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Globalization for Scalable Short-term Forecasting of Heterogeneous Loads
No full textPower systems are structured hierarchically, emphasizing the need for multi-step load forecasting not only at the system level but also at primary and secondary substations, area levels, and individual Points of Delivery (PoDs). This necessitates scalable forecasting techniques that can handle numerous measuring points with diverse characteristics while ensuring timely and accurate predictions. While intuitive and locally accurate, traditional local forecasting models (LFMs) struggle with scalability, becoming computationally expensive and less efficient as network size and data volume grow. In contrast, global forecasting models (GFMs) enhance generalizability, scalability, and robustness through globalization and cross-learning. However, GFMs assume that input time series are inherently related, overlooking potential spatiotemporal data heterogeneity. Heterogeneity in time series refers to diverse characteristics across different series or within a single series over time, which can bias forecasts and reduce generalizability if unaddressed. Data heterogeneity can be broadly categorized into spatial and temporal heterogeneity. Spatial heterogeneity arises from structural and behavioral differences across geographical areas, customer types (e.g., residential, commercial, industrial), and grid levels (e.g., system-level, substations, PoDs). Temporal heterogeneity, on the other hand, refers to changes within a single time series over time due to evolving user behavior, technological upgrades, or external disruptions such as extreme weather, wildfires, or policy interventions, which can manifest as data drift or concept drift. This thesis investigates globalization in the presence of data heterogeneity in power systems. First, a method is proposed to identify and segment price-responsive customers as an expression of temporal heterogeneity. It then examines global load forecasting under data drifts, comparing feature-transforming and target-transforming models to reveal how globalization, heterogeneity, and drift influence performance. The role of globalization in peak load and zero-shot forecasting is also explored. To address spatial heterogeneity and balance globality with locality, we propose time series clustering (TSC) methods, model-based TSC for feature-transforming models, and weighted instance-based TSC for target-transforming ones. Finally, we study globalization in multistep and probabilistic settings, emphasizing the importance of globalization and tackling temporal heterogeneity and sample indistinguishability. We propose a regime-aware global forecasting framework with temporal embeddings to address the challenges posed by temporal heterogeneity, while introducing a temporal globalization strategy to capture both long-term trends and short-term dynamics. Extensive experiments on real-world datasets validate the effectiveness of the proposed approaches
Redefining Resilience in Healthcare Leadership
No full textThe purpose of this qualitative research was to explore resiliency as a core leadership competency that healthcare leaders could develop, through participation in a leadership communities of practice. The term resilience has been misunderstood within healthcare and is often thought of as an inherent trait that leaders possess, rather than a skill that can be developed. As such, this study explored reimagining resilience as a set of abilities leaders can develop to navigate the complexity of the current healthcare system. Additionally, a leadership communities of practice was formed with research participants as an overarching framework to support leadership development. This research used a thematic analysis methodology to analyze the research findings from participants, which were made up of twelve healthcare leaders within Nova Scotia Health, a small provincial health authority on the east coast of Canada. Findings from the study concluded that participants’ view of resiliency evolved through their participation in the communities of practice, demonstrating that not only was resilience a practical leadership skill for healthcare leaders, but a leadership community of practice offered a beneficial approach to leadership development. This study also used an applied research approach that provides a practical and tangible approach to building resilience in healthcare leaders and enhancing their leadership skills by offering key recommendations for healthcare organizations to consider in supporting and enhancing the skills of their leaders. Keywords: resilience, communities of practice, leadership developmen
GICEDCAM: A Geospatial Internet of Things Framework for Complex Event Detection in Camera Streams
