VictorJuiz/thesis huggingface dataset:
Reifying 'Vision 2040': a Conceptual Framework for Community-Driven Development and Participatory Local Governance in The Bahamas

The ossification of inequitable power dynamics in liberal democracies often leads to the general populace receiving an abating share of decision-making authority. In many instances, this is innately true for democracies that inherited systematised conduits of subjugation and inequality from colonial ontology. For these post-colonial environments, the vestiges of colonial governance maintain limited avenues to public influence in crucial decision-making, while environmental factors like demographic growth exacerbate pre-existing constraints. In The Bahamas, the development of Vision 2040: The National Development Plan of The Bahamas presents an opportunity to mitigate the challenges that have arisen from this policy environment through a comprehensive policy framework and reform agenda guided by broad consultative engagement, interdisciplinary research, and extensive analysis. Leveraging the space for further research provided by ‘Vision 2040,’ this thesis seeks to delineate a two-part conceptualisation that outlines a pathway for socio-political and socio-economic development that may yield community ownership, control and influence over their resources and governance systems.

Keywords: Caribbean Studies
Community Development
Local Government
Participatory Governance
Public Policy
Public Sector Reform
Advisor: Vieta, Marcelo A
Resilience is associated with cortical gray matter of the antinociceptive pathway in people with chronic pain

Resilience is a personal characteristic that influences health. This thesis examined whether resilience was related to cortical gray matter in 4 regions of the antinociceptive pathway: rostral and subgenual anterior cingulate cortex (rACC, sgACC), anterior insula (aINS), and dorsolateral prefrontal cortex (dlPFC). Gray matter volume (GMV) and cortical thickness wereextracted from T1-weighted MRI images of 88 people with chronic pain and 86 sex- and age-matched healthy controls, who completed The Resilience Scale and Brief Pain Inventory. Resilience was found to be negatively correlated with average pain and positively correlated with GMV in the bilateral rACC, sgACC, and left dlPFC in people with chronic pain. Mediation analyses revealed that GMV in the right rACC and left sgACC together partially mediated the relationship between resilience and average pain. These findings suggest that the antinociceptive pathway may play a role in the impact of resilience on one’s ability to modulate chronic pain.

Keywords: ACC
chronic pain
dlPFC
gray matter
MRI
resilience
Advisor: Davis, Karen D
Neuro-Regeneration of Retinal Ganglion Cells on Bio-Activated Silk Based Scaffolds

Retinal ganglion cell (RGC) axons form the optic nerve, connecting the retina to the visual areas of the brain. Any injury to the optic nerve leads to RGC death and ultimately blindness as RGC axons are unable to regenerate. One of the main challenges in regenerative neuroscience is to identify ways to stimulate neuronal survival and regrowth. We seek to develop an in vitro method using biomaterial and 3D bioprinting to enhance RGC survival and support axon regrowth and directionality. I engineer bio-platforms containing aligned silk nanofibers on which we culture RGCs. These silk nanofibers are bioactivated by adding trophic factors which will be entrapped in the fibers. Growing RGCs on these bioactivated silk nanofibers enhanced neuronal survival and axon regrowth.This technology will ensure fast, reproducible, and accurate generation of bioactivated silk nanofibers scaffolds that can be used to investigate the mechanisms of neuro-regeneration in vitro and in vivo.

Keywords: 3D Printing
Electrospinning
Regeneration
Retinal Ganglion Cells
Silk Fibroin
Vision
Advisor: Reber, Michael
Material Screening for Solid State Electrolyte Applications through Machine Learning and Ab-initio Molecular Dynamic Calculations

Solid state electrolyte batteries provide a noticeable advantage over conventional liquid batteries due to improved safety and their potential for increased energy storage over current batteries. For this thesis, two types of calculations are undertaken: first, a machine learning neural network has been used to predict optimal systems; second, first principle Ab-Initio Molecular Dynamic (AIMD) calculations have been performed to predict the performance of the systems previously identified. Machine learning was implemented using the Tensorflow library, where a variety of neural networks were optimized and tested in the categorization and discovery of promising solid state electrolytes. AIMD was implemented using the Vienna Ab-Initio Software Package (VASP), where promising materials systems were simulated and then had ionic conductivities determined from these simulation results. The AIMD portion of this study focused on the Argyrodite system class, which was found during literature review and through the use of the preceding machine learning calculations.

