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Modeling Ablation in Al/CuO Nanothermite Pellet Combustion
No full textNanothermites are reactive materials composed of metal and metal oxide nanoparticles, engineered to produce rapid exothermic reactions with high energy density. Aluminum and copper oxide (Al/CuO) are widely used due to their strong reactivity and ability to achieve efficient combustion, making them ideal for applications in energetic materials.
This thesis investigates the combustion of Al/CuO nanothermite pellets, with a particular focus on ablation—mass loss due to thermal degradation and chemical reactions. A numerical model is developed to capture key combustion characteristics, including flame speed, pressure distribution, and temperature response, while accounting for both thermal and mechanical effects across varying packing densities. Leveraging the Porous-material Analysis Toolbox based on OpenFOAM (PATO), this model simulates complex reactions, heat flux, and ablation dynamics, thereby addressing existing gaps in understanding ablation effects on nanothermite combustion and enhancing predictive capabilities for these materials.
In its simulation methodology, this study adapts PATO to model multiphase reactive materials, formulating governing equations for mass, momentum, and energy conservation. A two-dimensional axisymmetric model was selected to represent the cylindrical pellet structure. Key parameters, including material porosity, permeability, specific heat, and thermal conductivity, were tailored to the properties of nanothermite materials, while distinct boundary conditions were applied to simulate ignition and ablation phases. Simulations were conducted across various packing densities, with some models incorporating ablation-specific boundary conditions to capture changes in flame speed, peak pressure, and pellet stability under different conditions.
Results indicate that ablation intensity significantly influences the combustion dynamics, with higher intensities leading to reduced flame speeds and peak pressures. The study's findings highlight the potential for controlled nanothermite combustion by optimizing pellet packing density and ablation characteristics, offering applications in propulsion and micro-energetics
AI-Assisted Ultrasound-Guided High-Intensity Focused Ultrasound (USgHIFU) in Non-Invasive Surgery
No full textThis comprehensive study combines several innovative approaches to enhance the precision and efficacy of high-intensity focused ultrasound (HIFU) for cancer treatment. HIFU,
a non-invasive therapeutic technique, uses high-frequency ultrasound to ablate tumors, but requires careful planning due to its potential for collateral damage to healthy tissues. To
overcome these challenges, multiple methodologies are introduced.
By employing a Physics-Informed Neural Network (PINN) integrated with a realistic breast model, breast tumors are targeted with high precision. The model utilizes a bowlshaped acoustic transducer to focus ultrasound waves directly on the tumor, achieving intense localized heating. The PINN method efficiently solves partial differential equations in a mesh-free domain, providing high accuracy with significantly lower computational demands than traditional finite element methods (FEM).
This model is employed to understand the governing dynamics of the HIFU process, particularly the heat transfer mechanisms during sonication. Using machine learning techniques, the model simulates the absorption mechanism and temperature rise, validated through ex vivo experiments with bovine liver. This helps in accurately predicting and visualizing the effects of treatment, facilitating the development of personalized treatment strategies.
A novel deep learning-based optimization procedure is used for preoperative treatment planning. It determines optimal focal locations and sonication times for each ablation
session, ensuring minimal tissue over- or under-treatment. This algorithm has shown high potential in handling various HIFU presurgical plans and is especially effective in creating precise treatment plans based on patient-specific material properties.
Addressing the limitations of manual HIFU operations, a real-time, low-cost image segmentation framework based on the Swin-Unet architecture is proposed. This system
is trained and tested on B-mode imaging simulations and real images from HIFU-treated chicken breast, demonstrating high efficiency in lesion segmentation and offering potential for monitored, automated HIFU therapy.