No full textComplex Event Detection (CED) in video streams is increasingly important for surveillance, safety monitoring, and real-time situational awareness. However, detecting complex events remains challenging due to missed object detections, unreliable spatial–temporal relationships, and the high computational cost of existing frameworks. This dissertation addresses these challenges through a manuscript-based thesis consisting of three integrated studies. The first study presents a systematic review of event-matching methods in video-based CED, analyzing 92 papers published from 2012 to 2024. The review shows that Object Detection and Spatio-temporal Matching (ODSM) approaches are the most suitable for near-real-time applications but still suffer from missing simple events, inflexible event-reasoning mechanisms, and limited scalability. These findings motivate the methodological gaps addressed in the remaining two studies. The second study develops an Internet of Smart Cameras (IoSC) architecture that uses edge–cloud collaboration and overlapping camera views to compensate for missed detections. By integrating simple events detected from multiple viewpoints, the IoSC framework significantly reduces false negatives and improves complex-event recognition. Experiments on COVID-19 risk-behavior scenarios show that the IoSC design improves CED accuracy by 1.73× compared to single-camera edge processing. The third study generalizes these insights into GICEDCAM, a geospatial IoT framework that distributes CED workloads across edge, stateless, and stateful layers. GICEDCAM introduces a spatial-event corrector, implemented using Bayesian networks, LSTM models, and trajectory analysis, to reconstruct missing spatial relationships and reduce false positives. Evaluations across four complex-event scenarios demonstrate that GICEDCAM reduces end-to-end latency by up to 36% and lowers computational cost by 45% relative to an open-source baseline, with performance advantages increasing under higher object densities. Among corrector variants, the trajectory-based method offers the best accuracy–latency trade-off for real-time deployments. Together, these three studies contribute a unified, scalable, and geospatially enriched approach to real-time complex event detection. The thesis advances CED research by (1) identifying methodological gaps in the literature, (2) demonstrating how multi-camera fusion reduces missing simple events, and (3) introducing a multi-layer IoT architecture that improves event-level reasoning, scalability, and computational efficiency
Subsurface thermal energy recovery from depleted heavy oil reservoir after in-situ combustion operations and its implication on CO2 storage
No full textAbstract In-situ combustion (ISC) has a huge potential in recovering heavy oil resources with a low environmental footprint. At the end of the ISC operation, a huge amount of thermal energy is left in the reservoir. By appropriately recovering the thermal energy in the depleted reservoirs after ISC operation, it could extend the economic life of the heavy oil reservoirs. This work investigated the potential of extracting heat using the cold water and CO2 as the working fluids to advance the knowledge base regarding the thermal energy recovery and CO2 storage in depleted ISC reservoirs. The extracted fluids are fed to the surface binary cycle for power generation or district heating. Simulation of ISC was performed to estimate the temperature, energy, and fluids distributions after 20 years of the oil production. An assessment of the energy remaining in the reservoir is performed. It is estimated that a total of 3 × 1014 J thermal energy was left underground after ISC operation in the typical five-spot well pattern. Subsequently, an examination of the potentially recoverable energy from the post ISC operation is performed. Cold water and CO2 are recirculated into the reservoir for energy recovery. The results indicates that the heavy oil reservoir after ISC operation can be regarded as an artificial geothermal system for subsurface thermal energy extraction. As the water circulation rate increases from 200 m3/day and to 400 m3/day and 600 m3/day, the average reservoir temperature is declined to 95 ℃, 79 ℃, and 71 ℃, respectively. Meanwhile, the corresponding thermal energy recovery factors are 48%, 63%, and 77%, respectively. Larger water circulation rate can generate high energy output and high energy recovery in the post ISC operation. The thermal energy recovery could prolong the energy production to 15 to 20 years in a depleted reservoir. Except for thermal energy extraction, a total of 6100 tons of CO2 can be sequestrated underground by using the CO2 as the working fluids in the five spot well pattern. The numerical investigation in this study indicate that huge energy extraction potential and CO2 storage can be achieved at the full field scale. Utilization of subsurface thermal energy after the ISC operation is a beneficial choice to offset the operating costs, reduce the CO2 emission and extend the economic life of reservoir
