VictorJuiz/thesis huggingface dataset:
Constructing Efficient Production Networks: A Machine Learning Approach
Keywords: Design
Healthcare Operations
Machine Learning
Manufacturing
Network
Process Flexibility

For production networks with multiple plants and products process flexibility, defined as the ability to build different types of products in the same manufacturing plant, is a key driver of operational efficiency when faced with uncertainty in demand or supply. In their seminal work on process flexibility \cite{jordan1995principles} considered two central questions faced by the network designer: 1) How much flexibility is enough? 2) Where should flexibility be added? In this work we develop two novel Machine Learning based greedy heuristics that can be used to create efficient production network designs. We show that our heuristics perform at or above the level of the current state-of-the art heuristics on a number of test settings from the literature. Finally, we introduce a novel application of process flexibility in healthcare operations, and use our heuristics to produce efficient network designs for the radiation therapy unit at Princess Margaret Cancer Centre (Toronto, Canada).
Unified Spatiotemporal Calibration of Egomotion Sensors and Planar Lidars in Arbitrary Environments
Keywords: Calibration
Camera
Lidar
Optimization
Robotics
Sensor

This thesis develops an automatic spatiotemporal calibration routine for lidars and egomotion sensors that relaxes common requirements, such as overlapping sensor fields of view, and calibration targets. A set of entropy-based calibration algorithms are extended to allow estimation of sensor clock time offsets in tandem with sensor-to-sensor spatial transformations. A novel Bayesian optimization routine is developed to address the non-smooth behaviour observed in the cost function. The routine is tested on simulated and real data. Simulated results show that the calibration can be constrained to within less than 5 mm, 0.1 degrees, and 0.15 ms in the translational, rotational, and time-delay parameters respectively. For real-world data, in the absence of a reliable ground truth, we present results that show a repeatability of ± 4 mm, 1 degree, and 0.1 ms. A very brief analysis of the applicability of the method to Inertial Measurement Unit (IMU) to lidar calibration is presented.
THE EFFECTS OF FOLIC ACID AND 5- METHYLTETRAHYDROFOLATE SUPPLEMENTATION ON FOLATE METABOLISM AND ONE-CARBON TRANSFER REACTIONS IN A MOUSE MODEL
Keywords: 5-methyltetrahydrofolate
DNA Methylation
Folate
Folic Acid
Genes
Mouse

Due to emerging evidence linking high folic acid (FA) intake to certain adverse health effects, many have speculated that 5-methyltetrahydrofolate (5-MTHF), the natural form of folate, confer safer means of supplementation. As such, we investigated the differential effects of FA and 5- MTHF supplementation on folate status indices, gene expression of enzymes involved in folate metabolism and one-carbon transfer reactions, and tissue-specific global DNA methylation. Folate concentrations, gene expression differences, and global DNA methylation was assessed using the microbiological microtitre assay, real time RT-PCR, and in vitro methyl acceptance assay, respectively. 5-MTHF was, at least, as effective as FA in increasing plasma and tissue folate concentrations. Additionally, 5-MTHF had differential effects on tissue-specific gene expression, and increased liver global DNA methylation relative to FA. Our data suggests that 5-MTHF influences gene expression of enzymes involved in the folate pathway, which may potentially affect downstream biochemical processes such as global DNA methylation.
On the variability of TEOAE human identification and verification system
Keywords: Biometrics
Deep Learning
Identification
Machine Learning
Security
TEOAE

This study presents a deep neural network architecture that achieves state of the art multi-session veriﬁcation and identiﬁcation performance for Transient Evoked Otoacoustic Emission(TEOAE) biometric system. TEOAE is a 20ms long response generated by the ear that is naturally strong against falsiﬁcation, and replay attacks. It can be measured using a device with a speaker and multiple microphones. Previous TEAOE authentication methods focused on single-session or mixed-session performance. Our method focuses on multi-session authentication performance. We train a neural network model that generates a TEOAE embedding that is separable in Euclidean space by using the triplet loss function. These embeddings are used to create identity templates which are used to authenticate the user. We show that our method has 7.56% performance increase for identiﬁcation scenarios and 13.3% performance increase for veriﬁcation scenarios over previous methods when averaged across all tests
Examining sex differences in social-communication function across neurodevelopmental disorders: Autism Spectrum Disorder, Attention Deficit Hyperactivity Disorder and Obsessive Compulsive Disorder
Keywords: ADHD
Autism
Communication
OCD
Sex differences
Social

Autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), and obsessive-compulsive disorder (OCD) are neurodevelopmental conditions with male predominance. Although social-communicative deficits have been observed in these neurodevelopmental disorders, few studies have systematically examined sex differences in social-communication function within and across disorders. To explore this, a systematic review and meta-analysis was conducted, followed by a cohort study investigating sex differences in social-communication function in children with neurodevelopmental disorders. Findings suggest that sex differences significantly vary with age in children with both ASD and ADHD. Similarities but also distinct sex by age patterns were observed in aspects of social-communication function across disorders. Findings will help us understand the unique needs and experiences in males and females with neurodevelopmental disorders, guide research in the biological underpinnings of these conditions, and inform the development of treatments tailored to the needs of male and female patients.
Evaluating and Prioritizing Antimicrobial Stewardship Programs for Nursing Homes: A Modified Delphi Panel
Keywords: Antibiotics
Antimicrobial Stewardship
Delphi
Interventions
Nursing Homes

Antibiotic use in nursing homes (NHs) is often inappropriate and linked to adverse events and antimicrobial resistance (AMR). Antimicrobial stewardship programs (ASPs) are initiatives that optimize antibiotic use by minimizing inappropriate use, treatment cost, and the overall spread of AMR. NH providers and residents are candidates for ASP implementation; yet, guidelines for implementation are limited. We utilized the Modified Delphi Method to provide NH providers with 2 tools: (1) a prioritized list of necessary ASPs, and (2) resources for implementation. 16 panelists completed two survey rounds, attended an in-person meeting, and evaluated 15 ASP interventions across several criteria. 6 interventions were prioritized for NHs: (1) guidelines for empiric prescribing, (2) audit and feedback, and communication tools (tied), (3) short course antibiotic therapy, (4) scheduled antibiotic reassessment, and (5) clinical decision support systems. Interventions may need to be delivered using a multifaceted approach; however, this is likely infeasible for nursing homes.
Predictive Modeling of Dosimetric Quality Assurance in Radiation Therapy
Keywords: Quality assurance
Radiation therapy
Statistical learning
Volumetric arc therapy

Volumetric arc therapy (VMAT) is a type of cancer treatment where radiation is delivered continuously from a moving beam source. Due to potential delivery errors incurred by continuous delivery, pre-treatment dosimetric verification is regularly performed to measure beam deliverability by quantifying agreement between delivered and planned dose distribution. Using a dataset of VMAT beams and past verification results, we present a statistical learning approach to predict the deliverability outcome. A regression modelling pipeline with modular components of prediction models and resampling methods is trained and evaluated on three feature sets. Classification and outlier detection approaches are also applied after dichotomizing the deliverability outcome. The results show some evidence of learning, but performances are poor due to limitations of the dataset and the complex nature of the underlying process. We also propose a novel visual beam descriptor involving the Radon transform and present a preliminary image-based predictive pipeline.
Thermal Design and Analysis Methodologies Applied to the DAUNTLESS Bus and GHGSat-C Microsatellite
Keywords: Analysis
Control
Design
Satellites
Thermal

