Faculty of Graduate Studies Online Theses

Permanent URI for this collectionhttps://hdl.handle.net/10222/11163

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Open Access
Beaded Buildings: Ornament, Craft and Digital Manufacturing in Canadian Architecture
(2025-04-14) Wallace-Lund, Jessie Hannah; Yes; Master of Architecture; School of Architecture; Not Applicable; María Arquero de Alarcón; Not Applicable; David Correa; James Forren
Ornament is an aspect of architectural practice that has seen a fall from grace since the early 20th century, but a recent “return” of ornament is being recognized in architectural discourse, inviting a new era of artistic and craft exploration that dovetails with the development of digital fabrication technologies. Beadwork is an ancient and highly valued art form that has so far rarely been incorporated into the built environment. Commonly known as 3D printing, Robotic Additive Manufacturing of ceramics for architectural application is likewise still a new field in architecture. This thesis focuses on the potentials of 3D printed ceramic components for architecture, presenting a suite of functional computational tools for the manufacture of building components, exploiting the unique aesthetic qualities of 3D printing to create “beaded” qualities.
Open Access
A Girl of Constant Sorrow: The Sad Girl, Authenticity and Personas in Popular Music
(2025-04-15) Leon, Rebekah; No; Master of Arts; Fountain School of Performing Arts; Not Applicable; Steven Baur; No; Jacqueline Warwick; Jennifer Bain
This thesis analyzes the music and careers of Billie Eilish and Lana Del Rey to demonstrate how a wave of American female pop stars leveraged the Sad Girl archetype to inform their personas and gain mainstream popularity in the first quarter of the 21st Century. These artists rely upon a culturally constructed sadness that is associated with whiteness and femininity and their inherent privilege to curate their authenticity and market their vulnerability across media. However, artists who adopt the Sad Girl archetype can elicit polarizing appraisals from the public sphere; drawing on works in popular music studies, persona studies, and feminist media studies, I propose a layered approach to persona that divides the public identities of musical performers into a series of coexistent personas to determine that aberrations from audience expectations can confuse the collective consensus and negatively impact artists’ public perception.
Open Access
Gloves à la Chopin: Androgyny, Disability, and the Performance of Consumption
(2025-04-23) Thomas, James; Not Applicable; Master of Arts; Fountain School of Performing Arts; Not Applicable; Dr. Megan Johnson; Not Applicable; Dr. Roberta Barker; Dr. Steven Baur
Despite the historical evidence of Chopin’s non-normative relationship to gender and disability, musicological scholarship largely neglects this aspect of the revered composer-pianist’s career. Chopin’s Romantic-era Paris put a premium on social intrigue, and the sentimental imagery surrounding consumption posed his chronic illness as a sign of genius, sensitivity and creativity. Presenting himself as a consumptive artist was a compromise between the accommodation of a real, life-altering disease and self-expression through social and artistic means. The resulting artistic persona blurred the boundaries of class, gender, ability, and even that between the real and mythological. Chopin’s artistic persona was celebrated by contemporaneous audiences, who were intrigued by his androgyny and obsessed with his sickness. This thesis explores the interaction between gender nonconformity, disability and romantic pianism. I make the case that Chopin’s artistic persona was both grotesque and fashionable in equal measure, and was wholly emblematic of the Romantic era.
Embargo
MAST CELL STING ACTIVATION IN INFECTION AND CANCER IMMUNITY
(2025-04-22) Al Bitar, Haya; Not Applicable; Master of Science; Department of Microbiology & Immunology; Received; Dr. Tobias Kollmann; Not Applicable; Dr. Ian Haidl; Dr. Craig McCormick; Dr. Paola Marcato; Dr. Jean Marshall
Mast cells (MCs) are long-lived, tissue-resident immune cells essential for host defense. The STING pathway, a key innate immune response to infection and cellular stress, promotes strong type I interferon (IFN) and pro-inflammatory responses. While the STING pathway holds therapeutic potential in cancer and infection, its role in MCs remains underexplored. Our study demonstrates that MCs trigger type I IFN and NF-κB responses upon STING activation. We show that MCs are susceptible to Shigella flexneri infection, leading to an upregulation of type I IFN and interferon-stimulated gene expression, partially dependent on STING. In a murine ovarian cancer model, MC deficiency led to longer survival, whereas reconstituted MC-deficient mice surprisingly showed improved survival. Treatment with a STING agonist increased survival, but overexpressing STING in MCs within tumors provided no additional benefit. These findings offer valuable insights into STING-mediated immunity in MCs and highlight potential avenues for future therapeutic exploration.
