VictorJuiz/thesis huggingface dataset:
Soft Electronic Materials Inspired by Biologic Derivatives for use in Neural Regenerative Applications

Regenerative medicine is an interdisciplinary field of medicine that develops methodologies to replace damaged or diseased cells. The recent understanding that neural precursor cell (NPC) behaviour is controlled by signaling from their micro-environment has been at the forefront of development for neurological therapies. The application of electric fields has been shown to promote NPC migration and electrodes have been the most used channel by which brain signals have been investigated and manipulated for therapeutic purposes. A challenge facing neural interfacing therapeutics is seamlessly incorporating electrodes that are typically made of rigid and stiff materials with the soft and dynamic nature of the brain where they would be situated. By minimizing this mechanical mismatch, the aim is to attenuate the host’s biological inflammatory response. In this work hydrogel-based materials are explored as potential alternatives to the existing less biocompatible counterparts with the goal of promoting brain repair following injury or disease.

Keywords: alginate
hydrogels
microglia
neuroinflammation
regenerative medicine
Advisor: Naguib, Hani E
Morshead, Cindi M.
Predictive Quality Assessment for Fused Filament Fabrication

Additive manufacturing is an emerging class of manufacturing technologies which are particularly suitable for producing complex and custom parts. Fused filament fabrication is a common type of additive manufacturing for producing thermoplastic-based parts. The aim of this study is to support future work towards closed-loop control for fused filament fabrication by integrating and extending in-situ monitoring and process understanding. First, a dataset designed to be useful for closed-loop control is proposed. This dataset features 359 tensile specimens fabricated with in-situ images and tensile property measurements. Based on this dataset, machine learning and computer vision methods were applied to develop a model which can navigate the complex relationships between process parameters, in-situ observations, and strength of the final product. Several variants of the model were proposed and their relative effectiveness was studied.

Keywords: Additive manufacturing
Computer vision
Machine learning
Advisor: Lee, Chi-Guhn
Priming the Primary Motor Cortex with Transcranial Direct Current Stimulation: Effect on Learning the Golf Putt

A history of low post-synaptic activity increases the threshold for long-term potentiation, while a history of high post-synaptic activity decreases this threshold. Transcranial direct current stimulation (tDCS) applied to the motor cortex prior to motor practice (“priming”) can modulate the history of a neuron's post-synaptic activity. Here, we investigated the effects of tDCS priming on performance on a golf putting task. Participants received anodal (n=12), cathodal (n=12), or sham (n=12) tDCS prior to practicing putting on two consecutive days. Performance was measured using radial error, defined as the shortest distance from the ball to the target. After accounting for baseline performance, there was a main effect of group [p = 0.07]. The cathodal group had a significantly lower radial error than the anodal group [p = 0.02] and the sham group [p = 0.01]. Cathodal priming enhanced retention. Overall, this study demonstrated that priming with tDCS can impact motor learning.

Advisor: Chen, Joyce L
Factors Affecting Orphans and Vulnerable Children Realizing their Right to Education in Mozambique

Despite notable efforts from countries and non-governmental organizations (NGOs) around the world, the right to education among orphans and vulnerable children (OVCs) in Sub-Saharan Africa is far from realized. Despite an increase in the number of girls in school over the past few decades, we cannot overlook the failures and growing disparities faced by the poorest and most vulnerable children. OVCs continue to suffer and face unfair educational access and opportunities despite initiatives by governments, development agencies, international organizations, NGOs, Education for All, Millennium Development Goals, and their successive Sustainable Development Goals. NGOs play a significant role in the educator sector of most African countries. This thesis focuses on the local response of an NGO in Mozambique and seeks to answer: What factors affect (hinder or enhance) OVCs realizing their right to education with a special focus on the female OVCs among them?