Distance Transform-Based Spatiotemporal Model for Approximating Missing NDVI from Satellite Data
No full textA common approach for evaluating vegetation growth using satellite imagery is the Normalized Difference Vegetation Index (NDVI), an important index for monitoring vegetation dynamics. NDVI exhibits variation both spatially and temporally, which is crucial for examining vegetation health and development trends over time. High-resolution, cloud-free satellite imagery, especially from open-access platforms such as Sentinel, is optimal for this purpose. Yet, these images are not always available because of cloud cover and shadow interference. To overcome this challenge, we present a model that combines the temporal and spatial aspects of the data to approximate the affected regions. In this method, we independently approximate NDVI using spatial and temporal components of the time-varying satellite data. Spatial approximation is expected to be more accurate near the boundary of the missing area, whereas temporal approxi-mation is more reliable farther from the boundary. Accordingly, we develop a model that employs the distance transform to merge these two approaches into one unified, weighted framework, outperforming each individually. We propose a novel decay function to manage this blending process. We evaluate our spatiotemporal model for NDVI approximation on 16 agricultural fields in Western Canada spanning 2018 to 2023. We empirically determine the op-timal parameters for the decay function and the distance-transform model. The findings indicate substantial enhancements over standalone spatial or temporal estimations. Additionally, our model shows considerable gains over simple combinations, Spatiotemporal Kriging and ConvLSTM methods. We also demonstrate the effectiveness of our spatiotemporal model in a case study on finding the peak green day for multiple fields. Lastly, we provide a detailed overview of the C++ software platform and algorithmic implementation devel-oped to facilitate this research
Effects of aerobic exercise and sleep on older adults’ proteomic profile, and cerebrovascular health
No full textFor the first time in history, older adults above the age of 65 years are projected to outnumber those under the age of 18. Aging is the primary risk factor for cardiovascular disease, neurodegenerative disorders, and obstructive sleep apnea (OSA), the most common sleep-related breathing disorder. A better understanding of relationships between lifestyle interventions such as sleep and exercise, and brain and blood biomarkers of health might indicate how changes in physical health can be beneficial to maintain and/or improve cerebrovascular health with aging. This thesis examined the effects of a six-month aerobic exercise intervention called the Brain in Motion Study (BIM) on plasma proteomics, and cerebrovascular health in community dwelling older adults. In the first study, we identified a protein panel signature in 12 female, and 12 male BIM I participants over 5-years, that correlates to longitudinal changes in cognition and sleep. The second study is a cross-sectional investigation of the relationships between sleep spindles, sleep disordered breathing indices of obstructive sleep apnea (OSA), and brain structure, using MRI, in 71 BIM participants. Finally, we investigated sex-specific effects of aerobic exercise on cerebral perfusion of the hippocampus, insula, and left pars triangularis, brain regions involved in verbal fluency, in 27 BIM I participants. These studies highlight the importance of reducing inflammation with aging, potentially through improving sleep and cardiorespiratory fitness. While OSA is prevalent in community dwelling older adults, cerebral perfusion may be protected in untreated OSA. We found that thalamic volume was positively associated with spindle frequency, suggesting that there may be a structural component to sleep architecture. In terms of implementing aerobic exercise as an intervention to improve cerebrovascular health, sex differences should be considered
DNA methylation analysis of NOTCH1 variants reveals the first episignature for non-syndromic congenital heart defects