Open Access
Developing Vehicle Ownership and Activity-Based Travel Demand Models for Transportation Network and Emission Analysis
(2025-04-22) Shahrier, Hasan; No; Doctor of Philosophy; Department of Civil and Resource Engineering; Received; Dr. Trevor Hanson; Yes; Dr. Hany El Naggar; Dr. Uday Venkatadri; Dr. Ahsan Habib
Vehicle ownership significantly contributes to greenhouse gas (GHG) emissions, as the type of vehicle chosen for various activities directly affects fuel consumption and emission levels. This study presents an Integrated Transport Land-use and Energy (iTLE) modelling framework including the diversification of vehicle type to assess vehicle ownership level, travel patterns, traffic network performance, and vehicular emissions in the Halifax Regional Municipality (HRM). The study begins with the development of electric vehicle (EV) adoption models, exploring the socio-demographic factors influencing EV ownership through machine learning-based clustering and econometric models. The findings highlight the role of income, education, employment status, and household characteristics in shaping EV adoption patterns. Additionally, an electric vehicle type choice model integrates attitudinal and lifestyle factors, identifying distinct user classes with varying preferences for battery electric vehicles (BEVs), plug-in hybrid electric vehicles (PHEVs), hybrid electric vehicles (HEVs), and internal combustion engine (ICE) vehicles. The study also examines activity start-times, durations, and vehicle allocation using advanced statistical modeling. A nested Archimedean copula framework captures the dependencies between individuals’ activity start-time and duration, highlighting variations in travel pattern across different demographics. Further, a vehicle allocation model analyzes preferences for different fuel types across mandatory, maintenance, and discretionary activity-based tours, providing insights into vehicle type choice decisions based on accessibility measurements and household characteristics. This study advances a prototype of the iTLE model to forecast future electric vehicle (EV) adoption and evaluate its effects on greenhouse gas (GHG) emissions. The simulation outcomes reveal a notable decrease in emissions across various scenarios as EV adoption grows, emphasizing its potential to enhance sustainable transportation strategies. Additionally, the research employs an in-depth traffic simulation to estimate vehicle kilometers travel (VKT) and analyze emissions at a highly detailed level. It considers different vehicle types, including electric, gasoline, and diesel-powered vehicles, alongside various activity categories such as mandatory, maintenance, and discretionary trips. This comprehensive approach allows for more precise emission evaluations and facilitates the identification of urban pollution hotspots using spatial analysis. By integrating transportation modeling, behavioral analysis, and emissions forecasting, this study provides a comprehensive tool to support policy decisions for sustainable urban mobility. The findings offer valuable insights for urban planners and policymakers aiming to achieve net-zero GHG emissions by 2050 and promote cleaner, more efficient transportation systems.
Open Access
Multimodal approach for seafloor image classification using feature-level data fusion
(2025-04-22) Pandya, Kedar; Not Applicable; Master of Computer Science; Faculty of Computer Science; Not Applicable; n/a; Not Applicable; Craig Brown; Janarthanan Rajendran; Thomas Trappenberg
Mapping seafloor substrates is crucial for understanding benthic habitats and monitoring their changes. Traditionally, this task involves manually annotating underwater images, an approach increasingly impractical due to growing data volumes. This thesis investigates a multimodal deep-learning approach for classifying seafloor substrates by integrating visual images with sonar data (backscatter and bathymetry) collected from the Bay of Fundy. To identify the role of wider spatial context, two sonar data sampling methods were compared: single-point sampling, which assigns a single sonar value based on image coordinates, and context-based sampling, which incorporates sonar data from adjacent measurements. Experiments demonstrated that context-based sampling, which provided a wider spatial context, significantly improved substrate classification accuracy. Feature-level data fusion strategies were then evaluated by encoding sonar data using multilayer perceptrons and extracting marginal representations from different layers of the sonar encoder. These representations were fused with visual features extracted via convolutional neural networks. The effective fusion strategy, established through these experiments, was subsequently implemented using a pre-trained Vision Transformer and ResNet50 models as image encoders. While ResNet50-based multimodal models showed only moderate improvements relative to a baseline trained solely on visual data, ViT-based fusion models achieved significantly larger gains, exceeding two standard deviations and improving accuracy by approximately 35% for challenging substrate types with substantial class overlap. Overall, this research demonstrates that multimodal learning frameworks can effectively leverage complementary features from sonar data sampled to provide a wider spatial context and visual data, resulting in substantial improvements in seafloor substrate classification accuracy. These findings establish a robust foundation for further research in multimodal oceanography and benthic mapping.