Keywords: child rights
education
Mozambique
NGOs
orphans and vulnerable children
Sub-Saharan Africa
Advisor: Cooper, Karyn
Manion, Caroline
A Speculative Hardware Scheduler Supporting Priority Updates

Many important algorithms schedule their tasks according to a priority order. While parallel priority schedulerscommonly implement task enqueue and dequeueMin operations, some algorithms need a priority update operation that alters the scheduling metadata for a task, which is not supported by existing hardware. We present Hive, a task-based multicore architecture that extracts fine-grained parallelism from algorithms with priority updates. Like prior hardware systems for ordered parallelism, Hive uses data- and control-dependence speculation and a large speculative window to execute tasks in parallel and out of order. Hive supports updates by identifying the scheduler-carried dependence, and speculating on it with task versioning, distinct from data versioning. Hive enables safe speculative updates to the schedule to better utilize speculation tracking resources and uncover more parallelism. Across nine benchmarks, Hive improves performance at 256 cores by up to 2.8x over the next best hardware solution.

Keywords: Accelerators
Harware-Software Codesign
Irregular Parallelism
Multicores
Programming Models
Speculation
Advisor: Jeffrey, Mark C
Service Engagement in Virtually Delivered Psychosis Treatment: A Systematic Review and Mixed Methods Evaluation

Disengagement remains a significant dilemma within mental healthcare, and among conditions characterized by psychosis specifically. Traditionally delivered in-person, specialized psychosis services, including early psychosis intervention (EPI), have promptly transitioned to virtual delivery amid the COVID-19 pandemic, despite limited research on engagement in virtual psychosis care. This thesis consists of a systematic review and mixed methods evaluation; the latter examining rates, predictors, and experiences of (dis)engagement within a virtual EPI program. Electronic health records of a cohort of EPI patients enrolled between April-November 2020 were examined; individual interviews and focus group discussions were conducted with patients, family members, and clinicians. Approximately 15.5% of the sample formally disengaged by follow-up (15-24 months); 12.3% disengaged within the first 9 months. Disengagement was associated with lack of early use of SEE (HR=.28, 95% CI=.12-.67) and NEET (HR=3.04, 95% CI=1.03-8.98). Qualitative analysis revealed 5 salient themes and service recommendations, with most preferring a hybrid model.

Keywords: Early Psychosis Intervention
Mixed Methods Research
Psychosis
Service Engagement
Virtual Care
Advisor: Kozloff, Nicole
Stergiopoulos, Vicky
Reduced Order Numerical Modelling of Electric Commuter Vehicle Battery Thermal Management Systems

Numerical modelling techniques have been used to study the performance of thermal management solutions for electric vehicle applications. However, these numerical methods require specific knowledge of input parameters and need to be designed carefully to balance accuracy and computational effort before they can be applied to real-world scenarios. The work presented in this thesis follows the process of developing and applying a reduced-order numerical model of an electric vehicle battery pack under wide variety of battery architectures and real-world scenarios. It includes characterizing the thermal properties of Li-ion battery cells for the use of application-specific thermal models, developing a computationally efficient thermal model of an electric vehicle battery and thermal management system, and proposing improvements to an existing case-study vehicle. Changes to the pack architecture and thermal management system resulted in reductions of maximum temperature of 8% and improvements in temperature uniformity of over 66%.

Keywords: Battery Thermal Management
Electric Vehicle Thermal Management
Numerical Modeling
Reduced Order
Advisor: Amon, Cristina H
Investigating the Role of JAK2 in Intestinal Homeostasis and Disease

Intestinal epithelial cells (IECs) are critical in the maintenance of intestinal homeostasis. Janus kinase (JAK) and signal transducers and activators of transcription (STAT)-mediated signalling coordinates the cell-cell communication underpinning many homeostatic, inflammatory, and host-defensive responses. Little is known about the role of JAK2 in IECs. To investigate the role of IEC JAK2 in homeostasis, we generated mice deficient in Jak2 using a Cre-loxP system. These mice exhibited increased intestinal barrier permeability, altered gene expression of tight junction proteins, and increases in TH2 and TH17 cytokine gene expression. Further, these mice exhibited greater histological evidence of intestinal inflammation and when subjected to experimental colitis, lost more body weight than control mice. Overall, IEC Jak2 deletion resulted in mild inflammation and impairments to the intestinal barrier, indicating IEC Jak2 may promote intestinal homeostasis.

