VictorJuiz/thesis huggingface dataset:
Development of Solid Oxide Fuel Cell Metal Supports and Intermediate-Temperature Direct Hydrocarbon Anodes
Keywords: Barium titanate
Copper-SDC based anodes
Metal supports
Solid oxide fuel cells
Solution plasma spraying
Suspension plasma spraying

Direct hydrocarbon (DH) solid oxide fuel cells (SOFCs) have the potential to play a role in the transition from high-polluting to cleaner and sustainable electricity generation systems. Moreover, metal-supported DH SOFCs have several advantages that could potentially increase the overall competitiveness of the technology. In this study, bi-layer metal-supported cells were developed using suspension plasma spraying (SPS). The cells were fabricated on bi-layer supports fabricated by powder metallurgy, with microstructures designed to provide a suitable surface morphology for uniform anode deposition. An open circuit voltage (OCV) of 1.105 V at 750 °C in 3% humidified hydrogen was obtained, which deviates from the Nernst potential by only 5 mV. Therefore, the bi-layer support has the potential to minimize cell defects that are known to be detrimental to cell performance. Metal-supported SOFCs containing Cu-Ni-samaria doped ceria (SDC) composites with or without additions of Co or CeO₂, were evaluated for stability against interdiffusion in H₂ at 850 °C. The interdiffusion of Fe, Cr, and Ni was studied, and three protective strategies to prevent the diffusion of Fe and Cr into the Cu-based anodes were evaluated: pre-oxidation of the metal support, application of a protective LaCrO₃ coating to the metal supports, and addition of an SDC interlayer between the metal support and anode. It was found that the most effective stand-alone method to diminish the diffusion of Fe into the anodes containing Co and Ni was the addition of an SDC interlayer between the anode and the metal support. An assessment of the feasibility of using BaTiO₃ as a catalyst for DH SOFC anodes was also performed. The microstructure and electrochemical performance of metal-supported Cu-SDC-Ni-based anodes with or without the addition of CeO₂ and/or BaTiO₃ were evaluated. Metal-supported SOFC anodes were fabricated by a hybrid suspension and solution precursor plasma spraying (SPS/SPPS) process and by SPPS. SPPS Cu-based anodes were found to have better resistance to coarsening than SPS/SPPS Cu-based anodes. The highest power density in this study was obtained for an SPPS Cu-SDC-Ni-BaTiO₃ anode, with peak power densities at 750 °C of 624 mW/cm² and 266 mW/cm² in H₂ and in CH₄, respectively.
Emergency Medical Services Response Optimization
Keywords: Drones
Emergency medical services
Facility location
Global health
Machine learning
Optimization

Time-sensitive medical emergencies are responsible for one-third of all deaths worldwide and similarly affect both developed and developing countries. Emergency medical services (EMS) provide rapid out-of-hospital treatment for time-sensitive medical emergencies. In this thesis, we combine optimization and machine learning to solve challenging EMS response problems in two diverse settings: Dhaka, Bangladesh - an urban center in a developing country, and Toronto, Canada - an urban center in a developed country. These settings are unified by uncertainty and stochasticity leading to new robust and chance-constrained optimization problems. In both cities, we employ machine learning to integrate real data with our optimization models. The second chapter develops a unified framework for emergency response optimization under travel time (edge-length) and demand (node-weight) uncertainty that is suitable for developing urban centers. We traveled to Dhaka to conduct field research resulting in the collection of two unique datasets that we leverage to estimate demand for EMS and to predict travel times in the road network. We carefully integrate our predictions and robust optimization model to develop an efficient solution algorithm for large-scale problems. We use our framework to provide an in-depth investigation into four key policy-related questions. The third and fourth chapters focus on improving EMS response for out-of-hospital cardiac arrest (OHCA). Chapter 3 employs a simplified location-queuing framework to quantify the potential benefit from using drones to deliver automated external defibrillators (AEDs) to OHCAs. We demonstrate, using data from 50,000 historical OHCAs covering 26,000 square kilometers around Toronto, that a drone network has the potential to significantly reduce AED delivery time. Chapter 4 develops a two-stage machine learning approach to simulate cardiac arrest incidents and an integrated location-queuing model tailored to the problem of drone-delivered AEDs. Our model combines the p-median framework with an explicit M/M/d queue to determine the minimum number of drones required to meet a pre-specified response time goal (average or 90th percentile), while guaranteeing that a sufficient number of drones are located at each base. We develop a novel reformulation technique that exploits the baseline (EMS) response times, allowing us to optimally solve large-scale instances and provide policy insights.
Scripting and Orchestration of a Knowledge Community and Inquiry Curriculum for Secondary Biology
Keywords: active learning classroom
blended learning
computer-supported collaborative learning
design-based research
learning analytics
science education