Throughout a mission’s preliminary design through to final acceptance, various thermal analysis and control techniques are implemented to verify feasibility through worst case hot and cold conditions. A satellite developed using the DAUNTLESS bus, the latest platform developed at SFL, faced many thermal challenges due to the large bus with an emphasis on methodologies to reduce risk. This resulted in a detailed thermal model leading up to the launch of the spacecraft, capturing details around the large antenna dish and the internal propulsion tank. GHGSat-C is a greenhouse gas monitoring satellite with high-resolution IR imaging capabilities. The satellite is a successor to the pre-existing GHGSat-D, which demonstrated the mission and its future constellation. The satellite features updates in almost every subsystem and introduces an optical downlink which drives aspects of the design. These topics are expanded on further in this thesis and any major milestones and results are presented accordingly.
Selective Ablation of Transgenic Neural Precursor Cells- A Cellular and Molecular Tool for Use in Neural Regeneration Studies
Keywords: ablation
brivudine
ganciclovir
metronidazole
neural stem cells
spinal cord injury

Induced pluripotent-stem cell derived-neural precursor cell (iPSC-NPC) transplantation represents a promising potential therapy for repair and recovery of the devastating condition of spinal cord injury (SCI). Three critical questions on NPC transplantation are (1) "Which neural cell type provides optimal regeneration?" (2) "Is long term cell integration critically necessary for recovery or only short term trophic support/immunomodulation?" and (3) “How much proliferation of transplanted iPSC-NPCs is required for neuroregeneration?” Selective cell ablation, using enzyme-prodrug systems, such as herpes-simplex virus thymidine kinase (HSV-TK) and ganciclovir (GCV) or brivudine (BVDU), or nitroreductase (NTR) and metronidazole (Mtz), is a cellular/molecular tool that may assist in answering the above questions via targeted ablation of transplanted cells in SCI-model rodents and assessment of repair/recovery. This project demonstrates successful creation (through transposon gene transfer) and the first GCV/BVDU-mediated in vitro ablation (>80% reduction over controls) of HSV-TK-expressing transgenic human iPSC-NPCs in an SCI transplantation context.
Novel DDBAC and TiO2 Functionalized Hydrophobic Electrospun Nanofibrous Membranes for Virus Adsorption in Drinking Water Applications
Keywords: DDBAC
drinking water
electrospun nanofibrous membrane
MS2 bacteriophage
titanium dioxide
water treatment

Waterborne pathogens such as enteric viruses have been identified as one of the main threats to sources of drinking water and aquatic ecosystem in Canada and around the world. Current methods to address viral contamination of water all present high degrees of efficiency. However, they also pose significant drawbacks such as release of potentially toxic/carcinogenic byproducts into the environment, high operating cost, or large land footprint. Nanofibre polyvinylidene fluoride (PVDF) membranes functionalized with TiO2 nanoparticles were developed. They were found to have a bacteriophage MS2 log removal value (LRV) of up to 1.2 likely through hydrophobic interactions and/or contact-inactivation with TiO2. Similarly, nanofibrous PVDF membranes functionalized with dodecyldimethylbenzylammonium chloride (DDBAC) for its positive charge and electrostatic effect were found to have a consistent MS2 LRV of 2.4. These novel membranes could provide a cost effective alternative to other available membrane technologies.
In Vivo Conjunctival Lymphatic Drainage Measured in Normal and Cancer Conditions by Photoacoustic Imaging
Keywords: conjunctival melanoma
in vivo
lymphatic drainage
melanin
near-infrared
photoacoustic imaging

The conjunctiva is a mucous layer which provides a physical barrier against the external environment. Lymphatic vessels within the conjunctiva drain excess interstitial fluid, contribute to immune surveillance, and are involved ocular surface inflammatory and neoplastic diseases, including conjunctival melanoma. Our understanding of conjunctival lymphatics is limited due to the lack of in-vivo imaging techniques to quantify lymphatic drainage. Using photoacoustic imaging and a near-infrared tracer, we quantified lymphatic drainage from the conjunctiva to cervical lymph nodes in normal mice. Lymphatic drainage from the conjunctiva was also assessed in a model of pigmented conjunctival melanoma. Lymphatic drainage after tracer injection within or around the tumour was similar to that observed in normal mice. Additionally, we monitored patterns of tumour growth over time using photoacoustic quantification of the tumour melanin signal. This non-invasive quantitative platform may be useful for future studies of conjunctival lymphatics in conjunctival melanoma and other ocular diseases.
The Effectiveness of Mental Health Courts in Reducing Recidivism
Keywords: Mental health courts
Police contact
Rearrests
Recidivism