Keywords: Gastrointestinal System
Intestinal Epithelial Cell
Janus Kinases
Advisor: Woo, Minna
Drug Response Prediction and Biomarker Discovery using Multimodal Deep Learning

Patients with the same cancer type respond differently to the same drugs. This difference is partly due to genetic and other molecular variabilities among patients. Efforts in the pharmacogenomics field are underway to better understand the relationship between the patient’s genomic background and their response to therapy. Advances in molecular profiling approaches have paved the way for the systematic screening of drug effects in cancer cell lines. Numerous computational approaches have been proposed to address the sizeable combinatorial space of testing cell lines and drug pairs. Although deep learning algorithms outperform traditional methods, there are still many challenges in drug response prediction (DRP) that ultimately result in these models’ low generalizability, which hampers their clinical application. I have recognized and addressed some of the shortcomings of DRP methods by combining multiple cell line characterization data, addressing drug response data skewness, and improving chemical compound representation. The result is an open-source, Python-based, command-line program available at: https://github.com/LincolnSteinLab/DRP.

Keywords: Biomarker
Deep Learning
Drug Discovery
Drug Repurposing
Drug Response Prediction
Multimodal
Advisor: Stein, Lincoln D
A Flat Plate Heat Exchanger for Waste Heat Recovery

A novel flat-plate drain water heat exchanger was developed and tested to reduce the overall energy consumption of household and commercial water heaters by increasing the cold water supply temperature through heat exchange with waste greywater. Characterization of the DWHR prototype unit demonstrated an average effectiveness of 0.401 across the entire operating flowrate range and an increase in effectiveness of approximately 0.031 on the highest-performing comparable DWHR unit. A novel thermally-sprayed resistive sensor was fabricated and tested to detect the relative amount of fouling on heat exchanger surfaces by locally heating the fluid volume around the sensor and measuring the associated cooling rate in a natural convection environment. The sensor demonstrated the ability to detect a decrease in overall heat transfer coefficient with an increase in thermal conductive resistance, using a curve-fitting lumped body transient conduction model, over a thermal conductive resistance range of approximately 0.05 to 0.6 ?.?/?.

Keywords: Falling Film
Fouling
Heat Exchanger
Heat Transfer
Thermal Spray
Waste Heat Recovery
Advisor: Chandra, Sanjeev
Summer Climate Reconstruction using Tree-ring Blue Intensity in the Mackenzie Delta, Northwest Territories

This thesis uses tree rings from white spruce taken from the Mackenzie Delta to i) explore the potential to extend the region’s current tree-ring record by using subfossil trees buried under lake sediments, and ii) be the first study in the region to use the tree-ring parameter blue intensity.The sample set spanned 1105-2018, adding crucial sample replication prior to 1500 AD. The subfossil wood from lakes did not extend the chronology. However, it yielded samples that were over 800 years old, indicating that lakes in the region could potentially harbor ancient samples to target for future research. The blue intensity record was strongly correlated with May-September minimum temperatures (r=0.63), while also proving to be highly effective at capturing extreme interannual temperature variations caused by volcanic eruptions. Findings from the blue intensity reconstruction confirm that the parameter can be used as a robust summer temperature proxy in the region.

Keywords: Blue intensity
Dendrochronology
Paleoclimatology
Tree ring
White spruce
Advisor: Porter, Trevor J.
Dynamic Modelling of Process Chemistry in Kraft Pulp Mills

Efficiency decline in the recovery cycle of Kraft pulp and paper mills can create dynamic conditions that lead to evaporator scaling and poor combustion properties at the recovery boiler. In extreme cases, these dynamic conditions can evolve into production disruptions and mill shutdowns. Currently, operational strategies are often evaluated with steady-state models without the capability to simulate dynamics, leading to misinformed operational decisions. Dynamic modelling offers the opportunity to provide accurate real-time information to operators to pre-emptively manage dynamic conditions before shutdowns are required. To this end, this research project developed a dynamic model of a Canadian softwood/hardwood pulp mill using CADSIM Plus with model calibration from a mill-wide sampling campaign. Time and impact estimates of common dynamic conditions were determined to inform and improve existing operational strategies. Further, linking the model to mill measurements would equip operators with real-time chemistry measurement leading to optimized process control.