This dissertation describes a Computer-Supported Collaborative Learning (CSCL) curriculum and corresponding technology environment called CKBiology. Using a design-based research methodology, CKBiology was designed in close collaboration with a high school biology teacher and two software developers, and was implemented in two sections of a Grade 12 Biology course over five design cycles during the 2016-2017 academic year. A pedagogical model called Knowledge Community and Inquiry (KCI) guided the design of CKBiology. Here, students worked as a community to co-construct a shared knowledge base which served as a resource for subsequent inquiry activities. An important aspect of KCI is the design of curricular scripts, which specify the activity sequences, interactions, and roles and responsibilities of individuals within the community. Whereas scripting refers to the structuring of activities before they are run, orchestration refers to the process of executing a script in real-time. This work responds to three challenge areas related to scripting and orchestrating KCI. First, recognizing the high degree of complexity associated with implementing CSCL scripts, one outcome of this work is the development of various student- and teacher-facing learning analytics and software operators that serve to reduce the “orchestrational load” of a KCI community. Second, in orchestrating small group collaborations, another outcome of this work is an evaluation of different group-formation protocols with respect to the collaborative processes, products, and outcomes of the resulting groups. Based on these findings, a series of recommendations is made regarding the ways that different kinds of small groups can be supported in their collaborations. Finally, acknowledging the lack of formalism in the learning sciences for representing CSCL scripts, a third outcome of this work is a series of representations that serve to capture and communicate CKBiology (and KCI scripts more generally) to other practitioners and researchers.
Assessing the Metastatic Potential of Cancer Cells with Microdevices
Keywords: assay
cancer
circulating tumor cells
metastasis
microdevices
migration

Cancer is a leading cause of death worldwide, and tumor heterogeneity presents a challenge for the clinical management of the disease. The central aim of this thesis is to present new strategies that can be applied towards cancer diagnostics and shed light on the metastatic process. Significant progress has been made over the last decade towards the development of approaches that enable the capture of rare circulating tumor cells (CTCs) from the blood of cancer patients and provide detailed proteomic, genetic, and phenotypic analysis. During cancer metastasis, cells may transition from an epithelial to a mesenchymal phenotype. We designed a microfluidic device that sorts CTCs into subpopulations representing different phases of epithelial to mesenchymal transition (EMT). This allows us to detect more invasive subpopulations and monitor their abundance during treatment regimes. We hypothesize that identification of CTC subpopulations can provide enhanced diagnostic information. CTCs are labeled with magnetic nanoparticles conjugated to an antibody against a cancer cell surface marker (such as epithelial cell adhesion molecule; EpCAM). Magnetically tagged CTCs are sorted into four zones of the device based on their surface level of EpCAM. The microfluidic device was used to profile low- EpCAM CTCs from metastatic castrate resistant prostate cancer patients over a defined treatment period, and towards identification of invasive cell subpopulations. Metastasis begins with the invasion of tumor cells into the extracellular environment and migration towards the blood stream. Elucidating factors which govern successful tumor cell dissemination may allow the development of therapies that can halt metastasis. We hypothesized that invasive cancer cells will selectively engage with pores of specific geometries when presented with porous micro-structures. We created 3D 20µm- tall pores of varying cross section and aspect ratios to mimic the extracellular space and to monitor the migration of breast cancer cells. We identified that breast cancer cells exhibit a pathfinding behavior when navigating through a complex arrangement of pores. Overall, the designed microdevices provide a means to identify features of invasive cancer cells using nanoparticle- assisted sorting, and migration analysis through porous structures. These approaches provide further insight into the metastatic process.
Dynamic Capitalisms? Understanding Patterns of Technological Specialization through an Exploration of Interfirm Interaction at International Trade Fairs
Keywords: Comparative Capitalisms
Garbage-Can Model
Institutional Change
International Trade Fairs
Search Processes
Varieties of Capitalism