Open Access
Anticipatory Locomotor Adjustments During Walking Over Unilateral Obstacles In Able-Bodied Participants
(2025-04-22) Ohanessian, Gary; No; Master of Science; School of Health & Human Performance; Received; Dr. David Westwood; Not Applicable; Dr. Scott Landry; Dr. Derek Rutherford; Dr. Christopher MacLean; Dr. Michel Ladouceur
Walking control was assessed by investigating the changes in net joint power while going over unilateral obstacles placed in the plane of progression. Participants performed obstructed walking trials across seven different obstacle heights (0 to 60 cm) while kinematic and kinetic data were collected. Anticipatory locomotor adjustments (ALA) were observed in both crossing and supporting legs. Notably, there were significant adjustments in the supporting leg with the emergence of a plantar flexor energy generation phase, accompanied by an increase in hip extensors energy generation at the onset of the stance phase for higher obstacles. Furthermore, a complementarity in the power bursts of the crossing leg muscles was noted. Specifically, the left leg exhibited a greater pulling motion at the knee due to an enhanced knee flexor energy generation. This thesis contributes to a greater characterization of ALA to unilateral obstacles and provides some evidence about the complementarity of these adjustments.
Embargo
ENDOLYSOSOMAL TRPML3 / BK COMPLEX IN AUTOPHAGY INDUCTION AND PATHOGEN DEFENSE
(2025-04-15) Xu, Mengnan; Not Applicable; Doctor of Philosophy; Department of Physiology & Biophysics; Not Applicable; Ryan Charles Russell; Yes; Zhenyu Cheng; Yassine El Hiani; Paul Linsdell; Xianping Dong
The transient receptor potential mucolipin 3 (TRPML3), encoded by the MCOLN3 gene, functions as a calcium (Ca²⁺) release channel localized to endolysosomal membranes. This channel is activated by phosphatidylinositol 3,5-bisphosphate (PI3,5P2), with its activity further enhanced by elevated luminal pH or substitution of luminal sodium (Na⁺) with potassium (K⁺). In this study, we identify a positive feedback loop between TRPML3 and the big-conductance Ca²⁺-activated potassium channel (BK). Ca²⁺ release through TRPML3 activates BK channels, which subsequently enhance TRPML3-mediated Ca²⁺ release, likely by providing a counter flux of K⁺ and alleviating the inhibitory effect of luminal Na⁺. We further demonstrate that TRPML3/BK and the mammalian target of rapamycin (mTOR) form another positive feedback loop. mTOR inhibition during nutrient starvation activates TRPML3/BK channels, which further suppress mTOR activity, thereby increasing autophagy induction. Mechanistically, this regulatory interplay is mediated by phosphatidylinositol 3-phosphate (PI3P), an endogenous TRPML3 activator that is enriched in phagophores and becomes upregulated as mTOR activity decreases. Importantly, bacterial infection triggers TRPML3 activation in a BK-dependent manner, with both TRPML3 and BK playing crucial roles in the suppression of mTOR and the promotion of autophagy in response to infection. Inhibition of TRPML3 or BK enhances intracellular bacterial survival, while upregulation of either protein facilitates bacterial clearance. Given that TRPML3/BK is inhibited at low luminal pH but activated by high luminal pH and PI3P in phagophores, we suggest that the TRPML3/BK and mTOR feedback loop via PI3P is critical for efficient autophagy induction during nutrient deprivation or bacterial infection. These findings reveal a pivotal role for TRPML3-BK coupling in maintaining cellular homeostasis and mediating intracellular bacterial clearance by regulating mTOR signaling pathways.