Keywords: CADSIM Plus
Causticity
Dynamic Modelling
Grade Transition
Kraft Pulp Mills
Reduction Efficiency
Advisor: DeMartini, Nikolai
Evaluating Control-flow Integrity with Syscall Reachability Analysis

In order to defend against memory exploits, control-flow integrity (CFI) has been widely researched as a defence and seen significant industry adoption. While attacks have demonstrated its limitations in preventing control-flow hijacking, we argue that CFI can still prevent end-to-end exploits by limiting an attacker's access to the syscall interface. We design and implement a specialization of static taint analysis called syscall reachability analysis to over-approximate the set of syscall gadgets available to the attacker under different CFI policies, which we propose as a quantitative upper bound on exploitability. Evaluating over a set of representative C/C++ programs, We find that most programs do not make very many sensitive syscalls and that in most of the remaining cases examined, the harm can be mitigated through sanitization of syscall arguments.

Keywords: Control-Flow Integrity
Memory Vulnerabilities
Program Analysis
Security
Taint Analysis
Advisor: Lie, David
Multi-stakeholder Perspectives on Acceptability of Pediatric Autism Therapies: A Mixed Methods Study

Exploring perspectives on acceptability of pediatric autism therapies (occupational therapy (OT, speech language pathology (SLP) services, applied behaviour analysis (ABA)-based therapies and physiotherapy (PT)) is critical to ensure services are appropriate and informed by lived experiences. This mixed-method survey study investigated multi-stakeholder (autistic people, parents of autistic children, clinicians) perspectives on acceptability, which reflects the extent to which people delivering or receiving a healthcare intervention consider it to be appropriate. The survey was created with stakeholder feedback. There were 157 completed responses, the majority of which were from clinicians. Results showed individuals across all stakeholder groups to be accepting of OT and SLP services, dependent on aspects of service delivery. Autistic and parent respondents were less accepting of ABA than clinicians, and shared ethical concerns about all therapies, with a focus on ABA. Further work is needed to understand what makes certain aspects of therapies (i.e., external reinforcement) unacceptable.

Keywords: autism intervention
autism spectrum disorder
autism therapies
perspectives
Advisor: Penner, Melanie
VRNNGAN:A Recurrent VAE-GAN Framework for Synthetic Time-Series

Time-series data generation is a machine learning task growing in popularity, and has been a mod- elling focus of deep generative methods. In this paper, we propose Variational Recurrent Neural Network GAN (VRNNGAN), a novel GAN framework for synthetic time-series generation. VRN- NGAN uses a recurrent Variational Autoencoder (VAE) as the generator and a bidirectional RNN as the discriminator. The recurrent VAE captures temporal dynamics into a learned time-varying latent space while the adversarial training encourages VRNNGAN to generate realistic time-series data. We evaluate VRNNGAN against other relevant deep generative methods on three datasets and perform four evaluations on the generated synthetic data.. We propose a novel metric for evaluating time-series GAN models using the time-step percentiles built from the generated synthetic data. We find that VRNNGAN achieves the best predictive score across all datasets and baseline methods and is competitive with the top-performing alternative in the majority of other experiments.

Keywords: Deep Learning
Machine Learning
Advisor: Andritsos, Periklis P.A
Maximizing Workspace Accessibility in Magnetic Actuation of Tethered Microsurgical Tools using Non-uniform Magnetic Fields

Magnetic microsurgical tools may be able to allow minimally invasive surgical techniques to be applied to neurosurgical procedures. However, typical magnetic actuation systems either cannot generate large enough magnetic fields, or surround the patient in a way that obstructs a neurosurgeon from taking over the surgery for certain surgical steps. In this research, we develop a magnetic actuation system with maximal workspace accessibility capable of generating non-uniform magnetic fields up to 38 mT in the x- and y- axes and 47 mT in the z-axis at a working distance of 12 cm, deep enough to deploy these surgical tools in the brain. We demonstrate the advantages of employing a non-uniform field control methodology over the more optimal uniform field control methodology and the limitations of such an approach. Due to its large workspace, this system could be used to control microrobots in a variety of medical applications.

