VictorJuiz/thesis huggingface dataset:
Bayesian Spatiotemporal, Sample Survey, and Forecasting Methods for Analyzing COVID-19 Infections and Mortality
Keywords: Bayesian statistics
Covid-19
Epidemiology
Spatiotemporal modelling

For decades, mathematicians and statisticians have been modelling infectious diseases to forecast case/death counts, estimate important epidemiological quantities, and understand the dynamics of disease spread.This dissertation offers methodological insights into each of these three challenges using novel spatial, spatio-temporal, and Bayesian modelling methods, with applications to COVID-19 data. Alongside methodological contributions, this thesis also presents estimates of important epidemiological quantities which, subject to peer review, could be utilized by public health professionals and policy makers. There are four primary contributions of this work: 1) a subnational, single-wave COVID-19 mortality forecasting model that accounts for day-of-the-week effects, which was shown to outperform the most highly-cited model during the first viral wave; 2) a mobility-augmented spatial model for COVID-19 case counts, where cellphone-derived mobility data is shown to capture dependence between areal units better than physical proximity; 3) a novel, interpretable spatio-temporal infectious disease model where infectiousness is a function of mobility between areal units, resulting in estimates of the risk associated with travelling in two Spanish Communities; 4) a modular Bayesian framework based on mixture modelling of serological data and disaggregated deaths data to estimate COVID-19 incidence and infection fatality rates, resulting in estimates of these quantities across Canada for various strata. Although the applications in this thesis are to COVID-19 data, the proposed methodology can be applied to a wide spectrum of problems across infectious disease epidemiology.
Storying Journeys in Environmental Sustainability Education: Navigating Teacher Candidates’ ESE Experiences
Keywords: eco-anxiety
environmental and sustainability education
environmental education
intersectional environmentalism
teacher candidates
teacher education

This thesis seeks to understand the experiences of twenty K-12 teacher candidates (TCs) within environmental and sustainability education (ESE) at a large Ontario faculty of education. Using Sauvé’s (2005) cartography of environmental education currents and Cooper and White’s (2012) five contexts as frameworks, I ask to what extent and in what ways are TCs engaging within ESE during their teacher education programs. Through a qualitative interview study inspired by the art of storytelling, twenty personal, highly-detailed and honest accounts developed of what it is like to be a TC engaging in ESE during an uncertain time (COVID-19) and looking towards an uncertain future for our climate. In their own words, TCs reveal the conceptualizations of ESE they carry, the range of emotions they navigate, the opportunities and supports they receive, the barriers and challenges they face, and the motivations that draw them into this complex field of study (despite their faculty of education not requiring them to do so). In listening to their stories, we hear powerful testimony from a group of TCs urging Canadian faculties of education and K-12 schools to respond to their call for relevant, engaging and meaningful ESE and climate action that is rooted in intersectionality, social justice, and appropriately inclusive of Indigenous pedagogies and perspectives. This study is significant in a number of ways: it informs our understandings of the role of ESE theory, the spectrum of emotions (from eco-anxiety to resilience), the critical importance of community, and the individual and systemic barriers at play in TCs’ personal and professional lives. I conclude the thesis with suggestions and considerations - theoretical and practical - for ESE in Canadian teacher education programs. Keywords: environmental and sustainability education; ESE; environmental education; teacher candidates; teacher education; climate justice; climate action; eco-anxiety; interview study; stories; intersectional environmentalism; environmental justice; ecojustice education; Indigenous education
Potential Hydrologic Changes of a Low Impact Development Neighbourhood
Keywords: Evapotranspiration
Hydrology
Low Impact Development
Lysimeter
SWMM Modelling
Water Balance

