VictorJuiz/thesis huggingface dataset:
Character Education Implementation in the Financial Literacy-Based Classroom Economy
Keywords: case study
character education
classroom economy
elementary education
elementary teacher
financial literacy

This study revolves around examining a classroom structure that is based upon financial literacy in order to teach character education to elementary school students in Grades 4-6. This study provides a rich description of one possible structure, the financial literacy-based classroom, as a pedagogical approach to implement the teaching of both financial literacy and character education simultaneously. The research question is: How can an elementary classroom structured around a financial literacy-based “classroom economy” framework facilitate character education instruction across the curriculum? Through classroom observations and teacher interviews, the study provides a detailed description of the process of two teachers using a financial literacy-based classroom economy to teach character education. Both teachers were observed making use of the classroom economy to engage students in conversations on the topic of character by making connections between classroom experiences and the world outside the classrooms. They integrated the classroom economy throughout the curriculum and used it to engage students in character-related discussions. Although the encountered struggles maintaining the economy due to their busy schedules, they both indicated that their students seemed motivated by the classroom economy when it was used in the classroom.
Chloride Dysregulation and Impaired Gamma-Amino Butyric Acid Signaling in the Basal Ganglia in Huntington's Disease
Keywords: GABA
KCC2
Synaptic inhibition

Huntington’s disease (HD) is an inherited neurodegenerative disorder caused by a cytosine-adenine-guanine (CAG) trinucleotide repeat expansion in Huntingtin (HTT) gene. The primary sites of degeneration are a group of nuclei called the basal ganglia which are essential for motor planning and voluntary movement. The basal ganglia contains two major pathways: the direct and indirect, which promote movement and inhibit unwanted movement, respectively. Neurons that form the beginnings of the indirect pathway are known to degenerate earlier in HD, though the cause of this enhanced susceptibility remains unclear. Accumulating evidence suggests that impaired synaptic inhibition within the basal ganglia circuitry may underlie this specific pattern of degeneration. Synaptic inhibition in the mature brain is largely mediated through gamma-amino butyric acid (GABA), which exerts fast hyperpolarizing inhibition by binding to chloride (Cl-)-permeable GABAA receptors (GABAARs). GABAergic inhibition requires low intracellular Cl- concentration ([Cl-]i), which is maintained by the antagonistic actions of Cl--exporting potassium (K+)- Cl- cotransporter 2 (KCC2) and Cl- -importing sodium (Na+)- K+ - Cl- cotransport 1 (NKCC1). However, when KCC2 function is reduced and/or NKCC1activity increases, [Cl-]i increases, consequently weakening synaptic inhibition. In my PhD thesis, I present three novel pieces of evidence that collectively demonstrate that Cl- dysregulation and altered GABAergic inhibition are key contributing factors to the development of motor deficits in two mouse models of HD. First, NKCC1 expression is increased and KCC2 function is reduced in the striatum, the major input structure of the basal ganglia. Second, KCC2 function is differentially altered in the direct and indirect pathways and GABAergic inhibition is switched to GABA-mediated excitation in the output structure of the indirect pathway. Finally, pharmacologically reducing pathological increases [Cl-]i delays motor behavioral deficits of R6/2 mice. Taken together, my PhD work not only significantly increases our understanding of the increased susceptibility of the indirect pathway but opens new possibilities where Cl- cotransporters may serve as therapeutic targets in the treatment of HD.
Reaction-Transport Model of Pitting Corrosion Relevant to Nuclear Steam Generators
Keywords: Multiple Pit Propagation
Pitting Corrosion
Reaction Transport Model
Stainless Materials

The pitting mechanism of nickel-based alloys relevant to the nuclear steam generator is complex and unpredictable, particularly without the aid of a pitting model. Thus, the present research focuses on developing a reaction-transport model to study the propagation stage of corrosion pits. A one-dimensional reaction-transport model is constructed to study the effect of both cation complexation and concentration-dependent diffusivity on the IR potential drop at a steady state and with an anodic limiting current density. When there is no cation complexation, the B value from Galvele’s pitting potential equation E_pit = A-B log(C_bulk) remains at 59 mV in NaCl even when the diffusivities are functions of the chloride concentration. The formation of chromium complexes could increase the B value up to 100 mV, in agreement with experiments, assuming dissolved chromiums can make cationic chloro-complexes. A comprehensive mass transport model coupled with electrochemical tests using the artificial pit technique is developed to study the diffusion-controlled dissolution of metal in a corrosion pit from a more advanced point of view when migration and diffusion are included in the mathematical model. At above 2.5 M NaCl, the conventional diffusion-only model gives reasonably accurate results, but the effect of Na+ at the bottom of the pit on the anodic limiting current density (i_L) and the metal’s solubility should be considered. For iron dissolution in NaCl, analytical results show the effect of critical factors on i_L. Finally, a two-dimensional charge transport model is developed to simulate the propagation stage of multiple corrosion pits and the interactions between these pits in different simulated environments. In this model, the moving of the corroding surface is tracked implicitly using a phase field method and based on the dissolution kinetics at the surface. The results of this work show the formation of the perforated pit cover and the merging between pits under galvanostatic conditions without any remeshing scheme, which is an advancement in the 2-D pitting model. The proposed model can be a valuable tool for studying the evolution of pit morphology in more realistic conditions when multiple pits are present.
Theoretical Modeling and Optimization of Thermal Transport in Thermal Insulation Foams
Keywords: Heat transfer
Modeling
Optimization
Plastic foam
Thermal conductivity
Thermal insulation