Advisor: Diller, Eric
Network Coded Information Raining Over IEEE 802.16j

Information raining has been shown to address the problem of delivering Internet access to high-speed rail passengers. However, a wireline repeater or heterogeneous wireless relay design complicates implementation. Recent developments in extending mobile multihop relaying to the IEEE 802.16e standard have made it feasible to deploy an information raining using a common radio and physical layer. Two automatic repeat request techniques and two network coding techniques are proposed. An upper and lower bound on delay is established for a single fully network coded relay system operating in static signal to noise ratio conditions. Simulations involving a physical layer model demonstrate that network coding schemes can attain the maximum downlink capacity.

Keywords: information raining
IEEE 802.16j
network coding
Advisor: Valaee, Shahrokh
Optimizing Correction of Motion and Physiological Artifact in Clinical fMRI

BOLD fMRI (Blood-Oxygenation Level Dependent functional Magnetic Resonance Imaging) measures the haemodynamic correlates of brain function, with research and clinical applications. However, fMRI is limited by relatively weak signal, and large, complex noise sources. A variety of preprocessing algorithms have been developed to remove artifacts and improve signal detection, but there is no literature consensus on optimal preprocessing strategies. Furthermore, it is not well understood how fMRI experimental design choices interact with preprocessing steps. This thesis develops a statistical framework for selecting the set of preprocessing choices (“pipelines”), using data-driven metrics of (R) reproducibility of brain maps, and (P) prediction of experimental stimuli. These metrics were used to evaluate standard pipeline steps on data from young healthy subjects, who performed a set of brief tasks in an fMRI cognitive assessment battery. It is shown that (1) preprocessing choices have significant, consistent effects on the detection of brain networks in fMRI. However, (2) optimizing pipelines on a subject- and task-specific basis, compared to the standard fMRI approach of applying a single fixed set of preprocessing choices, improves (P, R) and independent test measures of between-subject activation overlap. This indicates that signal detection in standard fMRI may be limited by sub-optimal pipeline choices. Even after optimizing standard pipeline choices, physiological noise is a major confound in fMRI analysis; this includes BOLD signal changes due to respiration and pulsatile blood flow. As a potential solution, the PHYCAA (PHYsiological correction using Canonical Autocorrelation Analysis) algorithm is developed. This multivariate, data-driven model estimates physiological noise, without respiratory and cardiac measurements. The estimated noise has a spatial distribution consistent with non-neuronal tissues, and its dimensionality is correlated with cardiac and respiratory variability. Removing this physiological noise increases (P, R) of analysis results. The PHYCAA model provides novel information about the structure of physiological noise in fMRI, and a principled method of removing physiological artifact. The results of this thesis were obtained using data from a prototype fMRI cognitive assessment battery, designed for clinical use. The datasets involve brief scanning sessions with complex cognitive tasks. These findings are therefore relevant for clinical implementation of fMRI.

Keywords: optimization
physiology
clinical
multivariate
image processing
functional MRI
Advisor: Strother, Stephen
Seismic Analysis of Steel Wind Turbine Towers in the Canadian Environment

The seismic response of steel monopole wind turbine towers is investigated and their risk is assessed in the Canadian seismic environment. This topic is of concern as wind turbines are increasingly being installed in seismic areas and design codes do not clearly address this aspect of design. An implicit finite element model of a 1.65MW tower was developed and validated. Incremental dynamic analysis was carried out to evaluate its behaviour under seismic excitation, to define several damage states, and to develop a framework for determining its probability of damage. This framework was implemented in two Canadian locations, where the risk was found to be low for the seismic hazard level prescribed for buildings. However, the design of wind turbine towers is subject to change, as is the design spectrum. Thus, a methodology is outlined to thoroughly investigate the probability of reaching predetermined damage states under seismic loading for future considerations.

Keywords: seismic hazard
probability of damage
incremental dynamic analysis
finite element
seismic analysis
tubular steel
wind turbine tower
Advisor: Christopoulos, Constantin
Packer, Jeffrey A.