Mental health courts were created to help criminal defendants who do not meet not-guilty-for-reason-of-insanity criteria, but who have a mental illness that is a significant contributing factor to their arrest. A systematic review of the literature was undertaken to look at the current evidence on the effectiveness of mental health courts in reducing client recidivism and contact with police. A secondary data analysis was done on mental health court support clients to see what individual level factors were associated with positive recidivism outcomes. From the systematic review, there is some evidence that diversion to a mental health court can lead to a significant decrease in recidivism while the effect on police contact is less clear. From the secondary analysis, age and criminal history were found to be significant factors associated with recidivism. Future research should explore other demographic, socio-economic, or programmatic factors that may be associated with positive recidivism outcomes.
Preventing Back Pain Among Caregivers Using Real-time Movement-centered Feedback
Keywords: back injury prevention
biofeedback
family caregivers
homecare
low back pain
wearable devices

Caregivers are at high risk of back injury from performing care tasks that often require the use of awkward postures. Existing traditional training programs have been shown to be effective at reducing the risk of back injury. The use of real-time feedback during care tasks offers a promising alternative approach. This thesis reports on the development of a training program based PostureCoach, a wearable device that warns users when the spine if flexed too far forward using real-time feedback. Novice caregivers (n=16) were asked to perform a series of simulated care tasks. The training program decreased the 80th and 95th percentile of forward flexion values in the intervention group (n=10) by 36.0% and 29.1%, respectively, while no change was seen in the control group (n=6). These results demonstrate the potential of our training program for reducing the risk of back injury for caregivers.
Immunophenotyping of cone photoreceptors during postnatal retinal development
Keywords: Cell-surface receptors
Cone photoreceptors
Immunophenotyping
Photoreceptors
Retinal Development

Cone photoreceptors are specialized postmitotic retinal neurons essential for detailed colour vision. When damaged, the mature retina cannot replace cones, leading to permanent vision impairment. Cone degeneration can occur secondary to retinal cell death due to a lack of surface-receptor mediated cross-talk between cells. The characterization of cones has proven challenging due to their low abundance and a lack of efficient enrichment methods. Here we perform an immunophenotyping assay using a novel cone-GFP reporter mouse, and identify novel cone markers during retinal development. The data show that the markers have temporal expression patterns on cones, which may be related to their roles in cone development. Validation based on known functions and availability of ligands and blockers, as well as gain- and loss- of-function experiments, will be used to investigate and characterize the functions of the identified markers. These experiments provide insight for developing targeted therapeutics for cone degenerative diseases.
Novel Techniques for Process Topology Reconstruction and Fault Diagnosis
Keywords: causality
fault detection and fault diagnosis
process topology reconstruction
root cause analysis

Fault diagnosis is a challenging problem, particularly for a large-scale industrial process. An up-to-date and accurate process topology – the causal relationships between system components of an industrial process – is crucial to understanding the fault propagation pathways and in diagnosing a fault. Data-driven techniques are more reliable for reconstructing the process topology, as compared to using expert knowledge. Many of the established algorithms for fault diagnosis impose strict assumptions that limit their applicability to industrial applications. As such, two novel algorithms from outside the field, Granger Net and Extended Convergent Cross-Mapping, were investigated. The classical Granger Causality algorithm was used as a performance benchmark. The effectiveness of the two novel algorithms for process topology reconstruction and fault diagnosis was investigated using five simulated processes and the Eastman industrial process data.
MATHEMATICAL MODEL AND VALIDATION OF MICROVASCULAR FUNCTION IN LOW PERFUSION TISSUE
Keywords: Gas Challenge
Microcirculation
Model
MRI
Perfusion
Tissue