Open Access
Validating an Inertial Measurement Unit System for Tracking Ice Hockey Goaltender Kinematics
(2025-04-15) Sutherland, Dylan; Not Applicable; Master of Science; School of Health & Human Performance; Not Applicable; Dr. Scott Landry; Not Applicable; Dr. Heather Neyedli; Dr. Christopher MacLean; Dr. Ryan Frayne
Ice hockey goaltenders perform save stances requiring extreme hip motions, increasing their risk of injury. Tracking these movements with traditional motion capture methods provide accurate kinematic data but are limited by ecological validity. Inertial measurement unit systems, such as the Xsens MVN Link, offer an alternative for quantifying goaltender kinematics. This study aimed to validate the accuracy and reliability of the Xsens MVN Link hip joint angles while goaltenders wore full protective equipment. Ten goaltenders (m=7, f=3) performed save movements where Xsens data were compared to optical motion capture. Accuracy results showed moderate sagittal plane agreement during simple stances but revealed proportional biases across all axes, with notable errors in frontal and transverse planes at high flexion angles. Reliability analysis indicated moderate agreement between sessions, with a low fixed bias (~5°). The Xsens MVN Link shows potential for quantifying goaltender kinematics; however, its limitations in complex situations necessitate further investigation.
Open Access
A Study of Techniques for Robustness to Out-of-Distribution Examples
(2025-04-17) Shama Sastry, Chandramouli; Not Applicable; Doctor of Philosophy; Faculty of Computer Science; Not Applicable; Dr. Pawan Lingras; Not Applicable; Dr. Evangelos Milios; Dr. Ga Wu; Dr. Sageev Oore
Deep neural networks have achieved remarkable success and human-level performance in many tasks and yet, behave unpredictably when input examples are not guaranteed to be similar to the train distribution. In this thesis, we address the limitations of deep neural networks under distributional shifts, focusing on adversarial examples, covariate shifts, and out-of-distribution (OOD) samples. Ideally, we expect a robust neural network to withstand adversarial perturbations, adapt to covariate shifts, and gracefully refuse to operate on OOD examples. Recognising robustness as a critical challenge for safe and trustworthy deployment, we develop and evaluate train-time and post-training methods --- and their combination --- to address the aforementioned aspects of robustness. First, we introduce a novel post-training OOD detection technique based on Gram matrices of intermediate representations. Notably, this method achieves state-of-the-art performance on several benchmarks without requiring prior knowledge of OOD examples. Our method can also be combined with Outlier-Exposure (OE) to achieve improved robustness, especially on challenging near-distribution outliers. However, since OE relies upon extra data, we explore generative models for improved robustness as described below. Next, we introduce DiffAug, a diffusion-based augmentation method for enhancing robustness against covariate shifts, adversarial perturbations, and OOD inputs. Using DiffAug, we also improve classifier-guided diffusion by achieving improved perceptual alignment of gradients. We thus introduce a computationally efficient technique for training with improved robustness that does not require any additional data, and effectively complements existing augmentation approaches. Moving beyond image classification, we also explore robustness in time-series forecasting --- a domain inherently affected by non-stationary distribution shifts. Building on the DeepTime framework, we propose a theoretically motivated regularization term that improves forecast accuracy under challenging conditions, such as missing data, reduced training set sizes, or higher test-time frequencies. In summary, we present train-time and post-training techniques to enhance model robustness. Beyond their application to improve model robustness, we believe that the research findings offer new insights about the internal workings of a neural-network opening up several interesting future research directions.