Keywords: Argyrodite
Machine Learning
Molecular Dynamics
Solid State Electrolytes
Advisor: Veer Singh, Chandra
The Cognitive Process of Affordance Recognition: Three Iterative Experiments Comparing Affordances to Alternate Uses

Fostering creativity in engineering can promote innovative solutions. Strategies to generate many solutions are also beneficial to solving the complex problems engineers face. Borrowing from engineering and psychology, this thesis developed two tasks to compare affordances and alternate uses. This was followed by an exploration of how each can contribute to creative problem-solving on subsequent tasks. Experiment 1 hints at the cognitive demands of identifying alternate uses. Experiment 2 confirms the critical role of physical properties in affordance recognition. Experiment 3 reveals an interesting relationship between functions, alternate uses, and performance on physical tasks requiring creative solutions. Taken together, these support the strategies used in engineering concept generation. Moreover, they highlight the vital role that divergent thinking plays in creativity.

Keywords: affordances
AUT
creativity
divergent
Advisor: Shu, LH
The Role of the Kidney as an Oxygen Sensor that Initiates Integrative Cardiovascular Responses to Preserve Brain Perfusion during Acute Anemia

The mechanism by which the kidney functions as an oxygen sensor during anemia and its role in regulating integrative physiological responses to anemia is not completely characterized. We hypothesize that the kidney contributes to the sensing of arterial oxygen content (CaO2) and regulates cardiovascular responses to maintain brain perfusion during anemia. Methodology to accurately measure kidney and brain microvascular pO2 was established. Our data demonstrate that the kidney is an important oxygen sensor based on measurements which demonstrate a direct relationship between CaO2 and renal microvascular pO2 (PkO2 = 0.30 × CaO2 + 6.9,r2 = 0.75). Removal of this oxygen sensing organ (bilateral nephrectomy) resulted in reduced baseline brain pO2 and an even more severe reduction during anemia. Measurements of reduced cardiac output in anemic nephrectomised animals suggest that increased venous capacitance and reduced systolic function contributed to the reduced cardiac response and associated brain hypoxia during anemia. These data support the hypothesis that the kidney is an important oxygen sensor which signals increased cardiac output to maintain brain perfusion during acute anemia.

Advisor: Hare, Gregory MT
Mazer, C David
Incorporating Clinical Access Limitations in Automated Treatment Planning for Photodynamic Therapy

Interstitial Photodynamic Therapy (iPDT) is a minimally invasive oncology treatment in the treatment of deeply-seated tumors. PDT-SPACE is an open-source tool that automates iPDT treatment planning by providing patient-specific light source placements to destroy a tumor while minimizing healthy tissue damage. This work extends PDT-SPACE in two ways. The first enhancement allows specification of clinical access constraints on source insertion to avoid critical structures and to minimize surgical complexity. Constraining fibre access to a single burr hole of adequate size preserves result quality. The second enhancement generates an initial placement of light sources as a starting point for refinement, rather than requiring entry by the clinician. This feature improves productivity and leads to solutions with an average of 4.5% less healthy tissue damage compared to manually generated placements. The two features are used to perform in silico comparisons of various surgical options for virtual glioblastoma multiforme brain tumors.

Keywords: Computer-aided Designs
Optimization
Photodynamic Therapy
Treatment Planning
Advisor: Betz, Vaughn
Distributed Privacy-Preserving Machine Learning via Homomorphic Encryption

Privacy-preserving machine learning enables training ML models on sensitive data while preserving individual data privacy using advanced cryptographic techniques. Some of the key techniques used in privacy-preserving machine learning include secure multiparty computation, homomorphic encryption, and differential privacy. In this thesis, we propose two protocols: a secure neural network inference protocol and a secure vertical federated learning protocol. In the first work, SECO, we extend prior work on secure inference to a multi-server hierarchy, allowing multiple server nodes to collaborate with a user to compute predictions without compromising data privacy. We use multiparty homomorphic encryption and garbled circuits to protect the model structure and evaluate SECO on MiniONN and ResNet32 models. In the second work, we analyze leakage in FedV using multi-input functional encryption and suggest using Quadratic functional encryption to avoid information leakage in training generalized linear models for vertical federated learning.