Superinsulation has been one of the most challenging topics because of global concerns in sustainability and energy. However, the practical underpinning needed to design the optimal cellular structure and economically manufacture high-performance thermal insulation is still challenging due to the complication of thermal transport in the foam structure involving intermolecular, interfacial, and radiation-matter interactions. Therefore, this Ph.D. thesis developed theoretical modeling for the thermal transport in thermal insulation foams and demonstrated the influence of cellular structure and material properties on the thermal insulation property of closed-cell and reticulated open-cell foams. In particular, this research also demonstrated how an optimal foam structure and the addition of radiation-blocking carbonaceous nanoparticles effectively suppress the overall heat transfer. The research findings demonstrated that the size of struts and cell walls in the foam structure are sensitive to the foam’s expansion and cell size. Heat conduction in the solid depends on the structural tortuosity. Conduction in the gas is substantially reduced in nanocellular foams when the mobility of gas molecules is limited due to the Knudsen effect. The radiative heat transfer depends on the opacity of each cell and the number density of cells. An optimal foam structure minimizes overall heat transfer, and a material with low thermal conductivity and high radiative absorption is preferable. This research also demonstrated the impact of carbonaceous nanoparticles in the polymer at a small content on the substantially reduced thermal radiation while minimizing the increased heat conduction. Adding such nanoparticles can further reduce the optimal thermal conductivity and increase the optimal volume expansion ratio for manufacturing high-performance insulation with less polymer. In addition, thermal transport in foams is sensitive to cellular morphology. This thesis also explained the thermal transport in nanocellular open-cell foams with excellent thermal insulation performance. Furthermore, the generation of anisotropic foams could reduce the heat transfer in the transverse direction and increases the heat transfer in the longitudinal direction, providing a unique controlled heat transfer. The models provided an in-depth understanding of the relationship between the structure and the thermal conductivity. It provides insights into the cellular structure design and material selection for thermal insulation.
Placebos—Their Nature and Ethical Implications
Keywords: Bioethics
Clinical Ethics
Philosophy of Medicine
Placebos
Research Ethics

Placebos have the potential to improve patient outcomes and promote our well-being. They are important to the methodology of clinical trials, and they disturb the boundaries of what counts as ‘real’ medicine. In this dissertation I present the Social Positioning Account of placebos wherein placebos are partially constituted by the social context in which they are administered, and furthermore have a distinctive normative profile. An intervention only counts as a placebo when the client perceives themselves to be in a treatment context, and placebos are held to a norm such that they are better the more they resemble an extant standard treatment. I argue that the current state of empirical literature does not support the assumption that there is a unique causal driver for placebo effects. Thus, the Social Positioning Account is causally neutral—it does not define placebos in terms of a particular class of causes or causal mechanisms.The ethics of placebo administration intersects with broader questions within bioethics. In research settings, placebos are particularly important in the running of Randomized Controlled Trials (RCTs). I therefore widen the scope of discussion and develop a framework for analyzing the ethical good standing of RCTs. I use that framework to examine a cluster of issues that arise in the running of RCTs, of which placebo administration is one. The placebo effect is also relevant to clinical practice; practitioners must balance the ethical obligation to disclose information to a patient with the assumed health risks or benefits consequent of that disclosure. Although withholding information can sometimes violate the duty to promote patient autonomy, I argue that there are surprising cases in which autonomy is promoted by withholding information and apply this insight to cases of placebo administration in clinical practice.
Robust Machine Learned Inter-atomic Potentials for Molecular Dynamics Simulations
Keywords: Interatomic potentials
Kernel approximation
Machine learning
Molecular dynamics
Random Fourier features
Uncertainity quantification