Low Impact Development is intended to maintain pre-development hydrology, but this remains widely unproven due to a lack of large-scale implemented and monitored projects. The Creek Side Village (CSV) is a planned community that will convert a 28 ha fallow field into a mixed-density residential neighbourhood for seniors. The proposed neighbourhood will not include conventional stormwater management systems and will instead manage stormwater exclusively through Low Impact Development (LID). This thesis proposes an approach for evaluating the pre-development hydrologic conditions of a land development site and examines how hydrologic processes may change when developed with LID infrastructure for stormwater management. The property's pre-development hydrological conditions were characterized by collecting field data in the summer and fall seasons with comprehensive onsite measuring tools to understand the current seasonal hydrological condition. The development property has highly permeable soils with an average saturated hydraulic conductivity of 24 mm/hr, making the site well suited for infiltration-based LIDs. Evapotranspiration (ET) and seepage water data collected from high-resolution weighing lysimeters were analyzed to determine the site’s water balance. A comprehensive filtering process was developed and adopted to minimize the errors in the lysimeter data, thereby improving the estimate of ET. The water balance between the proposed LID neighbourhood was compared with data from an instrumented bioretention cell site to assess if this LID approach is likely to mimic the pre-development water balance. Results showed that green infrastructure could mimic the water balance of the fallow field and compensate for potential ET losses after development. Field data were used as input data for a pre-development hydrologic model (using SWMM and SWMM-UrbanEVA). The model was calibrated using the lysimeter water balance data. Hydrologic modelling was conducted to determine if specific LID approaches (permeable pavements and/or bioretention cells) can maintain pre-development water balance conditions after development. Four proposed post-development scenarios were considered. The modelling work reveals that bioretention cells are unlikely to maintain the pre-development water balance. Using PPs to control stormwater is a more effective approach, while PPs+BioCells can help maintain pre-development water balance, but they are less effective than PPs only.
CLP: Efficient and Scalable Search on Compressed Text Logs
Keywords: compression
debugging
diagnosis
logs
search
systems

This thesis presents the design and implementation of CLP, a tool capable of losslessly compressing unstructured text logs while enabling fast searches directly on the compressed data. Log search and log archiving, despite being critical problems, are mutually exclusive. Widely used log-search tools like Elasticsearch and Splunk Enterprise index the logs to provide fast search performance, yet the size of the index is within the same order of magnitude as the raw log size. Commonly used log archival and compression tools like Gzip provide high compression ratios, yet searching archived logs is a slow and painful process as it first requires decompressing the logs. In contrast, CLP achieves significantly higher compression ratio than all commonly used compressors, yet delivers fast search performance that is comparable or even better than Elasticsearch and Splunk Enterprise. In addition, CLP outperforms Elasticsearch and Splunk Enterprise’s log ingestion performance by over 13x, and we show CLP scales to petabytes of logs. CLP’s gains come from using a tuned, domain-specific compression and search algorithm that exploits the significant amount of repetition in text logs. Hence, CLP enables efficient search and analytics on archived logs, something that was impossible without it.
The Reliability and Error Mitigation of the Storage Stack
Keywords: DRAM
Error Mitigation
memory
Reliability
Storage

The reliability of storage systems is critical for our modern world. As the world's demand for data expands, an ever increasing number of systems and devices are required to store and process these data. A single data center often contains hundreds of thousands and even millions of nodes. The numerous components dictate that failures within these clusters are no longer the exception but the norm, and simplistic assumptions about error characteristics and mitigation are insufficient at these scales. The cost of operation for individual systems as well as massive data centers are contingent on low rate of error incidences and successful, timely mitigation to minimize the impact to regular operations. To that end, this thesis studies the reliability of different components across the storage stack and develops mitigation strategies based on insight gained from large-scale, real-world data. Specifically, we study main memory and disks, two of the most frequently replaced components in data centers. We analyse memory error characteristics from several large-scale systems and recommend simple page retirement policies to mitigate errors. We further develop a novel technique to protect previously unprotected pages that belong to the kernel, by repurposing virtualization hardware already available on modern processors. We also evaluate several new error mitigation mechanisms available on modern hardware. At the same time, we examine the effect that temperature has on system performance and power draw. Lastly, we study the reliability of file systems on solid state drives by injecting errors based on realistic SSD fault modes. We discover bugs that are present in popular file systems and make recommendations on future file system design. Overall, we advance the understanding of error characteristics and mitigation in several aspects and across different layers of the storage stack. We study errors by observing systems in deployment, and use the insights gained to develop effective mitigation that target real-world scenarios.
Aerodynamic Atomization of Liquid Drops and Jets
Keywords: Atomization
Droplets
Sprays