By integrating these diverse approaches, the study not only enhances the thermal effects of HIFU but also offers a framework for safer, more precise, and individually tailored
cancer treatments. The combined use of advanced simulation models, deep learning approaches, and innovative technologies aims to significantly improve the therapeutic outcomes of HIFU, making it a more viable option for cancer treatment with reduced risks and enhanced efficacy
Microbial ecology of nitrification in engineered water treatment systems
No full textNitrification is performed primarily by chemolithoautotrophic microorganisms and is important for nitrogen transformation in aquatic environments. The understanding of nitrification has evolved over the years from a previously understood two-step process mediated by ammonia-oxidizing bacteria (AOB) and ammonia-oxidizing archaea (AOA) that perform ammonia oxidation to nitrite, and nitrite-oxidizing bacteria (NOB) that perform nitrite oxidation to nitrate. More recent research has revealed the existence of complete ammonia-oxidizing (“comammox” or CMX) bacteria from the genus Nitrospira that are capable of oxidizing ammonia to nitrate. With three groups of ammonia oxidizers often existing in the same environment, research is needed to understand the microbial ecology of nitrifying communities. These ammonia oxidizers play important roles in engineered systems, including wastewater treatment plants (WWTP) and aquarium biofilters, where they transform ammonia waste (NH₃/NH₄⁺) to less toxic nitrate (NO₃⁻) via nitrite (NO₂⁻).
Prior to the discovery of comammox Nitrospira, previous research revealed that AOA dominated over AOB in freshwater aquarium biofilters. In Chapter 2, aquarium biofilter microbial communities were profiled and the abundance of all three known ammonia oxidizers were quantified using 16S rRNA gene sequencing and quantitative PCR (qPCR), respectively. Biofilter and water samples were each collected from representative residential and commercial freshwater and saltwater aquariums. Distinct biofilter microbial communities were associated with freshwater and saltwater biofilters. Comammox Nitrospira amoA genes were detected in all 38 freshwater biofilter samples and dominant in 30, whereas AOA were present in 35 freshwater biofilter samples and only dominant in 7 of them. The AOB were at relatively low abundance within biofilters, except for the aquarium with the highest ammonia concentration. For saltwater biofilters, AOA or AOB were differentially abundant, with no comammox Nitrospira detected. Additional sequencing of Nitrospira amoA genes revealed differential distributions, suggesting niche adaptation based on water chemistry (e.g., ammonia, carbonate hardness, and alkalinity). Network analysis of freshwater microbial communities demonstrated positive correlations between nitrifiers and heterotrophs, suggesting metabolic and ecological interactions within biofilters. These results indicate that comammox Nitrospira play a previously overlooked but important role in home aquarium biofilter nitrification.
Following the identification of comammox Nitrospira among dominant ammonia oxidizers in freshwater aquarium biofilters, Chapter 3 monitored microbial community succession and water chemistry for three independent home aquariums during their first 12-weeks after start-up, with weekly collection of biofilter beads and sponge samples. Extracted DNA from biofilter samples was used for 16S rRNA gene sequencing to determine microbial community composition and quantitative PCR (qPCR) to quantify ammonia monooxygenase (amoA) genes. Water samples were also collected weekly for measurements of ammonia, nitrite, and nitrate. Biofilter nitrification activity reduced ammonia and nitrite concentrations below detectable limits by week 3 in two of the three aquariums, which showed comparable nitrification activity by the week 8 time point. Detection of ammonia oxidizers by qPCR coincided with ammonia oxidation activity for all systems. The two aquariums with nitrification occurring by week 3 contained live plants, whereas the aquarium with delayed nitrification did not, suggesting that live plants might provide an effective nitrifier inoculation source for aquarium establishment. Additionally, a preference in biofilter material was observed for detected AOA, which were present in higher abundance in bead samples compared to sponge samples. Although the tested aquaria differed in the timing and prevalence of ammonia oxidizers in biofilters during community establishment, samples from all three aquaria were consistently dominated by comammox Nitrospira by the end of 12 weeks. Additional metagenomic functional profiling of week 12 biofilter samples confirmed the presence of AOA amoA genes and comammox Nitrospira amoA genes as detected by qPCR for all aquaria, with nitrite oxidation marker gene nxrB for both comammox Nitrospira spp. and canonical NOB Nitrospira spp. also detected. Although this work sheds light on how ammonia oxidizers establish in residential freshwater aquaria, further research is needed to test factors that impact the establishment of nitrifiers populations, such as inoculation sources, fish loads, and water chemistry.