Many diseases have shown to be caused or triggered following microvascular endothelial dysfunction, such as coronary artery disease, diabetes, and hypertension. Currently, blood flow is used as the main measurement to assess vasomodulation. It has however been proven to not be accurate as many other factors must be considered. This dissertation presents a mathematical model to assess vasomodulation in low perfusion tissue using MRI. It was developed by taking the first-order derivative of the T1 relaxation times (ΔT1) from a series of T1-weighted images of rat’s kidneys (high perfusion tissue) and leg muscles (low perfusion tissue). A gas protocol was used to assess vasoconstriction with a severe hypercapnic condition and vasodilation with a mild hypercapnic condition. The gas mixtures were administered using a fixed inspired concentration delivery technique. Perfusion measurements using Doppler flowmetry were obtained as validation to the model. Every vasomodulatory scenario with detailed analysis of the ΔT1 plots are covered.
Mechanical Adaptations of an Existing Microsatellite Platform to Address Emerging Space Applications
Keywords: Aerospace Structures
Assembly
Integration
and Testing
Mechanical Engineering
Space Engineering
Space Systems
Space Systems Engineering

As more organizations look to microsatellite constellations to achieve increasingly diverse goals, spacecraft designs must continuously adapt to meet these new goals while simultaneously leveraging flight heritage. To accommodate the needs of these future missions, an iterative redesign of the flight proven Next-generation Earth Monitoring and Observation microsatellite platform was performed. This thesis presents the overall design of the bus for a sample three-spacecraft formation-flying mission, with a strong focus on the mechanical aspects of the system. This work covers the development of the platform from the structural design phase through to qualification, flight integration, and system-level acceptance testing. The successful implementations of an electromagnetic interference mitigation strategy, and a structural design that allows for a flexible and robust assembly process are detailed. The design of a mass dummy system and subsequent qualification of the structure is also described. Finally, aspects of assembly and testing of the spacecraft are discussed.
Oral Health-Related Quality of Life in Children with Oligodontia
Keywords: Oligodontia
Oral Health Related Quality of Life
Pediatric Dentistry

Background: Oligodontia (congenial absence of six or more teeth) affects functional, emotional and social aspects of an individual’s life. Few published studies have evaluated oral health-related quality of life (OHRQoL) in children with oligodontia. Methods: Thirty-five 8-to-18-year-old patients with oligodontia recruited from The Hospital for Sick Children and Holland Bloorview Kids Rehabilitation Hospital, and their parents, completed the short format of Child Perception Questionnaire (CPQ11-14) and Parent Child Perception Questionnaire (P-CPQ), respectively. Results: Children reported significantly higher overall CPQ score than their parents. Correlations between children’s and parents’ overall CPQ score, oral symptoms and functional limitations, and social well-being were not statistically significant. However, as children’s emotional well-being score increased, parents’ score also increased. There was no association between child CPQ score and age, gender, number and location of permanent tooth agenesis in this sample. Conclusion: Children with oligodontia had poorer OHRQoL compared to what was perceived by their parents.
Learning data representations for robust neighbour-based inference
Keywords: boundary tree
classification
data representations
differentiable boundary tree
MNIST
nearest neighbour

The recently proposed Boundary Trees algorithm by Mathy et al. (2015) enables fast neighbour-based classification, regression and retrieval in large datasets. While boundary trees use a Euclidean measure of similarity, the Differentiable Boundary Tree (DBT) algorithm by Zoran et al. (2017) was introduced to learn low-dimensional representations of complex input data, on which semantic similarity can be measured, to train boundary trees. The DBT approach contains a few limitations that prevents it from scaling to large datasets. In this thesis, we introduce Differentiable Boundary Sets, an algorithm that overcomes the computational issues of the DBT scheme and also improves its classification accuracy and data representability. Our algorithm is efficiently implementable with existing tools and offers a significant reduction in training time. We test and compare the proposed algorithm on the well known MNIST handwritten digits dataset and the newer Fashion-MNIST dataset by Xiao et al. (2017).