No full textAbstract Background Congenital heart defects (CHDs) are the most common malformation amongst newborns, with a prevalence of approximately 0.8–2%. The etiology of CHD is highly complex and can be linked to genetic and nongenetic factors. The molecular basis remains partially unclear, and only a minority of patients can be assigned to clear monogenic causes. Methods Here we analyzed a cohort of 3907 CHD cases and population-matched controls using exome sequencing. In addition, we employed epigenetic profiling on a subset of cases that harbored rare NOTCH1 variants. Results We identified 24 pathogenic or likely pathogenic single nucleotide variants (SNVs) in NOTCH1 in our exome cohort, as well as a further 15 variants of uncertain significance (VUS) likely to have a deleterious effect. Although the cardiac phenotypes showed some heterogeneity, non-syndromic Tetralogy of Fallot (ToF) and related malformations were the most frequent finding in 56% (22/39). In particular, missense variants altering cysteine residues involved in forming disulfide bridges were identified, specifically in TOF patients. Altogether, NOTCH1-haploinsufficiency represented the most common monogenic cause in our cohort and accounted for an estimated 1% of CHD cases. Combined with additional cases assembled through collaborations, we present 67 individuals with ultrarare variants affecting NOTCH1. This prominent role of NOTCH1 calls for an accurate and accessible evaluation of variants. To this end we explored DNA methylation testing and successfully established a NOTCH1-specific episignature. This signature also displays a robust specificity in relation to 99 other episignatures. Taken together, we found that truncating, splice-altering, as well as missense NOTCH1 variants, can generate a distinct DNAm episignature. Conclusions We identified that NOTCH1-haploinsufficiency variants represented the most common monogenic cause in our cohort and accounted for an estimated 1 % of CHD cases. Furthermore, we conclude that methylation profiling can contribute to (NOTCH1) variant interpretation and improve the diagnostic management of CHD patients. Lastly, we established a NOTCH1-specific episignature, which represents the first non-syndromic signature, significantly extending the scope of patients that can benefit from methylation analysis
Characteristics of Sulfolane as a Novel Stationary Phase in Supercritical Fluid Chromatography
No full textSulfolane (C4H8SO2) is a small, polar, aprotic, cyclic molecule largely used as a solvent in oil and gas production for its interesting selectivity towards aromatic compounds. Here, sulfolane is examined as a novel stationary phase for use in capillary column supercritical fluid chromatography with flame ionization detection (SFC-FID). A 2 m x 250 µm stainless steel capillary was found to be optimal for retaining the sulfolane phase and achieving practical retention times for most analytes. Under typical conditions of 65 oC and 120 atm, the dynamic solubility of sulfolane in the supercritical CO2 mobile phase was measured to be only 0.6 g/L. This translated into minimal system detector noise for various mobile phase pressure and temperature settings that produced a moderate CO2 density of 0.4 g/mL or lower. In this way, the phase normally provided good performance and very little background interference in the FID over several hours of operation. Due to its thickness (~ 4 µm) the phase produced plate heights about 10-fold larger than optimal capillary SFC performance, but with an elevated sample capacity near 50 µg of injected analyte. Good retention and peak shape were obtained for various polar and non-polar analytes. Conversely, organic acids and bases yielded poor peak shape and low recovery on the column. Adding water to the sulfolane coating produced significant changes in analyte selectivity. Compared to a longer conventional SFC column, the relatively short sulfolane column demonstrated high selectivity towards aromatic analytes over saturates in the analysis of a gasoline sample. Results indicate that sulfolane might be a potentially useful alternative stationary phase for the selective SFC analysis of aromatics in various petroleum samples.Natural Sciences and Engineering Research Council (NSERC
Impact of Genetics on Brain Aging: Identifying Associations with Brain Age Gap Estimation using Neuroimaging, Lifestyle Factors, and Genome Data from the UK Biobank
No full textPopulation aging is associated with an increased prevalence of neurodegenerative conditions, highlighting the need for biomarkers that capture individual vulnerability to brain aging. Brain Age Gap Estimation (BrainAGE), defined as the difference between predicted brain age derived from neuroimaging features and chronological age, provides an indicator of biological aging. This thesis used multimodal magnetic resonance imaging (MRI), genetic, and lifestyle data from 40,920 neurologically healthy adults in the UK Biobank to investigate genetic, behavioural, and sex-related influences on deviations from normative brain aging. The first aim developed BrainAGE models using structural imaging, diffusion-weighted imaging, and resting-state functional MRI features. Diffusion-based BrainAGE demonstrated the strongest predictive performance, with the splenium of the corpus callosum model achieving an R2 of 0.96 and a mean abso-lute error of 1.2 years, highlighting