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The Philosophy of Reconstructions of Quantum Theory: Axiomatization, Reformulation, and Explanation
No full textThe quantum reconstruction programme is a novel research program in theoretical physics aimed at deriving the key features of quantum mechanics from fundamental physical postulates. Unlike standard interpretations of quantum theory, which take the Hilbert space formalism at face value, quantum reconstructions seek to derive this formalism from axiomatic principles. Reconstructions represent a new shift in foundations of physics away from interpreting quantum theory and towards understanding its foundational origins. The reconstruction programme has been a major focus of research in physics, beginning with Hardy (2001)’s “Quantum Theory from Five Reasonable Axioms.” However, the quantum reconstruction programme has been met with very little interest in philosophy. The goal of this project is to situate the quantum reconstruction programme in a broader philosophical context, investigating themes such as scientific methodology, explanation, the applicability of mathematics to physical theories, and theory exploration and development in the philosophy of science.
I argue that reconstructions demonstrate a contemporary application of axiomatization with significant points of continuity to historical axiomatizations. I also argue that we should best understand reconstructions as provisional, practical representations of quantum theory that are conducive to theory exploration and development. Further, I contend that reconstructions function as alternative formulations of quantum theory, which is methodologically advantageous. I discuss Bokulich (2019)’s “Losing the Forest for the Ψ: Beyond the Wavefunction Hegemony” which argues that the existence of alternative formulations of quantum theory undermines our ability to literally interpret a single formulation. I argue that Bokulich (2019)’s conclusions further support the reconstructionist’s rejection of the standard interpretative project. I also argue that reconstructionists have gone beyond Bokulich (2019)’s insistence on the consideration of alternative formulations to develop a methodology that systematically constructs alternative formulations of quantum theory. Additionally, I argue that reconstructions of quantum theory are genuinely explanatory as they answer Wheeler (1971)’s “Why the quantum?” question. I contend that reconstructions are explanatory in the same spirit as Bokulich (2016)’s account of explanation in “Fiction As a Vehicle for Truth: Moving Beyond the Ontic Conception” which focuses on patterns of counterfactual dependence that correctly capture underlying dynamics. However, in order to accommodate the reconstruction case, I expand Bokulich’s account to consider theories and models as well as representations that are neither fictional nor literal interpretations. Thus, I offer an account of explanation in the reconstruction programme that is noncausal and non–interventionist, utilizing w–questions a la Woodward (2003). I conclude that reconstructions of quantum theory give us genuine insight into the structure of quantum theory via the generalized physical principles which carry physical content
Grafting of Starch Nanoparticles with Polymers
No full textAs a biocompatible and biodegradable polysaccharide, starch has sparked significant interest for various industrial applications, but its poor mechanical properties limit its uses without chemical or physical modification. The work reported herein concerns the development of synthetic techniques to modify starch by graft polymerization via cerium (IV) activation.
Starch nanoparticles (SNPs) were modified with acrylic acid (AA) in water under acidic conditions via activation with cerium (IV) in combination with potassium persulfate (KPS). The reactions were conducted with either the as-supplied SNPs containing glyoxal, or after purification (without glyoxal), for different target molar substitution (MS) values. A novel purification protocol using methanol extraction and centrifugation was implemented to purify the samples. This method proved to be selective to isolate the poly(acrylic acid) (PAA) homopolymer contaminant from the starch-g-PAA copolymer, and more reliable than the gravimetric analysis methods reported in the literature. The starch-g-PAA copolymers were characterized by dynamic light scattering (DLS), and degradation of the starch substrate allowed the determination of the molar mass of the PAA side chains via gel permeation chromatography (GPC) analysis.
In the presence of aldehydes the rate of polymerization of AA increased significantly (by > 37 %), and the highest grafting efficiencies were obtained for glyoxal and butyraldehyde. The combination of cerium (IV) with glyoxal and KPS resulted in the highest polymerization rate and grafting efficiency. Increasing the glyoxal concentration also increased the rate of monomer conversion and the grafting efficiency. The increased rate of polymerization provided further insight into the grafting mechanism, as it was discovered that esterification reactions between starch and PAA also contributed significantly to the grafting process, particularly at longer reaction times. In the presence of aldehydes, the production of large amounts of PAA homopolymer resulted in esterification dominating the grafting process. Model reactions involving direct coupling of linear PAA samples with starch were investigated. All the reactions were characterized by high coupling efficiencies for a target MS = 3, and higher molar mass PAA samples (30 and 250 kDa) coupled faster than a lower molar mass sample (1.8 kDa), as expected in terms of reaction probabilities. The importance of esterification was also confirmed with model reactions using 2-hydroxyethyl acrylate, a monomer not containing a free carboxylic acid functional group, which yielded notably lower grafting efficiencies.
