub-phenotypes of PD. The “brain-first” sub-phenotype is thought to have predominant involvement of the amygdala, while the “body-first” sub-phenotype involves the enteric nervous system. In mouse models, the microbiota has been shown to be involved in promoting α-synuclein pathology and characteristic motor features in PD. This study aims to investigate whether microbiota from PD patients characterized as “brain-first” or “body-first” sub-phenotypes will induce divergent gastrointestinal functions and motor deficits in human microbiome-associated mice. Human PD microbiome-associated mice were generated by performing fecal microbial transfers from “brain-first” and “body-first” PD donors into germ-free C57BL/6 and dbl-PAC-Tg(SNCAA53T)Snca-/- recipient mice. FMT recipient mice were bred and gastrointestinal and motor functions in these offspring were analyzed. In dbl-PAC-Tg(SNCAA53T)Snca-/- mice, slow intestinal transit, indicative of decreased intestine motility, and increased gastrointestinal permeability were observed in “body-first” group compared to “brain-first” sub-phenotype. No significant motor function differences were detected between the two PD sub-phenotypes in these mouse models. Additionally, male dbl-PAC-Tg(SNCAA53T)Snca-/- “body-first” mice demonstrated a better ability to differentiate preferred odors in the olfactory preference test, whereas those colonized with microbiota from "brain-first" PD patients showed more olfactory dysfunction. Together, these results suggest that gut microbiota from PD patients may influence non-motor impairments associated with PD sub-phenotypes

Entropy Estimation for Arbiter PUF and Applications to Authenticated Key Exchange

Physically Unclonable Functions (PUF) are hardware-based security primitives whose unique challenge and response behavior serves as a "fingerprint" for device authentication, key generation, and anti-counterfeiting. Modeling and estimating the entropy of PUF responses to random challenges is the first step in formalizing the security of PUF-based cryptographic protocols. In this thesis, we consider the Arbiter PUF (APUF), a widely used and studied PUF construction. We propose a novel approach to modeling and estimating APUF entropy as a special type of randomness source, known as the (α,k) source. These sources had been previously used to model low-entropy rate biometric data, such as iris codes. We show that APUFs can be modeled as an (α,k) source by developing a framework to estimate extractable, inter-PUF, and intra-PUF entropies of a large number of simulated APUFs. We use the (α,k) source model of APUF to design a novel mutually Authenticated Key Exchange (mAKE) protocol that uses a single (large) multi-bit APUF response. Our mAKE has proved post-quantum cryptographic security in the random oracle model. The main building block of our construction is a new hash-based robust and reusable Fuzzy Extractor (rrFE) that is obtained by extending the reusable FE in Canetti et al. We implement our protocol and show its performance results