Embargo
IN VITRO ASSESSMENT OF BIOACTIVE PHYTOCHEMICALS EXTRACTED FROM UPCYCLED KALE TO USE IN COSMECEUTICALS
(2025-04-15) Valisakkagari, Harichandana; Yes; Master of Science; Department of Plant, Food and Environmental Sciences; Not Applicable; n/a; Yes; Dr. Sophia He; Dr. Xiaohong Sun; Dr. H.P. Vasantha Rupasinghe
Food waste is a global concern, contributing to environmental degradation through greenhouse gas emissions and excessive landfill use. Upcycling food waste is emerging as one of the strategies to recover bioactive phytochemicals for cosmeceuticals, contributing to waste reduction. This study optimized carotenoid extraction from upcycled kale using response surface methodology-central composite design (RSM-CCD), identifying optimal conditions (100% ethanol, 57 °C, 30 min) that yielded 392 μg carotenoid/g dry weight (DW) using ultrasound-assisted extraction (UAE). The ultra-high-performance liquid chromatography-electrospray ionization-mass spectrometry (UPLC-ESI-MS) analysis revealed lutein, quercetin, chlorogenic acid, and sulforaphane as key bioactive phytochemicals. Furthermore, in vitro assays showed UAE-kale extract, chlorogenic acid, and sulforaphane reduced UV-induced DNA damage in WS1 cells. All the tested compounds demonstrated similar effects on reducing the UV-induced reactive oxygen species levels. The findings highlight that upcycled kale extracts show potential for UV-protection, which need validation through in vivo studies.
Open Access
THE INFLUENCE OF POTATO (SOLANUM TUBEROSUM) PRODUCTION SYSTEMS ON SOIL PROPERTIES, NITROGEN CYCLING COMMUNITIES, AND THE SOIL MICROBIOME.
(2025-04-14) MacIntyre, Leah; Not Applicable; Master of Science; Faculty of Agriculture; Not Applicable; n/a; Not Applicable; Ikechukwu Agomoh; Yunfei Jiang; David Burton; Claudia Goyer
Increasing plant diversity, soil coverage, and rotation length in potato production systems can improve soil quality and productivity but could also result in trade-offs associated with the soil microbiome, namely increased nitrogen (N) losses from nitrification and denitrification, or increased disease pressure. The objective of this study was to compare the short-term effects of four potato crop production systems (CPS) established in 2021 on the bacterial and fungal communities under conventional fertilizer management, the severity of soilborne diseases of potato, and the abundance of seven N-cycling genes and emissions of nitrous oxide (N2O) with and without N fertilizer. Overall, findings from this thesis highlight that diversifying potato CPS can quickly affect bacterial and fungal communities and can begin to have beneficial implications for soil health management even after the first rotation cycle.
Open Access
A’ faighneachd Mhic-Talla [Asking the Echo]: A corpus-based approach to vernacular classification of Gaelic songs
(2025-04-15) MacPherson, Chelsey; Not Applicable; Master of Information; School of Information Management; Not Applicable; n/a; Not Applicable; Shamus Y. MacDonald; Louise Gillis; Heather Sparling; Sandra Toze; Philippe Mongeon
Although Gaelic songs are an important facet to Gaelic cultural heritage, no study to date has systematically examined vernacular classifications of Gaelic songs. Due to this omission, digital archives may utilise foreign terminologies as descriptive metadata to classify Gaelic songs. These labels will influence how Gaels and non-Gaels interpret and understand Gaelic culture. Potential misrepresentations in archives can lead to a dilution of Gaelic culture. This study addresses this gap by utilising corpus linguistic methods to analyse adjectival and genitive constructions of the most common Gaelic word for song-poem, òran, and a dialectal variation amhran, while also observing vernacular categories in concordance of òran and amhran and traits of broader classification from the Gaels’ own narratives in the longest-running, all-Gaelic newspaper Mac-Talla (1892–1904). Forty-nine vernacular categories were observed along with various broader classifications such as melodic quality, age, taste, and bard. By engaging with Gaelic vernacular knowledge, this research aims to contribute to the discourse and development of culturally sensitive metadata for Gaelic songs in digital archives.
Open Access
Characterization of phytoplankton species in oyster habitats in Cape Breton, Nova Scotia
(2025-04-14) Hanrahan, Gracie; Not Applicable; Master of Science; Department of Animal Sciences and Aquaculture; Not Applicable; Ramon Filgueira; Not Applicable; Sarah Stewart-Clark; Shabana Bhatti; Stefanie Colombo; Rod Beresford
Phytoplankton is the base of marine food webs and all marine life depends on it for survival. This study identified phytoplankton species found in the water of the Bras d’Or Lake and in oyster gut contents using DNA sequencing and performed a fatty acid analysis of phytoplankton cultures to determine their nutritional value. Three locations in the Bras d’Or Lake and surrounding area were investigated between spring and fall to determine differences in phytoplankton composition. Nannochloris sp. was identified in water samples, oyster gut contents, and phytoplankton cultures which represents a species in the Bras d’Or Lake that can be grown as a food source for C. virginica and provides adequate essential fatty acids alongside other phytoplankton in the diet that contain nutrients it lacks. Continued monitoring of Bras d’Or Lake phytoplankton and its nutritional composition is essential to understand the environment and phytoplankton being ingested by oysters.
