DalSpace Institutional Repository
DalSpace is a digital service that collects, preserves, and distributes digital material produced by the Dalhousie community.
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Recent Submissions
Spatiotemporal Modelling of Lobster Abundance
(2025-04-08) Barss, Joseph; Not Applicable; Master of Science; Department of Mathematics & Statistics - Statistics Division; Not Applicable; n/a; Not Applicable; Adam Cook; Dave Keith; Orla Murphy; Joanna Mill Flemming; Théo Michelot
Species distribution models must account for spatial and temporal auto-correlation in ecological survey data. In this study, we considered a data set on lobster abundance collected by trawl survey programs in the Bay of Fundy area, and fitted a geostatistical generalized linear mixed model incorporating a Gaussian random field to account for spatial auto-correlation. We performed model selection using information criteria and 5-fold spatial block cross-validation. We then used the model’s predictions to produce an index of relative abundance, which displayed an increasing trend between 1995 and 2023. A Bayesian implementation of the model yielded similar results. In a simulation study, we showed that index estimates obtained by modelling standardized count data using the Tweedie distribution are reasonably accurate, and that estimates obtained using delta models are inconsistently biased. A second simulation study showed that combining data from two survey programs is appropriate when creating a model-based abundance index.
Board of Governors Minutes, 2024
(Dalhousie University, 2024) Dalhousie University
Cognitive Abnormalities in Long COVID: Discovering Patient Subtypes using Machine Learning
(2025-04-07) McCord, Betsabe; Not Applicable; Master of Computer Science; Faculty of Computer Science; Received; n/a; Not Applicable; Thomas Trappenberg; Evangelos Milios; Oladapo Oyebode; Carlos Hernandez Castillo
Long COVID is a complex and heterogeneous condition affecting millions worldwide, with cognitive dysfunction emerging as one of its most debilitating and poorly understood symptoms. While neuroimaging studies reveal brain alterations, they may not fully capture the underlying patterns that traditional statistical analyses often overlook. This study applies machine learning to identify neurocognitive subtypes in Long COVID using multimodal MRI data. We compared four hierarchical clustering methods, grey matter-based, white matter-based, structural (combined grey matter and white matter), and a novel multilayer network approach; using Ward’s linkage to ensure consistency. Standardized neuroimaging preprocessing and dimensionality reduction were applied, with clustering performance evaluated through internal and external validation metrics. Results identified distinct neurocognitive subtypes with varying brain structure alterations, cognitive performance, and health metrics. The multilayer network approach outperformed traditional methods, effectively capturing complex grey and white matter interactions. Significant cognitive impairments were linked to specific brain degeneration patterns in population with higher comorbidity risks and more severe symptoms during the acute infection. These findings highlight the potential of machine learning in refining the understanding of Long COVID by identifying structural markers to distinguish different patient subtypes.
Disclosing Mental Health Challenges: A Leader Follower Comparison
(2025-04-07) Adams, Maria; Not Applicable; Master of Science; Business; Received; n/a; Not Applicable; Dana Kabat-Farr; Heidi Weigand; Anika Cloutier
Organizations and scholars are increasingly interested in why people disclose mental health challenges (MHCs) at work. This study examined whether one’s work role (leader or follower) influences one’s willingness to disclose MHCs at work. We predicted leaders would be less likely than followers to disclose MHCs, and that the desire to promote leader prototypes would mediate this relationship. We further hypothesized that personal conditions (gender, stigma) would strengthen, while contextual conditions (health climate, affective trust) would attenuate, this relationship. Four hundred twelve leaders and followers completed an online questionnaire. Results indicated leaders are less willing to disclose MHCs than followers. However, contrary to predictions, leaders aspire to promote leadership prototypes at work more than followers and this increases their future willingness to disclose MHCs. No moderation effects emerged. This research contributes to the leadership and mental health literature and offers insights for organizations wishing to support employees’ access to resources.
ASSESSING THE STABILITY OF ESG INVESTMENTS USING A GARCH (1,1) MODEL
(2025-04-07) Huajian, Miao; Not Applicable; Master of Science; Faculty of Management; Not Applicable; n/a; Not Applicable; Leonard MacLean; Oumar Sy; Yonggan Zhao
This study investigates the volatility, stability, and resilience of Environmental, Social, and Governance (ESG) securities compared to non-ESG securities under varying market conditions. Using the GARCH (1,1) model, we analyze daily financial data from 2020 to 2024 to evaluate how ESG securities respond to market shocks and volatility dynamics. Our analysis focuses on three key dimensions: whether ESG securities exhibit lower volatility, maintain stability over extended periods, and recover more effectively from external disruptions such as the COVID-19 pandemic. The research results show that ESG securities demonstrate significantly lower conditional volatility, with faster reversion to stable states than non-ESG counterparts. During market turbulence, ESG securities experience smaller price declines and quicker recovery, highlighting their resilience. Sector-specific analysis reveals that renewable energy and technology sectors benefit most from ESG integration, while traditional industries show limited improvements. These findings suggest that ESG investments mitigate financial risk and enhance long-term securities sustainability. This research contributes to the sustainable finance literature by empirically validating the risk-management advantages of ESG strategies. It provides practical insights for investors and policymakers seeking to align financial goals with sustainability objectives. While the study relies on historical data and aggregated ESG metrics, future work could explore dynamic ESG scoring and cross-regional comparisons to refine the understanding of ESG financial impact further.
Beneath the surface: Community archives and belonging in Cumberland County, NS
(2025-04-04) Burbine, Cassandra; Not Applicable; Master of Information; School of Information Management; Received; n/a; Not Applicable; Alison Brown; Phillipe Mongeon; Sandra Toze
This study explores the connections and disconnects between archives, people, and community in deindustrialized areas of Cumberland County, Nova Scotia. I have sought to understand one central question: “In what ways can local archives improve one’s sense of belonging within (and understanding of) community?” To address this, I have used a narrative inquiry approach and interviewed four archival professionals who run community archives in the area, as well as four community members who have accessed local archives. The archival professionals serve as key informants, while the community participants provide rich perspectives on their experiences in their communities and in accessing archives. Based on thematic analysis of these interviews, it is clear that engagement with local archives can affect feelings of belonging in rural and working-class deindustrialized communities, namely by deepening connections between individual, historical, and geographic identity.