In recent years, the study of comparative capitalisms has raised important questions about the dynamic elements of capitalist diversity and the need for better explanations of institutional change. Building on this and related literatures in political economy and economic geography, this thesis explores a source of economic dynamism that has been overlooked in political science: international trade fairs. Despite their relative neglect in this field, trade fairs have long been recognized as an important promotional and sales tool. More recently, scholars have conceptualized trade fairs as critical sites through which global knowledge flows are circulated and ideas for innovation explored. From this perspective, trade fairs represent important platforms for networking, interactive learning, and knowledge exchange because they foster intense interactions among actors despite spatial boundaries. And, since trade fairs are organized according to a specific technological or industry focus, they facilitate interactions between firms from different capitalist varieties. Through the diffusion of state-of-the-art knowledge, trade fairs may serve as drivers of economic globalization, challenging the continuation of distinct capitalist varieties by enabling cross-system convergence regarding the technological specialization of firms. Yet, it is clear that countries have retained competitive advantages in specific industries and that full convergence has not taken place. Adopting a mixed-methods approach, this thesis addresses this puzzle, first, by exploring the micro-processes of search through a qualitative study. Using the garbage-can model and the “organized anarchy” concept, it finds that firms’ search processes rarely follow a linear or rational trajectory; yet, they are guided by the pre-existing production contexts and knowledge bases of their home countries. The empirical foundations of these findings are broadened through a quantitative study, which identifies different learning and search patterns between German and U.S. firms at trade fairs, largely consistent with the Varieties of Capitalism perspective. Taken together, the analysis reveals the path-dependent nature of firms’ search processes and suggests that firms from different countries are embedded in distinct paths and tend to follow their specific pathway. The thesis highlights the role of trade fairs as mediators of global economic processes and calls for greater attention to these events in political science.
Revenue Management in the Sharing Economy
Keywords: Crowdfunding
On-demand matching
Revenue management
Sharing economy

In this thesis, we study revenue management problems in the sharing economy. In the first chapter, we study contingent stimulus policies in crowdfunding. We consider a model where backers arrive sequentially at a crowdfunding project. Upon arrival, a backer makes her pledging decision by taking into account the expected project success rate. We characterize the dynamics of a project's pledging process. To boost success, we propose and characterize three contingent stimulus policies, namely seeding, feature upgrade and limited-time offer. We show that the benefit of contingent policies is greatest in the middle of crowdfunding campaigns. Testing with the data set of Kickstarter, we obtain empirical evidence that the projects' success rates improve by 14.6% on average with updates in the middle of the campaign and when the pledging progress is lagging. In the second chapter, we study the revenue management problem with an all-or-nothing constraint. We consider a seller who would receive her rewards only when her total sales exceeds a threshold by the end of the selling period. We derive the optimal sales intensity and prove that the intensity is contingent on the sales progress and is not monotone. We analyze the deterministic heuristics and show that their performances are compromised because of the all-or-nothing constraint. We then propose a two-stage modified resolving heuristic that greatly improves the performance and has provable optimality gaps. In the third chapter, we study platform competition of on-demand matching platforms. Customers and service providers chooses the platforms according to nested logit models. We show that as the users gain access to multiple platforms, the platforms can match supply and demand more efficiently, thus increasing in the utilization of the services. However, access to multiple platforms also increases the overall attractiveness of the service. We show that if the supply side is stringent enough, the price will increase when the platforms compete on the demand side. Similarly if the demand side is stringent enough, the wage will decrease when the platforms compete on the supply side.
Supporting Transfer Time Predictions in Medical Dispatch using Visualizations of Historical Data
Keywords: Decision making
Decision support
Medical dispatch
Prehospital emergency care
Time prediction
Uncertainty visualizations

Decision making in emergency medical dispatch is difficult due to the high time pressure and uncertainty faced by dispatchers. This is especially true in large-scale medical transport systems, such as Ornge in Ontario, because of the large geographical area serviced and the limited air and land ambulance resources available. Oversight agencies have highlighted the need to support patient transport time prediction for different transport options to facilitate better dispatch decisions. This dissertation proposes the use of visualizations of historical transport time data as a decision-aid to support predictions of patient transport time. Specifically, this dissertation examines whether visualizing the variability of historical data, which can help decision makers understand the uncertainty of the process, may improve transport time predictions. Historical time predictions recorded by Ornge were statistically analyzed and two field studies were conducted at Ornge. Transport time predictions were found to be an important part of the dispatch process, but they are often underestimated, and that dispatchers do not explicitly incorporate uncertainty information in their predictions, possibly due to time constraints. Based on these results, an interface and workflow for a decision-aid visualizing historical data was proposed. In order to support the design of this decision-aid, two experimental studies were conducted to examine the influence of display format and context information on prediction behavior. The experiments were based on a proposed framework, informed by the literature, for how individuals use historical data visualizations to predict values of a variable. The experimental results provided evidence that both display format and context information impact prediction behavior, however the underlying process used by the participants appeared to differ from that suggested in the proposed framework. Thus, further research is needed to improve this framework. Overall, this dissertation adds to the very limited literature on supporting medical dispatch decisions through the design of a decision-aid for transport time prediction. In addition, this dissertation provides preliminary evidence for how individuals use visualizations of historical data to generate predictions of variables and what factors may influence these predictions.
Aiming for Peace and Responding to Crisis: Movement and the Saints in Eleventh-Century Southern French Miracle Collections
Keywords: France
medieval
miracles
movement
reliquaries
saints