Embargo
Locomotion Techniques for Virtual Reality Omnidirectional Treadmills: An Evaluation on User Performance and Wayfinding
(2025-04-15) Homami, Helia; Yes; Master of Computer Science; Faculty of Computer Science; Received; Adria Quigley; Yes; Derek Reilly; Joseph Malloch; Mayra Donaji Barrera Machuca
Navigating virtual environments that exceed the boundaries of the physical space remains an open research challenge in Virtual Reality (VR) applications. In particular, rehabilitation and sports training require locomotion techniques that deliver realistic movement without compromising the user's navigation and wayfinding abilities. This thesis evaluates two VR omnidirectional-treadmill locomotion techniques, walk-in-place with the Kat VR and sliding with the Cyberith Virtualizer, and compares them against natural walking in an open space. Using a within-subject study with 18 participants who navigated a maze under each technique, user performance was assessed via metrics such as completion time, step count, and task load. Additionally, spatial understanding was evaluated by analyzing head-position and head orientation data, alongside post-trial sketch maps to measure spatial memory. The results reveal that natural walking enables faster and more efficient navigation with lower physical exertion than the treadmill-based techniques, while yielding smoother and more continuous exploration. In contrast, walk-in-place and sliding produced more frequent stops, increased reorientation, and had a higher cognitive load. However, these additional cognitive demands did not significantly affect overall spatial memory, as determined by the post-trial sketches and head-tracking data. These findings demonstrate that although treadmill-based locomotion techniques may require more directional adjustments, they do not impair the user's ability to form accurate cognitive maps. The insights from both the user performance and spatial understanding studies provide valuable guidance for designing VR locomotion systems and developing intuitive interfaces that balance immersive movement with efficient spatial understanding.
Open Access
SOVEREIGN DEBT AND THE ILLUSION OF SUSTAINABLE DEVELOPMENT IN AFRICA: COLONIAL ROOTS AND NEO-COLONIAL DEBT TRAPS
(2025-04-15) Akinyi, Eurallyah; Not Applicable; Doctor of Philosophy; Faculty of Law; Not Applicable; Prof Ohio Omiunu; Not Applicable; Prof Sara Seck; Prof Liam Mc-Hugh Russell; Prof Olabisi Akinkugbe
This thesis critically examines the intersection of sovereign debt and sustainable development in Africa, focusing on how the global financial and debt architecture perpetuates colonial and neo-colonial inequalities. Through a theoretical framework grounded in TWAIL, the study reveals how Africa's unsustainable debt burdens stem from historical legacies of imperialism and the structural imbalances of contemporary global debt governance systems. The research explores the colonial origins of sovereign debt and its evolution into a neo-colonial tool for economic subjugation. It emphasizes how institutions like the IMF, the World Bank, and private creditors, through policies such as loan conditionalities, debt restructuring, balance-of-payment surveillance, surcharges, and the quota system, undermine the development agendas of African states. These policies often prioritize creditors' interests over human rights, economic equality, and environmental sustainability, creating a paradox where debt financing intended to support development instead exacerbates poverty and inequality.
Embargo
Photochemical Processes for Remediation of Steroid Estrogens
(2025-04-15) Bennett, Jessica Lee; Yes; Doctor of Philosophy; Department of Civil and Resource Engineering; Not Applicable; Dr. Maricor Arlos; Yes; Dr. Margaret Walsh; Dr. Neil Ross; Dr. Graham Gagnon
The growing presence of steroid estrogens in aquatic systems poses risk to ecological health and stability. These compounds can elicit endocrine disrupting effects at even trace (i.e., parts-per-trillion) concentrations making monitoring and treatment efforts especially challenging. The objectives of the work were to 1) Develop and validate a QuEChERS method for improved isolation of parts-per-trillion concentrations aqueous steroid estrogens in a complex water matrix for quantitation via LC-MS/MS, 2) Evaluate the effectiveness of a commercially available treatment technology, medium-pressure UV (MP UV) photolysis, for aqueous steroid estrogen removal to develop an understanding of existing treatment capabilities, 3) Determine the efficacy of NO3- as an in-situ photooxidant in the MP UV treatment process and understand its implications for mitigating aqueous steroid estrogens, and 4) Assess the utility of UV LED photolysis as a tuneable treatment approach for targeted degradation of aqueous steroid estrogens and other trace organic compounds.