Novel WWTP technologies, including membrane aerated biofilm reactors (MABR), aim to improve nitrogen removal, reduce energy consumption, and improve nitrification in cold weather conditions. The municipal WWTP in Southern Ontario was upgraded with an MABR system in 2022, which was installed upstream of the existing conventional activated sludge (CAS) system at a municipal scale. This current study evaluates the impact of an MABR upgrade on the CAS and MABR microbial communities, with this MABR system being the largest in North America based on media surface area at the time of its installation. In Chapter 4, the microbial community of the CAS system was characterized before and after the MABR upgrade to evaluate the impact of the upstream MABR installation on the functional potential of the downstream activated sludge. Microbial community characterization was done using 16S rRNA gene amplicon sequencing of the V4-V5 region and additional MABR biofilm samples were characterized using metagenomic analysis to evaluate the functional potential of the biofilm for nitrification and denitrification. Before upgrade, the CAS system included the nitrifiers Nitrosomonas (AOB) and Nitrobacter (NOB), which exhibited seasonal abundance and activity differences. Following the upgrade to the hybrid MABR-CAS system, seeding effects from the MABR biofilm increased diversity of the CAS, including nitrifiers. Along with AOB, Nitrospira NOB and comammox Nitrospira were present in the MABR. Metagenomic profiling showed that biofilm microbial communities were well equipped to perform nitrification, denitrification, and phosphate removal. Characterization of the microbial communities in the plant showed that the MABR technology had a positive impact in increasing microbial diversity in this treatment system, along with an increased inventory of nitrifiers with diverse metabolic capabilities
Land-to-Water Linkages: Nutrients Legacies and Water Quality Across Anthropogenic Landscapes
No full textAn increasing population and the intensification of agriculture has driven rapid changes in land use and increases in excess nutrients in the environment. Globally, excess nutrients in inland and coastal waters have led to persistent issues of eutrophication, ecosystem degradation, hypoxia, and drinking water toxicity. Over the past few decades, we have seen policies set to mitigate the degradation in water quality. The existing paradigm of water quality management is based on decades of research finding a linear relationship between the net nitrogen inputs to the landscape and stream nitrogen exports. For instance, in the U.S., in response to these nutrient problems, working groups have spent approximately a trillion dollars to improve water quality by upgrading wastewater treatment plants and implementing nutrient management plans to decrease watershed nitrogen and phosphorus inputs. Despite concerted efforts, in many cases we have not seen marked improvements in water quality. In cases where water quality has improved, it is frequently after decades of nutrient management. The lack of or delayed water quality improvement suggests the importance of other drivers in modulating the relationship between nutrient inputs and watershed exports. Indeed, watershed nutrient loads are not just a function of current-year nitrogen inputs but can also depend on the history of inputs to the watershed. However, we still have little understanding of the relationship between nutrient inputs related to exports and the extent that accumulated stores of nitrogen and phosphorus influence this relationship.
The central theme of my research has been an exploration of the history of anthropogenic nutrient use and the relationship between nutrient inputs and the response in water quality. Specifically, I have focused on the role of current nutrient inputs versus historical nutrient use in impacting water quality at the watershed scale, as well as the various landscape and climate controls that can mediate responses to changes in management. My research objectives will be to (1) develop a multi-decadal mass balance of nitrogen and phosphorus at the sub-watershed scale across the contiguous U.S. in order to investigate (2) the relationship between watershed nitrogen inputs and export and the drivers of changes in watershed nitrogen export, (3) the magnitude, spatial distribution, and drivers of nitrogen retention and legacy stores, and (4) the use and management of phosphorus in agricultural landscapes in the context of both food security and environmental health.
I began by developing county-scale nitrogen and phosphorus surplus datasets, TREND-N and TREND-P, for the contiguous U.S.—with surplus defined as the difference between anthropogenic inputs (fertilizer, manure, domestic inputs, biological nitrogen fixation, and atmospheric deposition) and non-hydrological export (crop and pasture uptake). In Chapter 2, I present the updates to a previously published TREND-N county-scale nitrogen mass balance dataset, improving crop and pasture uptake and livestock excretion methods. In Chapter 3, I develope new county-scale phosphorus surplus dataset, using similar methods. These datasets were then downscaled to a 250 m gridded-scale dataset, known as gTREND-Nitrogen and gTREND-Phosphorus, a step led by my collaborator Shuyu Chang. These novel datasets serve as the foundational data for the subsequent chapters.