Molecular Dynamics (MD) simulations accelerated material discovery by performing property prediction at scales higher than Density functional theory (DFT). To make property prediction from MD simulations more robust, we propose an uncertainty quantification framework. The developed framework considers uncertainty in the parameters of the interatomic potential (IP) used to compute energy/forces between a set of atoms. We considered two distinct potentials namely AIREBO and Embedded atom method. The material of choice are graphene and aluminium. Sensitivity analysis was performed to understand the effects of IP parameter change on the final properties of interest. The sensitivity analysis showed that by varying the potential parameters as small as by 1%, the variance in the resulting mechanical properties was as high as 1e5. Basic properties such as elastic constant of graphene showed variation of 66% by changing AIREBO parameters only by 0.5%. Based on the sensitivity analysis results, a new robust version of each IP (AIREBO/EAM) was developed using Bayesian methodologies. Using samples from the resulting posterior distribution, MD simulations were performed and error bars for each property was obtained. The variance in the properties was within experimental tolerance exhibiting the reduction in sensitivity for both the material systems. It can be challenging to obtain a single classical IP to describe the interactions involved in multi-element systems. Various machine learning based potentials have been proposed to overcome this limitation however their computational cost can be high. The proposed model approximates the energy/forces using a linear combination of random features at a lower computational cost. This study provides results for three classes of materials, namely two-dimensional materials, metals, and semiconductors. Force and energy predictions made using the proposed method are in close agreement with density functional theory calculations, with training time that is 96% lower than standard kernel models. This study also develops IPs for High Entropy alloys based on random features. MD simulations for mechanical and thermal properties are within 7% of the corresponding DFT values. Complex interactions present at high temperatures were also correctly identified by the proposed IP.
Monitoring Hand Use and Hand Role of Stroke Survivors at Home Using Egocentric Video
Keywords: Machine Learning
Rehabilitation
Stroke
Wearable technology

Capturing hand function performance at home is essential to measuring the ultimate impact of novel interventions after stroke. Wearable technologies have been used to capture upper limb use ratios outside of clinical environments, however, wrist-worn devices measure arm use rather than hand use, and finger-worn devices may interfere with natural hand movement during activities of daily living. Therefore, an egocentric (first-person) camera was applied to capture natural hand movements of stroke survivors at home. In addition to hand use, the role of the affected hand in bimanual tasks, including stabilization and manipulation, provides distinct information about hand function performance and has not yet been investigated. Machine learning-based computer vision algorithms were used to detect hand-object interactions (hand use) and hand role after stroke from videos. In this thesis, the three objectives were to investigate the feasibility, validity, and user perspectives regarding the use of egocentric video to capture hand use and hand role of stroke survivors at home, respectively.Two datasets of recorded daily tasks from 24 stroke survivors at home and in a home simulation laboratory were collected and used in the studies. Hand-object interaction detection at home was feasible using a pre-trained deep learning model: the Hand Object Detector. As for hand role classification, other computer vision techniques that track finger movement may be helpful in the future. The measured hand use ratios from home-recorded videos had close to strong correlation with the Motor Activity Log (MAL) and demonstrated that egocentric video can capture hand function performance after stroke at home. Hand role ratios from the annotated bilateral tasks had very weak correlation with the MAL and negative correlations with the Fugl-Meyer Assessment – Upper Extremity and the Action Research Arm Test. Hand role ratio might capture different information about hand function than clinical upper limb assessments. Stroke survivors had high acceptance of using the camera to record daily routines at home and were willing to use it for rehabilitation purposes again. This thesis demonstrates the potential of using egocentric video with computer vision-based algorithms to measure hand function after stroke in the community.
The Autoinflammatory C188Y Mutation in Cdc42
Keywords: autoinflammation
Cdc42
inflammasomes
innate immunity
periodic fever
Rho GTPase

The autoinflammatory syndromes are rare genetic diseases manifesting as widespread inflammation in the absence of an infectious trigger. The study of these unique diseases has revealed causative mutations in genes that regulate innate immune signaling pathways, such as the tumor necrosis factor (TNF), interleukin-1 (IL-1b), and inflammasome systems. A patient with heterozygous C188Y mutation in Cdc42 was recently described, presenting in early childhood with rash, periodic fever, recurrent infections and extremely high IL-18. Cdc42 (cell division cycle 42) is an ubiquitous Rho-family GTPase that controls actin cytoskeleton rearrangements, cell polarity and migration. The C188Y mutation is located in the C-terminal tail of Cdc42, which is modified by prenyl lipidation and required for membrane insertion. Two other C-terminal mutants, R186C and *192C, have been reported in childhood autoinflammation, but the mechanism by which C188Y causes systemic disease has not been examined. Here, I report the first comprehensive study of the biochemical and immunological effects of the C188Y mutation in Cdc42. The C188Y mutation results in the mislocalization of the protein from the plasma membrane and cytosol to the nucleus. Cells expressing Cdc42-C188Y protein exhibit defects in filopodium formation, an important Cdc42-driven process. BioID proteomics of Cdc42-C188Y revealed loss of interaction with key actin effectors including IQGAP1, Toca-1, and IRSp53. Expression of Cdc42-C188Y in the monocytic cell line THP-1 did not affect NLRP3 or pyrin inflammasome activation. THP-1 cells expressing Cdc42-C188Y showed impaired actin dynamics, impaired differentiation into macrophage-like cells and reduced phagocytosis. Thus, the Cdc42 C188Y mutation likely disturbs actin organization in innate immune cells to drive immunodeficiency and subsequent autoinflammation.
Genomic architecture of the aging hematopoietic system
Keywords: Aging
Blood
Genomics