Keywords: Functional Encryption
Homomorphic Encryption
Privacy-preserving Machine Learning
Secure Inference
Vertical Federated Learning
Advisor: Khisti, Ashish
Deep Generative Machine Learning to Create Synthetic 2D Ultrasound Images for Rare Disease Research

Research on rare diseases is often not considered by the artificial intelligence (AI) field due to data scarcity. Hemophilia is a rare genetic bleeding disorder that affects blood clotting. Hemophiliacs often suffer from bleeding into the joint space (hemarthrosis) that causes pain and ultimately damage. Ultrasound imaging is used for non-invasive hemarthrosis assessment. Early detection using AI is key to improve treatment efficacy and access to care. This thesis proposes a novel generation framework that includes domain knowledge from an external dataset, decreasing missing information in rare cases. By dividing the problem in content and context generation, our enriched synthetic ultrasound showed no significant differences from real data; improved model performance, especially at smaller sample sizes; outperformed a previously accepted model in synthetic quality; and provided a robust model for hemarthrosis detection. Our framework facilitates AI research in the study of rare diseases and the prototyping of AI-driven clinical solutions.

Keywords: Artificial simulation
GAN
Generative models
Hemarthrosis
Hemophilia
Ultrasound
Advisor: Tyrrell, Pascal N.
Behavioural Response as a Function of Temperature and Interactions between Behaviour and Melanin Ornaments in Dragonflies

Sexual selection creates and maintains elaborate phenotypes despite costs imposed by natural selection. This creates a trade-off between natural and sexual selection, whereby organisms can display traits that decrease survivorship if they sufficiently improve reproductive success. In many species of odonates males possess ornaments, formed by melanin, which increase their mating success, but also increase body temperature, potentially imposing novel trade-offs in a warming climate. Using a combination of comparative and experimental methods, I examined the effects of temperature on mating and thermoregulatory behaviour in Celithemis elisa and Leucorrhinia intacta dragonflies. In C. elisa, I found evidence for this trade-off with positive associations between temperature and time spent thermoregulating and a negative association with time spent mating. In L. intacta, only thermoregulatory behaviour was positively associated with temperature. My results suggest that some dragonflies will have reduced time windows for mating as climate change continues.

Keywords: behaviour
climate change
melanin
natural selection
sexual selection
trade-off
Advisor: McCauley, Shannon J
Murray, Rosalind R
Discourse Analysis across Three Different Professional Contexts to Inform Knowledge Building Professional Development

This thesis explored teacher discourse across three settings: a professional learning community, a professional learning network, and a global innovation network. In each case, the goal was greater understanding and advancement of Knowledge Building in the classroom. Inductive coding was used to analyze discourse within and across settings. Discourse forms identified were Connecting, Problematizing, Questioning, Self-Reflection, Idea Exploration, Inferencing, and Off-topic. Subsequent analyses explored deeper levels of engagement within these categories, strength of connections between codes, and descriptive accounts of types of interaction. No setting stood out as more engaged in principles-based design-mode discourse aimed at Knowledge Building improvements in practice, but the global innovation network showed the greatest variety of discourse moves, supports, and conditions for increased potential for Knowledge Building professional development The thesis ends with a discussion of future research and development to enhance work across different settings.