Next, in Chapter 4, I explored the relationship between net nitrogen inputs and nitrogen export for over 400 watersheds across the U.S. I used the newly developed nitrogen surplus dataset to understand how watershed-scale nitrogen surplus magnitudes and exports change over time and examine how the relationships are influenced by both natural and anthropogenic controls within watersheds. To achieve this, I used a set of 492 watersheds with nitrogen input and export data spanning from 1990 to 2017. We found that 284 watersheds had a significant (p<0.1) increasing or decreasing trend in both nitrogen surplus and nitrogen load. Of these watersheds, we identified 62 where both nitrogen surplus and export have been significantly increasing over the last two decades. These input-driven watersheds are characterized by high livestock density, agricultural area, and tile drainage. In contrast, nitrogen surplus and export have been decreasing in 127 "bright spot" watersheds, characterized by high population density and urban land use. Nitrogen surplus is also decreasing in 60 "transitioning" watersheds, but export is increasing as nitrogen surplus decreases. We argue that these watersheds are transitioning from agriculture to more urban areas, such that fertilizer inputs have decreased, but the higher nitrogen export is driven by legacy nitrogen stores. Finally, we found 35 watersheds demonstrating a delayed response, with nitrogen export decreasing despite an increase in nitrogen surplus. Climate appears to be the driver of response in these watersheds, with aridity likely driving lower nitrogen export, despite increasing inputs. The four typologies of nitrogen inputs and export relationships suggest that watersheds can act as filters and modulate the movement of nitrogen. Our results provide insights into the complex dynamics of nitrogen surplus and export relationships, as well as how the landscape, climate, and legacy nitrogen can influence these relationships.
In Chapter 4, I analyzed relationships between changes in nitrogen inputs and export, to understand what drives changes in watershed export, finding that legacy stores may be modulating the watershed response to changing net nitrogen inputs. However, we have limited knowledge of the magnitude and spatial distribution of legacy stores across North America. Therefore, in Chapter 5, we quantified how much nitrogen retention, which is the mass of nitrogen stored in legacy pools and nitrogen lost to denitrification, has accumulated in watersheds, and where it can be accumulating. To achieve this, we used existing datasets and machine learning algorithms to calculate the mass of ‘retained’ nitrogen in the landscape—defined as the nitrogen stored in the soil organic nitrogen pool, the groundwater pool, or lost through denitrification. Specifically, we built a random forest modeling framework trained on the watersheds’ nitrogen surplus and components, loads, and characteristics to predict nitrogen loads at the HUC8 scale across the U.S. We calculated retention for HUC8, which is the difference between nitrogen surplus and predicted loads, and found that nitrogen retention is highest in the Midwestern and Eastern U.S. because of low exports in regions with high agricultural inputs or high population density. Next, we used a data-driven approach to estimate legacy stores by allocating retained nitrogen mass into their legacy pools. We partition nitrogen retention in the Upper Mississippi region HUC8 watersheds into the mass stored in the groundwater pool, soil organic nitrogen pools, and mass lost to denitrification. We found that, on average, 42% of the mass is stored in the soil organic nitrogen pool, 16.5% is stored in the groundwater pool, and 40% is lost to denitrification.
While these two chapters focused on nitrogen, in my final chapter we shifted to explore phosphorus use in agricultural systems. In my final chapter, we used the new gridded phosphorus surplus and components dataset to explore current and historical agricultural phosphorus use and management in landscapes within the context of both food security and environmental health. To characterize the extent of phosphorus depletion and excess, we employed indicators such as annual and cumulative phosphorus surplus and phosphorus use efficiency (PUE). We found that the evolution of agricultural phosphorus management is shaped by changing fertilizer management, the proliferation of concentrated animal operations, climate, and the landscape's memory of past phosphorus use. We further integrated both cumulative phosphorus surplus and PUE into a framework to quantify phosphorus sustainability in intensively managed landscapes. We found that in the 1980s, much of the agricultural land was undergoing ‘intensification,’ with positive and increasing cumulative stores because phosphorus inputs exceeded crop uptake (PUE 1). However, 70% of the agricultural area in the U.S. is still undergoing ‘intensification,’ particularly in areas with more of their inputs from livestock manure, pointing to the need to treat manure as a resource instead of the current approach of treating it as a waste product.