the sensitivity of white matter microstructure to aging. Genome-wide association analyses across seven BrainAGE phenotypes identified variants enriched for pathways related to neuronal development, extracellular matrix organization, immune and inflammatory processes, metabolism, and neurodegeneration. These findings support BrainAGE as a genetically informative biomarker reflecting multiple biological systems involved in aging. The second aim examined the contribution of modifiable lifestyle factors to brain aging. More frequent alcohol consumption was consistently associated with older-appearing brains across most structural and microstructural measures. Sleep duration exhibited a U-shaped relationship, with both short and long sleep associated with accelerated aging. Moderate physical activity showed minimal associations after correction for multiple testing. Gene-by-environment analyses revealed only suggestive effects, indicating that lifestyle modulation of genetic risk may be subtle or require more precise exposure measurement or longitudinal data for detection. The third aim evaluated sex differences in brain aging and genetic architecture. Males showed signifi-cantly higher BrainAGE values overall, particularly for lateral ventricular volume. Sex-stratified analyses revealed distinct genetic influences in females and males, suggesting partially divergent biological mechanisms underlying neural aging. Overall, this work establishes multimodal BrainAGE as a biologically meaningful marker influenced by genetic predisposition, lifestyle factors, and sex-specific effects, supporting its potential utility for precision medicine approaches to maintaining brain health across the lifespan
The Effects of Financial Incentives and Clinical Practice Guidelines on the Adoption of Virtual Care
No full textThis quantitative dominant mixed methods study examined the effect of financial incentives and clinical practice guidelines on the adoption of virtual care during the COVID-19 pandemic. A quantitative analysis of physician billing data was conducted, supplemented by key informant qualitative interviews which were used to inform analytic decisions and interpret the study findings. Using Practitioner Claims data from Alberta, Canada, a sample of 105,222 physician claims relating to hepatology practice were analyzed using generalized linear models with cluster-robust standard errors over three study periods: pre-pandemic, intra-pandemic and late-pandemic. The quantitative findings indicated that virtual care adoption differed substantially by study period, with remarkably higher use during the intra-pandemic period and sustained use above pre-pandemic levels in the late-pandemic period. Financial incentives were associated with slightly higher odds of virtual care use; however these effects were limited in shaping virtual care patterns. Financial incentives were not found to moderate the relationship between clinical practice guidelines and virtual care adoption. Qualitative insights suggested that clinical practice guidelines legitimized the adoption of virtual care modalities during the pandemic although guideline effects could not be independently isolated from broader pandemic-related policy and system-level shifts. Drawing from business model theory, the study situated financial incentives and clinical practice guidelines as business model components that influenced physician practice within the broader institutional and policy context. The study findings suggest that pandemic-related policy and system-level shifts were the dominant catalysts for the extensive adoption of virtual care, outweighing the influence of financial incentives alone. While virtual care adoption declined between the intra-pandemic and late-pandemic periods, sustained adoption was demonstrated in the late-pandemic period suggesting an emerging shift in clinical practice patterns. However, the durability and generalizability of this change is yet to be established. Recognizing the complex regulatory, institutional, and professional environments in which clinical practice occurs, and drawing from business model theory, this study demonstrated how two foundational and complementary business model mechanisms influenced virtual care adoption and highlighted the critical import of rethinking business models in response to changes in the healthcare business environment. The advancement of virtual care as a form of digital health innovation is a healthcare challenge that demands the design and governance of an organizational capability. This study makes several contributions including demonstrating that value capture mechanisms and legitimacy signals create a context for influencing virtual care adoption and providing evidence of the sustained increase in virtual care adoption in the late pandemic period suggesting an emerging shift in clinical practice