Advisor: Scardamalia, Marlene M.S.
Numerical Modeling, Optimization and Simulation of Phase-Change Material Thermal Management Systems for Electric Vehicles

Battery thermal management systems (BTMS) are vital for the safe operation of electric vehicles (EVs) through cost-effective battery heat dissipation and thermal control strategies. Numerical modeling, optimization, and simulation of BTMS is essential for understanding and analysing thermal phenomena which can be difficult to quantify through experiments. This thesis first presents a comprehensive review of numerical modeling techniques at each level of the EV hierarchy from nanoscale to vehicle, emphasizing novel thermal modeling approaches. Subsequently, the thesis presents a design optimization framework for BTMS using phase-change materials for passive thermal management. The framework is illustrated through a case study that considers fast charging of a module containing four lithium-nickel-cobalt-aluminum-oxide cylindrical cells electrically connected in a 4-series-1-parallel configuration. Through the case study, the cooling performance of common encapsulant materials for passive BTMS is compared and assessed.

Advisor: Amon, Cristina H
Acellular EVLP Perfusate Silences Pro-Inflammatory Signaling in Human Lung Endothelial and Epithelial Cells

Lung ischemia-reperfusion (IR) injury still stands as a major post-transplant complication and remains to be fully addressed. Fortunately, ex vivo lung perfusion (EVLP) has emerged as an innovative platform to assess and repair marginal donor lungs prior to lung transplantation with promising clinical outcomes. However, the exact mechanisms of IR injury and EVLP are unclear. Therefore, we conducted a bulk RNA-sequencing on human lung endothelial (HPMEC) and epithelial (BEAS-2B) cell culture models that simulate IR and EVLP to elucidate cell-type-specific responses. After pathway enrichment analysis, we found that IR induced pro-inflammatory signaling in both cell types. Interestingly, our EVLP simulation with acellular Steen solution exhibited a disappearance of inflammatory signaling in endothelial and epithelial cells. Our findings may provide basic molecular explanations for why normothermic EVLP with acellular solution has been yielding better lung conditions than directly reperfusing the lungs with warm recipient blood with serum components.

Keywords: Ex Vivo Lung Perfusion
Ischemia-Reperfusion Injury
Lung Transplantation
Machine Perfusion
Organ Preservation
Transcriptomics
Advisor: Liu, Mingyao
Impact of Multi-level External Factors on Role Identities of Community Pharmacy Owners/Managers and Service Provision in Ontario, Canada

This qualitative paper aims to explore how multi-level external factors influence the role identity of pharmacy owners/managers and pharmacy service provision. Data were collected from 10 semi-structured interviews and 14 online questionnaires from pharmacy owners/managers at community pharmacies in Ontario, Canada, and were analyzed through thematic analysis. In addition, documents were reviewed to explore the pharmacy field in Ontario. Ten role identities have been found, each showing different degrees of adherence to four institutions: profession, corporation, market, and state. Participants had to negotiate between competing demands created by the four institutions, which were likely to have arisen from a mismatch between policies and the system in the pharmacy field regarding how pharmacy services are understood, financed, and delivered. The findings suggest the importance of understanding healthcare professionals as being socially embedded and creating a coherent system that can facilitate professionals in achieving policy goals and professional ideals.

Keywords: Canada
chain pharmacy
community pharmacy
expanded scope of practice
pharmacy management
Advisor: Andiappan, Meena
Efficient Data Streaming for a Tightly-Coupled CGRA

We propose a Data Streaming Unit (DSU) for a CGRA that tightly couples to its host CPU. The DSU operates in the context of the T-CGRA accelerator allowing the CGRA to access memory at the granularity of cache lines. It streams data from memory to CGRA cells and conversely. The DSU supports direct and indirect access patterns as well as dynamic memory accesses. Further, the DSU exploits cache line reuse to feed (collect) data on the same cache line to (from) multiple CGRA cells, reducing the number of memory transactions. We implement a prototype DSU on an FPGA that is connected to external memory. The evaluation shows that ideal throughput is achieved for common direct accesses and indirect ones when a sparsity structure is present. Thus, we conclude that direct data streaming between memory and CGRA cells is effective and enables previously unsupported memory access patterns on CGRAs.