By using novel datasets, we have been able to explore nutrient use across space and time and its impact on food security and environmental outcomes. I have made significant contributions towards expanding the discussion of nutrient us and fate, understanding the magnitude and distribution of cumulative net nutrient inputs stores in the landscape, as well as the ways in which intrinsic watershed properties, climate, land management, and historical nutrient use can modulate the relationship between inputs and export. Overall, my findings underscore the importance of nuanced, place-based, and context-dependent nutrient management strategies, with a focus on manure management, to address the diverse challenges of different agricultural systems and prevent unintended environmental consequences
Enzyme-mediated controlled release of DNA polyplexes from gelatin methacrylate hydrogel for nonviral gene delivery
No full textCorneal diseases such as recurrent corneal erosion and dry eye disease are common diseases which can significantly interfere with quality of life. The lack of effective treatments for these diseases necessitates the development of novel treatment methods. Skin wounds are another disease area which presents opportunities for improvement of clinical solutions. Gene delivery solutions have gathered interest for both corneal disease treatment and skin wound healing applications. Matrix metalloproteinase 9 (MMP9) enzymes are highly upregulated in both corneal diseases and in chronic skin wounds. Gelatin methacrylate (GelMA) is a biocompatible hydrogel which is degraded in the presence of MMP9 enzyme. It was thus hypothesized that GelMA could serve as an enzyme-responsive controlled release scaffold for polyplexes.
The objective of this project was to demonstrate the potential of GelMA hydrogel as an enzyme- responsive controlled release scaffold for chitosan-graft-polyethyleneimine (CS-PEI) polyplexes as a non-viral gene delivery platform to treat corneal diseases or skin wounds. Key criteria of a controlled-release system are tunable release kinetics and maintained bioactivity of the therapeutic molecule after loading and release. The aims of this work were to characterize the release kinetics of the polyplexes and assess the bioactivity of polyplexes released from the GelMA hydrogel system.
Methods were developed to quantitate the concentration of the released CS-PEI polyplexes in solution, and the release profile of polyplexes from GelMA in different MMP9 concentrations was measured. The release was found to be sustained over a 5-day period in the presence of physiologically relevant MMP9 concentrations. The in vitro transfection efficiency of released CS-PEI polyplexes was also explored to demonstrate the bioactivity of the polyplexes. The released polyplexes successfully transfected COS-7 cells in an enzyme-responsive and dose- dependent manner
Non-Intrusive Diagnostics of Outdoor Ceramic Insulators Using Ultrasonic Signatures and Deep Learning Models
No full textCeramic insulators have been widely used in overhead power lines for over a century. However, in recent years, transmission and distribution networks have been gradually shifting toward polymeric insulators. Despite this transition, many ceramic insulators remain in service, and a significant portion are now approaching or exceeding their intended lifespans. This aging infrastructure poses an increasing risk of sudden failure, thereby compromising network reliability. Insulator failures account for nearly half of maintenance costs in transmission lines [1], prompting a growing demand among utilities for fast, reliable, and cost-effective condition monitoring systems.
Defective ceramic insulators that experience internal punctures, broken discs, or cracks will ultimately initiate partial discharge (PD) activities. Additionally, ceramic insulators exposed to high contamination levels are prone to dry band arcing (DBA), which increases the risk of flashover. Both PD and DBA emit electromagnetic, ultraviolet, infrared and/or ultrasonic radiation, serving as critical indicators that can trigger corrective maintenance actions. Employing sensors to detect these early-stage discharge activities is essential for preventing insulator failure and reducing the risk of power outages. Furthermore, insulator strings often exhibit multiple concurrent defects, resulting in various discharge activities—such as corona, PD, or DBA—each characterized by distinct properties. The overlapping nature of these discharge activities poses significant challenges to accurate diagnosis.