Open Access
Seeing and Hearing: The Influence of AI-Generated Political Media on Public Trust and Intentions
(2025-04-14) Shakeeb, Hinda; Not Applicable; Master of Digital Innovation; Faculty of Computer Science; Received; n/a; Not Applicable; Binod Sundararajan; Sandra Toze; Colin Conrad
Artificial Intelligence (AI) is reshaping political communication through deepfakes, synthetic voices, and manipulated images. While promising for engagement, these media also raise concerns around misinformation and public trust. This study experimentally examines how different AI-generated media formats (image, video, audio) and realism levels affect trust and political decision-making. Results from Linear Mixed Models and Natural Language Processing reveal that audio content is perceived as more trustworthy than video or images, supporting cognitive load theory. High realism enhances trustworthiness, while increased excitement may reduce skepticism, making audiences more persuadable. Though generalizability is limited by the controlled setting, the findings offer novel insight into how AI-generated political content influences perception and behavior. This research contributes to political communication and media psychology by highlighting both the persuasive power and ethical risks of AI in digital politics.
Open Access
Architectural Dressing: Rethinking Conservation as an Act of Care, Adaptation, and Material Engagement
(2025-04-14) Kinnear, Bailey; Not Applicable; Master of Architecture; School of Architecture; Not Applicable; n/a; Not Applicable; Catherine Venart; Christopher Trumble; Michael Faciejew
This thesis introduces architectural dressing as a transformative approach to heritage conservation, advocating for the treatment of buildings as dynamic, evolving entities rather than static artifacts. Contrasting with traditional preservation methods that emphasize permanence, this methodology adopts a participatory, material-based engagement akin to dressing a body. Utilizing Critical Care Theory, it argues for a curatorial approach focused on repair, layering, and reinvention. The case study of NSCAD’s Fountain Campus in Halifax illustrates this method in action, highlighting its potential to sustain heritage sites as vibrant spaces of artistic production and public engagement. By drawing parallels between buildings and corsets, which historically restricted but now empower the body, the thesis proposes conservation as an active, care-driven practice, ensuring buildings remain integral, lively participants in the urban fabric.
Embargo
DESIGN OF PICKER-TO-PARTS WAREHOUSE FULFILLMENT SECTIONS USING SURROGATE MACHINE LEARNING MODEL
(2025-04-15) Basava Sri Krishna Vamsy, Lanka; Not Applicable; Master of Applied Science; Department of Industrial Engineering; Not Applicable; n/a; Not Applicable; Dr. J. Pemberton Cyrus; Dr. Ya-Jun Pan; Dr. Uday Venkatadri
The design of picker-to-parts warehouse sections contains various decision parameters such as warehouse dimensions, routing policy, and storage assignment policy. Assessing the holistic importance of each decision parameter cannot be easily quantified due to their mutual interdependence. It is crucial to obtain this information and investigate the possible combinations of policies and warehouse specifications. To solve this problem, we use a surrogate machine learning model to simulate the warehouse conditions across varying pick list sizes. Seasonally varying demand and pick face requirements are also considered. A dataset derived from simulation is used to train various machine learning algorithms. The model uses the Monte Carlo method and average travel distance as the output parameter to evaluate performance. The Random Forest, Decision Tree, Gradient Boost, XG Boost, LightGMB, CatBoost, and a tuned Artificial Neural Network show the best performance in terms of error and fit. SHAP feature importance is calculated for interpretability analysis. Warehouse design practitioners and fourth-party logistic problems can easily adapt and deploy the developed warehouse simulation methodology and machine learning model to help with bid design in determining optimal warehouse parameters and policies.