Hematopoiesis is a dynamic, yet tightly regulated process continuously replenishing populations of cells, including those with immune functions, that circulate in peripheral blood. Aging is associated with changes in hematopoiesis, resulting in altered composition and reduced function of immune cells, increasing risk of disease in the elderly. However, some elderly individuals remain healthy throughout their lifetime, which provides an opportunity to identify mechanisms that maintain healthy hematopoiesis. The underlying genomic architecture and molecular mechanisms that regulate hematopoiesis among the healthy aging population have yet to be studied comprehensively at the population level. Using genetic and functional genomic approaches, I utilized large population cohorts to identify genetic and transcriptomic factors associated with variation in blood aging. First, I investigated the impact of pleiotropic variants on blood and age-related phenotypes because they have the potential to simultaneously increase an individual’s risk for multiple phenotypes and chronic conditions. Out of more than 65,000 candidates, I identified six pleiotropic variants associated with diverse traits including blood cell counts, metabolic and cardiovascular disease, and anthropometric traits. I found that pleiotropic variants are more likely to be coding mutations and are highly deleterious yet remain at relatively common allele frequencies, supporting the mutation accumulation and antagonistic pleiotropy theories of aging. Next, I investigated the genomic architecture and mechanisms associated with variation in blood aging. I selected 400 individuals from the extremities of the age and immune health spectrums and performed single cell RNA sequencing on more than 500 000 cells, together with whole genome sequencing and chromatin profiling on bulk CD45+ cells to reveal genetic, epigenetic, and transcriptional factors underlying healthy immune aging. I show that sex and cell-type specific transcriptional signatures rather than cell composition differentiate individuals with healthy or unhealthy blood. We also identified 2565 cell-type specific expression quantitative trait loci associated with blood health enriched in innate cell types, suggesting a significant portion of the heritable component to blood aging mainly acts through innate cells. My research demonstrates how natural variation in healthy agers can help to uncover mechanisms that prevent or protect dysregulated immune function during aging.
Cardinality-Constrained Financial Optimization Problems
Keywords: Cardinality Constraints
End-to-End Learning
Factor Modelling
Mixed-Integer Programming
Portfolio Optimization

To construct an optimal portfolio, an investment process typically involves a risk model and an asset allocation. Whether it is which factors to include in the risk model or which assets to include in the portfolio, investors are constantly making a choice. If the selection is made a-priori, then the included subset may be suboptimal; another choice may lead to better outcomes. In order to integrate the selection problem in the overall optimization, cardinality constraints are imposed on the decision variables resulting in mixed-integer programs (MIPs). This thesis investigates various cardinality-constrained optimization problems throughout the investment process using different techniques and methods to deal with the computational complexities presented by MIPs and improve model performance. First, a cardinality-constrained risk model is examined. A unified, data-driven framework that produces general factor models is presented. Three sparse factor models of varying degrees of nonlinearity across feature selection and engineering are proposed. Next, cardinality-constrained asset allocation problems are studied. First, cardinality constraints are imposed on the risk parity (RP) portfolio optimization problem. Presented are the first two formulations that can be solved using off-the-shelf solvers to global optimality without the need of specialized algorithms or approximations. Second, index tracking is reframed to be more risk-centric, focusing on matching the risk make-up of the tracking portfolio to that of the benchmark’s. To that end, a novel risk allocation-based index tracking (RABIT) framework is proposed. While it is formulated as a non-convex MIP that is solvable to global optimality by the latest off-the-shelf solvers, an exploration-exploitation heuristic is developed to tackle larger scale problems. The final work presented integrates both aspects of the investment process. Using end-to-end learning, the performance of the downstream portfolio is taken into account during the risk model’s parameter estimation. This is the first work to implement the end-to-end learning framework to non-linear MIPs. A tight, semi-definite programming relaxation is placed as an implicit layer in a neural network that uses a loss function corresponding directly to the portfolio's realized performance quality.