Overall, the grafting mechanism for starch and acrylic acid promoted by cerium (IV) therefore appears more complex than described previously, particularly in the presence of aldehydes: The high overall grafting efficiencies observed result from two distinct reactions occurring concurrently, namely grafting via cerium (IV) activation, as well as the esterification of free PAA homopolymer. The additional insight gained for these reactions was possible due to the newly developed purification protocol, used in combination with NMR spectroscopy analysis, which provided detailed composition data for the different sample fractions and a better understanding of the grafting mechanism.
Furthermore, preliminary results were obtained for starch modified with acrylonitrile and cerium (IV) in water under acidic conditions. Extraction of the polyacrylonitrile (PAN) homopolymer component was more difficult due to its solubility characteristics, but mixtures of dimethylacetamide with water (up to 10 % by volume) provided consistent results. High grafting efficiencies (> 67 %) were obtained for the starch-g-PAN copolymers, and characterization of the products was performed by Fourier transform-infrared spectroscopy, DLS, GPC, and atomic force spectroscopy. Hydrolysis of the starch substrate yielded hollow PAN shells or spheres, depending on the MS level of the copolymer, with potential applications in nanoencapsulation
A Characterization Scheme to Assess the Heating Performance of Developed Biological Solders for Laser Tissue Welding
No full textLaser tissue welding (LTW) is an alternative, suture-less wound closure technique. It involves positioning opposite wound edges in close proximity, followed by near-infrared laser irradiation until a temperature of approximately 60°C is reached. At this temperature, protein interdigitation and tissue coagulation occur, resulting in a re-homogenized continuous tissue matrix. Heat localization at the site of joining is essential to produce strong tissue bonds while minimizing thermal damage to the surrounding region. One method to achieve heat localization is through the administration of photothermally responsive biological solders (biosolders).
For the presented work, nanocomposite gels (NCGs) were developed as biosolders. They were prepared by dissolving hyaluronic acid (HA) and guar gum (GG) to a final polymeric concentration of 3% w/v in aqueous solutions with up to 1.4 nM gold nanorods (GNRs). The addition of GG was found to stabilize GNR dispersion through the gel and enhance their heat generation under laser irradiation. To assess the clinical relevance of the gel formulations, a simplified photothermal characterization scheme was developed. The method relies on a custom-built measurement system that can confine gel samples to clinically relevant thicknesses as thin as 100 μm. This setup was used to measure temperature increases (ΔT) of gel specimens with and without GNRs, with a repeatability of ΔT = 0.123°C.
A plasmonic heat amplification factor, ξ , is proposed as a new safety metric for assessing the clinical relevance of a biosolder formulation. It is defined as the ratio of ΔT for an NCG by ΔT of its corresponding control gel. A subset of the developed NCG formulations were found to possess ξ >1.3, indicating their thermal suitability for safe LTW. The presented method aims to establish a new standard foundation for the temperature monitoring of laser-irradiated gels in isolation, allowing for the cost-effective screening of preliminary NCG formulations for eventual administration to tissue models.