Movement, a literal change in physical location, is often overlooked in historical studies of medieval miracle texts. Instead, scholars largely focus on the “miracle-working power” of the saints or other cultural economic, political, religious, and social details that can be gleaned from these sources. This dissertation adds to such readings of texts on saints’ miracles by using movement as a category of analysis. In the miracle texts of Saints Vivien at Figeac, Privat at Mende, and Enimie at Sainte-Enimie, all written in the eleventh century in the south of France, movements abound in a flurry of danger and excitement in reference to their relics. While the hagiographers never question the movements of relics, their discourse about movement of people shows a preference for stasis. If people must move, they should do so according to the proper protocol in support of the saints’ cults; out-of-the-ordinary movements, especially those “bad” movements made by proven criminals, are condemned and punishable by the saints. The first chapter introduces the field of research and the second chapter contextualizes seminal themes and the primary sources (reliquaries, the Peace of God, saints, and settings). After these two prefatory chapters, each of the three analytical chapters discusses different collaborations and directions of movement in the sources. Chapter 3 addresses the movements of relics and the populace on the road to and at Peace of God councils, portraying a sanctified version of the Peace. Chapter 4 examines movements of roving dangerous men-at-arms and retaliating saints wreaking vengeance away from the relics’ presence, showing that the anomalous movements of wicked men warranted reciprocal, similar movements on the part of vengeful saints. Chapter 5 recounts movements of all social groups drawn toward the saints’ shrines by their spiritual magnetism in hopes of experiencing transformation. Overall, the descriptions of movement included in the three miracle collections were meant to establish social norms of movement. Therefore, this dissertation concludes that movements of relics, with relics, and for relics were the most acceptable movements in these three miracle texts and that all other movements were subject to scrutiny.
Behavioural and Neural Correlates of Attention Bias to Infant Cues as Predictors of Parenting from Pregnancy to Postpartum
Keywords: attention bias
electroencephalography
infant cues
maternal sensitivity
mother-infant bonding
pregnancy

Infant cues (e.g., faces and vocalizations) constitute powerful signals that bias adult attention. Research on the neuroanatomy of parenting primarily examines neural biases to passive viewing of or listening to target infant cues, with enhanced activations to infant versus non–infant cues seen in parents compared to non–parents. In contrast, behavioural paradigms present infant cues as task–irrelevant distractors during conflict tasks. While these studies suggest that infant cues capture attention and disrupt executive control, investigations into more specific types of neurocognitive processes affected by this bias are limited. Importantly, both neuroimaging and behavioural research suggests that heightened bias to infant cues is linked to more sensitive mothering. However, this work is largely specific to the postpartum period and does not address potential changes in infant bias during the transition from pregnancy to motherhood. Using event–related potentials (ERPs) one objective of this thesis was to establish how attention bias to distractor infant vocal cues modulates adult cognition and cortical activity (P2, N450) during a conflict task (Stroop) in non–parents (Study 1) and in pregnant mothers and non–mothers (Study 2). Whether variations in this bias during pregnancy predict the strength of the mother–infant bond was additionally addressed (Study 2). Subsequently, Study 3 determined how attention bias to distractor infant facial cues influences cognition and face–sensitive encoding (N170) during a conflict task (go/no go) in pregnant mothers and non–mothers, and whether a heightened bias during pregnancy predicts sensitive mother–infant interactions. A final objective was to explore prenatal to postnatal changes in cortical responses to target infant and adult facial cues (P1, P2, N170, LPP) and determine whether mothers’ individual differences in cortical change predict the strength of the mother–infant bond (Study 4). Overall, this thesis represents a step forward in identifying the mechanisms related to parenting.