This thesis introduces a novel, non-contact method for assessing the condition of outdoor ceramic insulators by employing an ultrasonic sensor in conjunction with deep learning techniques to detect and classify insulator defects. Notably, it demonstrates how the strong directionality of ultrasonic sensors can be leveraged to indirectly identify internal punctures by monitoring surface discharges on adjacent discs, overcoming attenuation limitations caused by the porcelain body and metallic caps. The dissertation is structured into three phases. In the first phase, ultrasonic data from defective insulator strings is generated under controlled laboratory conditions. A multi-class classification model is developed and trained to diagnose individual defects; the model’s performance is then tested in both laboratory and field environments to evaluate its robustness and real-world applicability. The second phase extends this approach to the diagnosis of insulators with multiple concurrent defects. Here, a multi-label classification model is developed to identify and categorize overlapping defect signatures within a single insulator. This approach captures multiple defect types simultaneously, thereby enhancing diagnostic accuracy and reflecting the true operational conditions of outdoor insulators, where different kinds of degradation can co-exist. The final phase involves in-depth analysis of ultrasonic signals by leveraging model-learned features to identify distinct temporal characteristics for each defect type. This enables precise defect characterization and further boosts the accuracy and reliability of outdoor insulator condition assessment.
Additionally, findings from Shannon entropy analyses corroborate the presence of unique entropy profiles for different defect classes, improving classification performance—though internal puncture and corona classes can exhibit overlapping energy and entropy characteristics. The results highlight the potential for real-time, non-intrusive monitoring, while emphasizing future work on advanced time-frequency analysis and exploring diagnostic methods for polymeric insulators to address broader asset management challenges
Investigating Dual Embodiment in Recurring Tasks with a New Social Robot: Designing the Mirrly Platform
No full textIn many contexts, including education, therapy, and everyday tasks, assistive robots have demonstrated considerable promise for augmenting human capabilities and providing supportive interactions. By designing and building a new tabletop social robot, Mirrly, as well as empirically examining how different robotic embodiments affect user engagement and task compliance, this thesis tries to contribute to this field. In light of advances in human-robot interaction (HRI) and child-robot interaction (CRI), I investigated a comprehensive set of mechanical, electronic, and software requirements. As a result of these requirements, Mirrly was developed, a low-cost, compact platform that could be deployed in schools, therapy centers, or personal homes and it is anthropomorphic enough for supporting social interactions with people.
Following the design and implementation of Mirrly, I conducted a multi-session experiment to determine whether physical embodiment, virtual embodiment (mobile-based), or dual embodiment (both physical and virtual) promoted compliance with repetitive daily tasks, as relevant e.g. in clinical applications where patients need to comply with repetitive treatments. According to the results, physical presence is a strong motivator, leading to higher compliance and engagement, whereas dual embodiment enhanced participants' enjoyment (pleasure) of the interaction specifically. Interestingly, individual differences in the participant sample, such as personality traits and self-control, did not have a significant impact on adherence or user satisfaction. As at least within the short, relatively simple user tasks, these results emphasize the importance of design factors namely physical tangibility and interactive behaviors.
As part of the thesis, a review of relevant HRI and CRI literature is conducted to contextualize Mirrly's design within the context of current robotics. Following a detailed description of utilized methodology, I present the experimental conditions, measures, and analytical methods for assessing compliance, engagement, and perceived enjoyment. Finally, I discuss the implications of the findings for building more adaptive, child-centered robots, especially in clinical, therapeutic and educational settings. Several future directions are also proposed, including extending task complexity, integrating advanced sensors for personalized feedback, and conducting longitudinal studies.