Keywords: access pattern
CGRA
coarse-grained reconfigurable arrays
data reuse
data streaming
tight coupling
Advisor: Abdelrahman, Tarek S
Novel Ni-V-Y Electrocatalysts for the Hydrogen Evolution Reaction

Anion exchange membrane water electrolysers (AEMWEs) convert renewable energy into fuel in the form of hydrogen gas. Novel electrocatalysts are required to be active, stable, and economic for AEMWEs to become commercially viable.A novel amorphous Ni-V-Y system was successfully synthesized along with Ni, Ni5Y and Ni3V intermetallic phases in micron and nanosized powders to serve as a baseline measure of performance. Thermodynamic modelling was used to develop the amorphous alloy system, which was then produced in a two-step ball milling process. Elemental powders were mechanically alloyed and amorphized under cryogenic conditions, then size-reduced to nanoparticles via surfactant assisted high energy ball milling. Using XRD, Ni3V and Ni5Y phases were identified in the Ni80V10Y10 alloy, and SEM was used to confirm nanoparticle sizes. The electrochemical performance of a Ni80V10Y10 electrocatalysts was superior compared to pure Ni, Ni3V, and Ni5Y due to spillover effects in the multiphase Ni80V10Y10 material. The surfactant was not completely removed leading to lower-than-expected activity of the nanoparticles.

Keywords: electrocatalysis
electrochemistry
electrolyser
hydrogen
Advisor: Thorpe, Steven J
An Observational Study of System Factors Affecting the Use of the Surgical Safety Checklist in General Surgery

Numerous studies report mixed results on the effectiveness of the Surgical Safety Checklist (SSC) to reduce patient deaths and complications. Yet, few studies have assessed how the SSC is used by the surgical team and the system factors that impact its use. Data from 74 general surgery procedures were captured using the OR Black Box (audio/video recording system) or direct observations. Framework analysis was used to code system factors (i.e., Person(s), Tools, Tasks, Environment, and Organization) associated with checklist use. Percent completion of individual checklist items ranged from 3.4% to 100%. Descriptive assessments of checklist use during each pause point (i.e., Briefing, Timeout, Debriefing) showed variable team member presence and leadership, frequent multi-tasking by team members, and lack of use of the physical copy of the checklist, all of which were associated with different system factors. Changes to system factors are required to yield improved and consistent checklist use.

Keywords: black box
healthcare
operating room
surgery
surgical safety checklist
system factors
Advisor: Trbovich, Patricia
Deep fMRI Encoding Models of Human Vision

In this work, we analyze artificial intelligence models that predict fMRI-measured brain activity in response to visual stimuli (ie. encoders). First, we introduce capsule encoders, the first alternative to convolutional neural networks (CNNs) for deep image-fMRI encoding. We show that although capsule networks are not yet effective for complex naturalistic images, with simpler visual stimuli, they improve upon existing CNN encoders. We also introduce new metrics and visualizations that provide a more complete picture of how encoders behave when able or unable to predict voxel activity. With this, we separate these behaviors using a novel thresholding approach and show it is more robust than p-value significance. We also find that encoders may be able to surpass the noise ceiling, revealing how the SNR of the validation set may be limiting our ability to evaluate encoders and highlighting a trade-off between the number of unique and repeated samples.

Keywords: Computational Neuroscience
Computer Vision
Encoder
fMRI
Machine Learning
Visual Pathway
Advisor: Uludag, Kamil
Multi-FPGA Application Partitioning and Infrastructure

Field Programmable Gate Arrays (FPGAs) have increasingly been deployed in data centers. As a result, there has been much focus on tools that provide developers with straightforward and efficient abstractions. As state-of-the-art FPGAs integrate multiple dies on a single device to provide abundant hardware resources, designing an efficient shell to abstract these FPGAs in data centers seems crucial. In this thesis, we extend the Galapagos project and propose a multi-die shell. The shell abstracts the cross-die interconnects and handles the communication of the FPGA with other devices. Additionally, we introduce an automated simulated annealing-based partitioner to map the input application kernels to multiple FPGAs, considering different objectives and constraints. Finally, we evaluate the shell and the partitioner using two micro-benchmarks and demonstrate them implementing a real-world case study, a Transformer model.

Advisor: Chow, Paul P