The developed photothermal characterization system was further used to determine the photothermal conversion efficiency, or fraction of heating power per absorbed light, η, of developed NCGs. Although a standardized method for determining η in liquid photothermal solutions has been previously proposed, it is unsuitable for semisolid materials such as viscous gels that are considered for medical applications like LTW. As such, a simple, yet robust approach is proposed for estimating η of viscous photothermal gels via the direct determination of thermal conductance. The method allows for more confident reports of η for optimizing the photothermal response of photoresponsive materials
FPGA-Accelerated Deep Learning for Denoising Low-Dose PET Scans
No full textPositron Emission Tomography (PET) is an essential imaging technique used in clinical settings for diagnosing conditions such as cancer and neurological disorders; however, its dependence on radiopharmaceuticals poses potential radiation exposure risks. Lowering the administered dose can help improve patient safety but results in imagery with reduced Signal-to-Noise Ratio (SNR), impacting diagnostic accuracy. The trade-off between minimizing radiation exposure and maintaining image quality remains a key challenge in PET imaging. Recently, deep learning-based denoising techniques, such as Denoising Convolutional Neural Network (DnCNN), have proven effective in restoring noisy images to standard quality. Traditional implementations relying on CPUs and GPUs are often constrained by high power consumption and hardware overhead, limiting feasibility in edge-compute applications. To address these challenges, this thesis explores FPGA-based acceleration for PET image denoising. A dataset is constructed using PET scans from 10 Alzheimer’s disease patients from the ADNI database, with only 0.5% of the original radiotracer dose used. A software-based implementation is developed using a proposed U-Net-like architecture, then ported to an FPGA using OpenVINO and Intel’s FPGA AI Suite for hardware emulation. Experimental results show the FPGA implementation offers a 77% improvement in performance-to-watt ratio compared to the GPU-based solution, and a 2x reduction in latency compared to the CPU-based solution
Health Care Costs Associated with Minor Ailments and Cost Minimization Analysis of Pharmacists Prescribing for Minor Ailments in Ontario, Canada
No full textBackground: Pharmacists in Ontario, Canada are now able to prescribe for certain minor ailments. Minor ailments are defined as health conditions that can be managed with minimal treatment and/or self-care strategies. Economic evaluations with costs and burden associated with minor ailment conditions using Ontario health administrative data are needed to accurately evaluate the economic impact of the pharmacist prescribing for minor ailment (PPMA) program.
Objectives: The objectives of this research thesis are to: 1) assess the baseline characteristics, mean health care costs, and predictors of health care costs of minor ailment cases using a retrospective analysis of Ontario health administrative data for patients presenting with minor ailment conditions and 2) perform a cost-minimization analysis to determine the economic impact of a remunerated program for PPMA compared with usual care.
Methodology: First, using Ontario health administrative data from ICES, baseline characteristics, mean health care costs, and predictors of health care costs were determined for patients diagnosed with the 16 studied minor ailments. Second, a decision-analytic model was implemented to perform the cost-minimization analysis for the minor ailments. Two prescribing strategies were considered in this analysis: PPMA and usual care. In the PPMA strategy, patients have the option of either seeking care from a community pharmacist or a physician. In the usual care model, all patients seek care from physicians. Probabilities and costs used in the model were derived from mostly Ontario health administrative data, literature, or expert opinion when there was insufficient literature. This analysis used a public payer perspective and outcomes were expressed in costs in 2019 Canadian dollars.
Results: Analysis of Ontario health administrative data from ICES identified that the minor ailments with the highest number of unique patients billed were musculoskeletal sprains and strains (8,099,393; 24%), gastroesophageal reflux disease (5,822,495; 17%), dermatitis (5,649,829; 17%), urinary tract infection (3,356,887; 10%), and insect bites and urticaria (2,699,684; 8%). Health care costs varied by minor ailment and cost category, with older age, lower income quintiles, urban residency, and comorbidities as predictors of higher total health care costs. Cost-minimization analyses from a public payer perspective provided evidence that implementing a PPMA program for the studied minor ailments could yield cost savings for the Ontario government compared to the usual care model, with savings ranging from 77.38 per patient. One-way sensitivity analyses showed that results were most sensitive to the likelihood of patients receiving care from a pharmacist rather than a physician. In probabilistic sensitivity analyses, the PPMA model proved cost-saving in 100% of the simulations for all 16 minor ailments studied.