Atomization is a process ubiquitous in both nature and industry where a liquid flow is fragmented into a spray of small droplets. Despite having been studied extensively for almost a century, sprays remain poorly understood. The existing models are not based on a realistic understanding of the mechanisms of breakup and thus fail to provide good prediction of the spray behaviour across a wide range of operating conditions, in particular the size distribution of the spray droplets.In this thesis, the aerodynamic atomization of liquid drops and jets is studied experimentally and theoretically, with a focus on developing realistic models for the many sub-processes that occur during the breakup. It is shown that the initial deformation rate of a liquid drop exposed to a high speed gas flow governs its breakup morphology as well as the diameter of the ligaments produced from its deformation. Following the formation of these ligaments, a plurality of mechanisms are shown to occur throughout the fragmentation, which result in the distribution of droplet sizes. Analytical models are derived for each of the sub-processes of the breakup and compared with measurements of intermediate stages of droplet breakup, ultimately resulting in a prediction of the droplet size distribution from aerodynamic droplet breakup. Notably, the model presented in this thesis is unique in that it provides a complete description of the breakup process as well as a prediction of the resulting size distribution. Finally, the framework of the droplet breakup model is leveraged to model the atomization of a coaxial, twin-fluid spray, giving a deterministic, analytical prediction of the droplet size distribution resulting from twin-fluid atomization. The models developed in this work were made available in a Python implementation that we refer to as the Aerodynamic Droplet and Atomization Model (ADAM).
Fluorine NMR as a Tool for Analysis of Fluorinated Compounds in the Environment
Keywords: Environmental
Fluorine
Mass Spectrometry
NMR
PFAS

The ubiquity of per- and polyfluorinated alkyl substances (PFAS) in the environment is a global concern. As a result of an ever-expanding number of fluorinated compounds, the existing methods of analysis for PFAS are facing increasing challenges. Tandem mass spectrometry methods become limited by numbers of compounds able to be examined simultaneously. Non-targeted or suspect screening methods are useful for identifying unknown or undiscovered fluorinated compounds in samples but can be limited by difficulty in quantifi- cation or ionization. Fluorine nuclear magnetic resonance spectroscopy (19F NMR) offers a complementary tool to mass spectrometry methods. 19F NMR provides an unbiased, non-targeted snapshot of a sample. Unlike mass spectrometry, there is no required anticipation of types of carbon-fluorine bonds or functional groups present. The sensitivity of the fluorine nucleus being similar to that of proton allows for low-concentration analysis of nearly every fluorinated compound in a particular sample. Additionally, the lack of naturally occurring, fluorinated species allows for analysis with minimal background contamination. In this thesis, 19F NMR was used to monitor degradation and transformation of fluori- nated agricultural chemicals. It was also used to examine organic fluorine contamination in a variety of samples including surface waters, rainfall, municipal water sources, wastewater, human urine, and human serum. Across all these samples, 19F NMR was better-able to account for the extractable organic fluorine content when compared with either targeted or non-targeted mass spectrometry techniques. Additionally, detection limits of 19F NMR have been pushed as low as 389 pg/L, when detecting trifluoroacetic acid in rainwater using a novel noise-reduction strategy. The capability of 19F NMR was further demonstrated by the first experimental measurement of aqueous diffusion coefficients for several PFAS. The thesis concludes by publishing the 19F NMR spectrum of numerous PFAS (100+ compounds) and provides useful guidelines for acquiring 19F NMR spectra of environmental samples. The overarching goal of this thesis was to demonstrate the capability of 19F NMR for environmental and analytical chemistry. This has been successfully achieved by the straightforward application of 19F NMR for the low-concentration detection of PFAS, and its capability for non-targeted analysis of PFAS within complex samples.
Integrating Relational Perspectives into Strategic Human Resource Management: Three Papers on Strategic Relational Human Resource Management
Keywords: Human Resource Management
Interpersonal Relationships
Strategic HRM

This thesis highlights the importance of integrating a relational perspective into strategic human resource management (HRM) and investigates how to make this integration in three studies. Chapter 1 first provides a review of the extant strategic HRM studies with a relationship-related focus. The review shows that our knowledge is limited in understanding the potential for HRM systems to directly influence individuals’ relational attitudes and behaviours. To address this void, this chapter develops a theoretical model of strategic HRM systems—strategic relational HRM—an interrelated set of HR practices that intends to help employees build and maintain workplace relationships. Theoretically, the model of strategic relational HRM emphasizes the role of individuals’ relational competencies in facilitating the emergence of human and social capital at the unit-level, which further contribute to firm performance. Chapter 2 explores how strategic HRM can enable new ventures to satisfy their simultaneous strategic needs of survival and growth through an inductive approach. Drawing on 51 semi-structured interviews conducted in two young hospitals in China, this study finds that young organizations relied on relationship-oriented collaboration to achieve their strategy through the implementation of a multilevel model of HRM systems. Specifically, at the macro-level, they used cross-organizational partnership to share human resources and develop their own employees. At the meso-level, these organizations cultivated collaborative cultures and designed HR policies targeting a promotion of relationship-building within firms. At the micro-level, they highlighted relational capabilities of individual employees, in addition to their technical skills. Having established the importance of relational attributes in the first two chapters, Chapter 3 develops a measure of strategic relational HRM following a rigorous procedure of measure development. This study underscores the importance of employees’ relational competencies as the micro-foundation of human capital and argues that strategic relational HRM can lead to superior firm performance through enhanced relational skills of individuals. Drawing from four samples from North America and Asia, this study tested content validity, internal consistency reliability, convergent, discriminant, criterion-related and incremental validity of a new strategic relational HRM measure. Evidence of the present study supports the use of strategic relational HRM measure in future research.
Development of a Perspective Switching Task to Train Decentering
Keywords: Decentering
Interpersonal
Mood Induction
Perspective
Psychological Distancing
Virtual Reality