As part of ongoing efforts in social and assistive robotics, this work introduces a novel robotic design. Moreover, in my study, I demonstrate that a robot with careful engineering, physical embodiment, and adaptability can significantly boost compliance. Consequently, this thesis lays a good foundation for future developments in CRI, highlighting how embodiment, anthropomorphism, and structured experimental design converge to support recurrent task compliance efficiently
Cloud-Connected Model Predictive Control for Autonomous Mobile Robots in the Presence of Network Latency and Message Losses
No full textAutonomous driving systems have become increasingly popular due to their potential to reduce traffic accidents compared to human-operated vehicles, as well as their significant potential to optimize traffic flow and reduce pollution.However, there remain cases in which autonomous driving system malfunctions lead to accidents. These failures often arise from the partial observability of the environment and
the inherent limitations of onboard sensor systems. To address these shortcomings, Cloud-Connected Autonomous System (CCAS) has emerged as a promising alternative, leveraging cloud-based computation and multiple distributed sensors to build a comprehensive understanding of the environment (including the ego vehicle) and make collective decisions for all controlled agents in the cloud. This approach
improves decision-making and reduces the need to install costly onboard hardware on every vehicle by centrally sharing sensors and computational resources. Despite its advantages, introducing cloud connectivity into autonomous systems presents significant challenges, particularly network latency and message loss. Such latencies can negatively impact vehicle control and safety, especially when they exceed the control sampling interval, leading to unstable maneuvers or potential collisions. This work introduces a cloud controller designed to compensate for the effects of latencies longer than a single control sampling interval. A Robot Operating System (ROS)-based simulation environment was developed for rapid algorithm prototyping and seamless integration with a real testbed. The proposed solution was validated through both simulations and real-world tests involving an autonomous hospital bed using Ackermann steering in an indoor scenario. The experimental outcomes highlight the method’s effectiveness and practical potential
The Weight of Leaving: Gender, Identity Shifts, and Health Challenges in the Post-Competition Lives of Women Bodybuilders
No full textDrawing from my own experience as a bodybuilder in the bikini division, the purpose of this research is to explore the retirement transition for women bodybuilders. More specifically, this study explores how women bodybuilders are influenced by gendered ideologies and how these ideologies affect their transition experience in relation to identity, health, and well-being. There is limited literature exploring bodybuilding retirement, and no literature exploring women’s experiences during this transition. Guided by a sport feminism theoretical orientation, I conducted reflexive dyadic interviews with 15 women who retired, or were thinking of retiring, from bodybuilding. I used narrative inquiry to guide the methodological process and observed online spaces including Instagram and Reddit. I constructed five composite characters to represent the diverse experiences of my participants and to present the findings, I crafted a series of narratives, authored by these characters, while also including my own narration to provide context throughout the series of stories. The narratives are organized into two parts. Part one highlights the impact of retirement on the women’s physical health, mental health, and well-being, while part two reveals the influence of gender ideologies on the retirement experience regarding gendered expectations both inside and outside of the industry. I conclude that gender plays a significant role in the retirement transition as gender ideologies are engrained deeply within the industry and society, leading to a dynamic experience impacting identity, health, and well-being during the transition, unique to women. This research is the first of its kind to address women’s bodybuilding retirement in the academic literature, sheds light on an understudied topic and offers insights that may resonate and validate women who share this experience
An Investigation Into the Effectiveness of Latent Variable Models for Domain Adaptation
No full textThe proliferation of machine learning with neural networks (NNs) has revolutionized fields such as computer vision and natural language processing. However, their successes often overshadow two important weaknesses of neural networks: (i) their reliance on large amounts of training data and (ii) the assumption of independent and identically distributed (i.i.d.) data. Because of these weaknesses, the vast majority of NNs today are applicationspecific machineries tuned to one task and one data domain.
This thesis investigates the effectiveness of a latent variable model for unsupervised domain adaptation, aiming to bridge the gap between two different data distributions while leveraging only labeled data samples from one, and unlabeled data samples from the other. A novel generative modeling framework is proposed to address this problem, incorporating recent advances in probabilistic modeling and variational inference techniques from the
neural network literature.
Empirical results of the proposed approach seem promising, and indicates adequate transfer of the labeling knowledge of the model across disparate data domains without requiring manual re-labeling or domain-specific adjustments. Moreover, the proposed approach has also shown potentials in solving the related domain translation problem. Despite these fortunes, the existing approach has shown limitation in solving more complex scenarios of unsupervised domain adaptation, speficially those involving more vibrant differences between domains