Conclusion: The results of the thesis research identified the baseline characteristics, health care cost burden, and predictors of total health care costs for patients presenting with minor ailments using Ontario health administrative data. In addition, the cost-minimization analyses conducted from a public payer perspective provided evidence that implementing a PPMA program provided cost-savings for the Ontario government when compared to the usual care model for the studied minor ailments. The results of this research can continue to help shape implementation strategies of a PPMA program in Ontario, Canada
“AnnoTools”: Extending AnnoTree and AnnoView for Database-Wide Genome Annotation, Visualization, and Comparison
No full textGenomic analysis has revolutionized our understanding of the biology and evolutionary history of bacterial and archaeal microorganisms, leading to numerous applications in biotechnology, medicine, and environmental sciences. One of the fundamental aspects of genomic analysis is protein functional annotation, which involves assigning biological functions to protein-coding sequences identified within genomes. These annotations are widely used to support analyses, such as examining gene or function distributions across the tree of life and comparing gene neighborhoods across taxa. By combining these analyses, researchers can comprehensively explore gene functions and the mechanisms of given genes or gene clusters. In this thesis, I will introduce a pipeline that supports genomic analysis. The project consists of three parts: data annotation, visualization, and the language model.
The first part of the pipeline is the generation of protein function annotations. Raw protein sequence data is downloaded from the Genome Taxonomy Database (GTDB) and submitted to two tools: Kofamscan and DIAMOND. Kofamscan assigns KEGG ORTHOLOGY IDs to each input sequence, while DIAMOND assigns Uniref IDs, which are then mapped to InterPro IDs. Combining these IDs provides comprehensive and reliable annotations. The data is filtered for quality and stored on a remote server as an annotation database for further analysis.
The second part of the pipeline involves updating two user-friendly, web-based visualization tools, AnnoTree and AnnoView, which utilize the annotation database. AnnoTree displays the distribution and taxonomy of different protein annotations across GTDB using a tree of life representation, offering insights into biological and evolutionary patterns through species phylogenies and supporting genome-wide co-occurrence analysis. AnnoView focuses on comparing and exploring gene neighborhoods, identifying functionally related genes clustered together in genomes as "gene clusters," thus emphasizing window-based co-occurrence analysis. The new annotation database not only provides more comprehensive and accurate annotations, enhancing the databases that both visualization tools rely on, but also extends their functionalities for fast data retrieval and new features.
The last part of the pipeline involves the application of the Word2Vec language model, which treats genome contigs as sentences in natural language and trains the model using the annotation database. After training, the updated model can encode each annotation from a specific protein family into high-dimensional vectors with continuous number, allowing researchers to explore annotations that share similar genomic contexts. This allows protein functions prediction based on this comparative gene neighborhood analysis.
Finally, I will use one protein domain in the Type VI Secretion System (T6SS) as a case study. T6SS is a cell envelope-spanning machine that translocates toxic effector proteins into eukaryotic and prokaryotic cells. Besides the conserved essential core components, there are various effector and accessory proteins in the system. Some proteins are annotated as Domains of Unknown Function (DUF) and are poorly explored. In this case, I will focus on PF20598 (DUF6795), which shares a similar genomic context with one of the T6SS proteins. Using the visualization tools AnnoTree and AnnoView, I will demonstrate that this DUF is part of the T6SS cluster, supporting the hypothesis that it may function as an adaptor protein in T6SS.
In summary, the AnnoTools pipeline integrates all components to enhance comparative genomic analysis with a large-scale annotation database. The user-friendly web-based tools enable researchers to visualize data both genome-wide and at a window-based scale. The ultimate goal of this thesis is to provide researchers with a comprehensive and easy-to-use method for predicting functions of genes or gene clusters of interest
Graph-Based Autonomous Vehicle Motion Planning Using Game Theory
No full textAutonomous driving technologies promise safer, more efficient, environmentally friendly,
and accessible mobility systems. To realize these benefits, advanced planning and control
algorithms are crucial. Given the interactions between Autonomous Vehicles (AVs) and
human-driven vehicles in mixed traffic, as well as with pedestrians and cyclists, the deci-
sions and trajectories of AVs significantly impact other road users. Therefore, considering
these interactions is vital for achieving safe and efficient AV driving behavior.
In automated driving, the basic idea is to traverse from point A to point B au-
tonomously. This state space is often represented as an occupancy grid or lattice that
depicts where objects are in the environment. From the planning point of view, a path
can be set by implementing graph-based algorithms that visit different states in the grid,
solving the path-planning problem. The graph-based algorithms treat the static and dy-
namic objects/actors detected by the perception system as static impassable areas inside
their costmap and fail to capture future actions. Considering the intention of road users
results in a more reliable path and control for the AV to follow.