Decentering is the ability to shift experiential perspective from being immersed in internal experiences to observing them from a distance. Research has consistently shown the therapeutic benefits of taking a decentered perspective, but how to directly train this skill has yet to be investigated. One component of decentering, disidentification from internal experience, closely resembles self-distancing, which is the ability to switch from a first- to third-person perspective. Virtual reality provides a unique platform to train self-distancing using behavioural modification principles to enhance learning. As such, the goal of this research program was to develop an interpersonal mood induction with first- and third-person perspective videos (studies one and two) to be used in a Perspective Shifting Training (PST) tool to train decentering (study three).Using interpersonal theory of complementarity to inform the content of our videos, we developed a mood induction (30 videos) that successfully elicited positive and negative valence in undergraduate and global samples, regardless of the technology used (virtual reality, 360-degree videos, flat videos). However, technology impacted levels of presence (sense of “being” in the virtual environment). Specifically, watching virtual reality videos elicited greater levels of presence relative to flat videos; this difference was not observed when comparing 360-degree videos with flat videos. Using this mood induction, we recruited participants to train for two weeks in a PST condition (practice in “switching” perspectives through watching first- then third-person videos) or a Control condition (watching first-person perspective videos only). Participants in PST reported improvements in self-distancing that were associated with a gradual decrease in evoked valence. Both conditions increased in levels of decentering, suggesting the presence of an unexpected mechanism. Moreover, change in decentering was not associated with change in psychological well-being. The impact of individual differences, personal relevance, and immersive technology on presence is discussed. We explore the benefits of taking a first- or third-person perspective and highlight the importance of clarifying the dose-response relationship for self-distancing. Implications and future directions are discussed; most notable being the importance of recognizing and harnessing individual differences to enhance the therapeutic benefits of new and existing interventions to train decentering.
Developing Novel Approaches to Bacterial Detection
Keywords: Bacterial Detection
Biosensing
Diffraction Sensing
Surface Interaction Profiles

The rise of antibiotic resistance is a critical problem impacting healthcare networks worldwide. A common bottleneck in the assessment and effective treatment of antibiotic resistant infections is lengthy bacterial detection, which includes both pathogen identification and antibiotic susceptibility testing (AST). Standard clinical AST techniques require upwards of 48-72 hours, in large part delayed by the need to observe growth of bacterial colonies by eye. Furthermore, emerging technologies, while effective, are limited in their routine use and otherwise inaccessible in the developing world due to significant operational barriers. This work introduces the development of two novel approaches to bacterial detection that aim to improve upon detection times for both bacterial identification and AST with an emphasis on reduced cost, ease-of-use, and utility. The first approach involves the adaptation of an existing optics-based technique (diffraction sensing) for use in the measurement of bacterial growth, with a future application in AST. Three distinct cases were developed and proof of concept results discussed, focusing on shifts in the diffraction spot intensities produced by patterns comprised directly of the test bacterium, Escherichia coli, and those made from inoculated polymer and LB agar gels. The signal detection times for bacterial growth compared with the non-inoculated pattern controls across all three approaches ranged from 10-30 minutes, with more consistent shifts in diffraction intensities observed in cases with improved bacterial-surface spatial control and growth confinement. The second approach was developed from the ground up and focused on differentiating bacteria by their unique interactions with a small series of polyelectrolyte multilayer (PEM) surfaces. A simple microfluidic channel-based method was optimized to fairly assess these attachment strengths, which were combined to build cell-specific surface interaction profiles (SIPs). SIPs were applied to a series of hospital acquired ESKAPE pathogens and S. aureus single nucleotide polymorphisms (SNPs), highlighting their ability to distinguish bacteria by Gram stain, individual species, and SNPs. A limited and successful blind experiment introduces the potential for utilizing SIPs in bacterial identification, and their capability to differentiate resistant isoforms of S. aureus underscores the future clinical applicability of SIPs in potentially streamlining identification and AST with a single, low-cost test.