Game theory is a framework for addressing problems involving multiple decision-making
agents, where each agent’s decision is influenced by the choices of others. The appropriate
solution depends on the game’s structure and the relationships between players. In this
work, we explore Nash, Stackelberg, and Bayesian equilibria as solutions to the interactions
between AV and road users. Nash equilibrium ensures that all participants in the game
are treated equally, such that no player can reduce their cost by changing their strategy
unilaterally. Stackelberg equilibrium considers a hierarchical structure, where leaders and
followers exist among the players. Leaders commit to a strategy first, and followers react
optimally to this decision. Bayesian equilibrium incorporates uncertainty and incomplete
information, where players have beliefs about the types and strategies of other players. By
investigating these equilibrium concepts, we aim to develop optimal strategies for both AV
and road users, ensuring effective and efficient motion planning for AV.
In this research, a graph-based algorithm will be integrated with game theory to plan the
motion of AV, considering the future actions and decisions of other road users. Numerical
and experimental simulation results demonstrate that the proposed framework effectively
manages interactions between AV and other road users, such as human-driven vehicles or
pedestrians, across various scenarios
Heartworks: Feminist Encounters with the Gendered Selves of Young Divorcées
No full textCompelled by personal connections to women in my life experiencing the gendered complexities of divorce, this study explores how young, divorced women (in their 20s and 30s), without children, were influenced by different gendered ideologies—including femininity, coupledom, and pronatalism—along with other social, cultural, and relational contexts and pressures, all of which can be variously experienced, reproduced, and resisted within leisure. Aligning feminist theory with narrative inquiry, I conducted one-to-one interviews and group interviews with 12 young, divorced women. I represented the findings using Creative Analytic Practice through a variety of literary forms, including monologues, social media posts, and researcher field notes. The findings elucidate women’s experiences within a framework I conceptualize as the Heartworks, which details the heart-work of women’s divorce processes and the feminist research praxis it fosters. Collectively, the findings highlight the challenges women faced as they navigated the “shattering” of their married selves and engaged in “re-creating” distinct post-divorce selves against the sociocultural backdrop of gendered ideologies. This research expands current conceptualizations of identity, grief, transition, and transformation. It also adds complexity to our thinking about women’s relationships as a shifting cultural nexus where leisure contexts both confine and expand notions of femininity and love. As a feminist social justice project, this research exposes the marginalization and stigmatization faced by young, divorced women and shares new understandings of their complex, lived experiences, including possibilities for resisting and re-creating limiting narratives of women’s divorce through counter-narratives of (re)claimed agency, solidarity, and empowerment
A Graph Neural Network Based Approach for Predicting Wildfire Burned Areas
No full textWildfires annually cause substantial economic and environmental losses and has a detrimental impact on human lives and health due to the release of their harmful byproducts. Moreover, wildfire incidents have exhibited an alarming surge in frequency as well as severity in recent years due to increased urbanization near forested areas coupled with climate change, highlighting the need for advanced technologies to predict wildfire behavior in advance and mitigate its impact. In recent years, the enormous strides in machine learning research coupled with the increased availability of wildfire data through various sources such as remote sensing and the increased availability of computational resources have fueled the rise of data-driven approaches across all stages of wildfire management. Despite the growing adoption of machine learning-driven approaches in wildfire mitigation, the primary focus has been on analyzing historical patterns and identifying the causes leading to wildfire patterns rather than predicting wildfire behavior. The prediction of wildfire behavior over time, such as the burned area has been largely underexplored. This study aims to address this gap by advancing data-driven methods for predicting wildfire behavior during the active fire stage and aiding in resource allocation efforts.
This study adopts a Graph Neural Network based framework for predicting the burned area resulting from a wildfire ignition. While CNN-based architectures have been widely employed to model wildfire behavior, including spread prediction, as a semantic segmentation task, these architectures impose specific limitations on geospatial data due to their reliance on fixed-size inputs and local receptive fields. Graph Neural Network (GNNs), have shown success in capturing the long-range dependencies and irregular-sized inputs inherent in geospatial data, such as wildfires, making them a viable alternative to CNNs. To this end, a GNN-based approach is adopted to model wildfire burned area prediction. A framework is developed to represent spatial wildfire data and its influencing factors as homogeneous graphs followed by the development of three distinct GNN models based on different message-passing mechanisms to process the graph-structured data. The results obtained through various experiments illustrate the efficacy of Graph Neural Networks in modeling wildfire behavior. In terms of Precision, most GNN models outperform the segmentation models, with the highest achieving a score of 0.4536. For AUROC, all GNN models demonstrate superior performance, reaching a maximum of 0.9377. Based on AUPRC, the Graph Convolutional Network (GCN) model surpasses all others, including segmentation models, with a top score of 0.4787. These findings underscore the potential of Graph Neural Networks (GNNs) as a powerful tool for wildfire behavior modeling and supporting resource allocation initiatives
Effect of substrate topography on human vascular smooth muscle cell proliferation and phenotype change
No full textCardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, with vascular occlusion being a primary contributor. Bypass grafting is a common surgical intervention to restore blood flow, traditionally using autologous grafts such as saphenous veins and internal thoracic arteries. However, the limited availability and invasive harvesting process of autologous grafts have prompted the development of synthetic small-diameter vascular grafts (sSDVGs) as alternatives. Despite advancements, the clinical efficacy of sSDVGs remains unsatisfactory due to high rates of thrombotic occlusion, intimal hyperplasia (IH), and restenosis, primarily caused by dysregulated vascular smooth muscle cell (VSMC) behavior.
VSMCs play a critical role in the progression of IH through their proliferation, migration, and phenotypic plasticity following vascular injury. While extensive studies have explored the influence of substrate topography on endothelial cell (EC) response, the effects on VSMCs remain underexplored. This study investigates the hypothesis that substrate topographies with varying geometries, isotropy, and sizes can differentially regulate VSMC behavior, potentially mitigating IH and improving the functionality of sSDVGs.
To test this hypothesis, a 16-pattern multiarchitecture (MARC) chip was employed to screen various surface patterns for their ability to modulate VSMC phenotype. Five promising patterns were selected and individually fabricated on polydimethylsiloxane (PDMS) substrates for further evaluation. The influence of these topographies on VSMC behavior was assessed under normal and platelet-derived growth factor (PDGF)-stimulated conditions by analyzing protein markers associated with VSMC phenotypic states, including α-smooth muscle actin (α-SMA), phosphorylated myosin light chain kinase (pMLCK), F-actin, desmin, vimentin, phosphorylated focal adhesion kinase (pFAK), and yes associated protein (YAP). Among the tested patterns, the 2μm grating emerged as the most effective in inducing a contractile VSMC phenotype. VSMCs cultured on this pattern exhibited reduced proliferation, an elongated spindle-like morphology, and increased expression of muscle-specific proteins, irrespective of PDGF presence. Conversely, VSMCs on the 1.8μm convex microlens and unpatterned substrates showed higher proliferation rates and a diminished contractile phenotype. Remarkably, the beneficial effects of the 2 μm grating pattern were retained when incorporated into a fucoidan-modified polyvinyl alcohol (PVA) hydrogel, a biomaterial known to support EC adhesion and exhibit low thrombogenicity. The 2μm grating suppressed PDGF-induced proliferation while promoting a contractile phenotype and enhancing directional motility. Mechanistic studies revealed elevated pMLCK expression, increased cytoplasmic localization of YAP, and enhanced focal adhesion maturation on 2μm gratings, supporting contractility and reducing proliferation. In contrast, unpatterned and 1.8μm convex lens substrates induced nuclear YAP localization and reduced pMLCK expression, favoring a proliferative phenotype.
This study introduces a promising strategy for regulating VSMC behavior through substrate topography, leveraging biophysical cues to promote a contractile phenotype while suppressing proliferation. By incorporating these insights into the design of biomimetic graft surfaces, this approach holds significant potential to address the limitations of sSDVGs, reduce complications such as IH, and improve long-term graft patency. Furthermore, the integration of topographical and biochemical modifications into PVA-based hydrogels represents an innovative avenue for the development of next-generation vascular grafts that combine mechanical strength with enhanced biological functionality. This work paves the way for advancing sSDVGs toward better clinical outcomes, reduced graft failure, and improved patient prognosis