Masters Theses

Permanent URI for this collection

https://hdl.handle.net/10919/9291

Browse

Now showing 1 - 20 of 23021

A framework to improve climate-resilient health among Indigenous communities
Perera, Chrishma Dharshani (Virginia Tech, 2025-04-15)
Climate change has severely impacted Indigenous communities' health. Many global strategies aimed at addressing climate-related health risks assume all populations face the same challenges and can adapt equally, overlooking the specific needs of groups such as Indigenous communities. Adopting generalized global approaches can unintentionally harm Indigenous communities and worsen their climate health risks. In this context, my study explores how the World Health Organization's climate-resilience health systems approach can be tailored for Indigenous communities. Two objectives guide the study: i) to develop a conceptual framework to improve climate resilience health among Indigenous communities, ii) to identify the capacities of Indigenous communities that can serve as transformative pathways in safely adopting global climate-resilient health approaches. To achieve these objectives, I followed a two-step methodology. First, I conducted a systematic literature review encompassing 137 peer-reviewed articles from Web of Science, PubMed, and Medline by ProQuest databases. Second, I collaborated with the Indigenous Peoples' Observatory Network and conducted 17 key informant interviews. In the first objective, I identified fifteen Indigenous health drivers, categorized into risk, protective, and overlapping categories. I developed a conceptual framework based on identified health drivers. The conceptual framework comprises two components: i) a place-based feedback loop and ii) the shaping of health drivers. The feedback loop comprises five elements: i) place, ii) causations, iii) infirmities, iv) interventions, and v) sustaining. The shaping of health drivers is designed as a collaborative process involving governance-level officers and Indigenous communities. Applying the framework to case studies confirmed its adaptability across various locations. In the second objective, I identified five transformative pathways: i) government community interactions, ii) traditional medicine and spiritual beliefs, iii) experience-based practices, iv) community-based collective actions, and v) community-based policies. I developed evidence-based narratives to explain how transformative pathways support resilience consideration of the World Health Organization's climate-resilient health systems. Based on insights from key informants, I proposed three recommendations to strengthen transformative pathways: i) Indigenous mentorship in knowledge, health education, and research, ii) identifying opportunities to develop Indigenous inclusive health workforce, and iii) enhancing indigeneity in health policies. This study provides valuable insights to researchers, policymakers, and health professionals on enhancing Indigenous communities' resilience to climate-linked health risks by developing a conceptual framework and identifying transformative pathways.
Investigation of Modular CLLC DC/DC Converter using Bypass Control for Wide Output Voltage Regulation
Sathri, Jaswanth Daniel (Virginia Tech, 2025-04-14)
With the recent emerging demands in power electronics and electric grid of the future, resonant converters like CLLC converter are gaining popularity in applications like dc microgrids because of their advantages like high efficiency and bidirectional operation capability. However, one limitation of such converters is their limited voltage range which makes it hard to interface wide output voltage range loads like electric vehicle charging. Using additional power conversion stages solves this issue but it comes with added cost and reduced efficiency. This paper proposes a novel converter topology referred to as modular partial power architecture that has reduced power conversion stages and a novel bypass control strategy which allows it to have wide voltage range. Using the bypass control method, full voltage range of 0-100% is possible with bidirectional power flow by bypassing or turning on the modules based on voltage requirement. The detailed design considerations for this converter have been analyzed for a specific design case, and it is shown that the device losses can be reduced by up to 60% at full load. The working of the converter and the control strategy have been verified both in simulation and hardware.
Using Rigid and Soft Grippers for Assistive Robotic Tasks
Keely, Maya Nicole (Virginia Tech, 2025-04-10)
For robot arms to perform everyday tasks in unstructured environments, these robots must be able to manipulate a diverse range of objects. Today's robots often grasp objects with either soft grippers or rigid end-effectors. However, purely rigid or purely soft grippers have fundamental limitations: soft grippers struggle with irregular, heavy objects, while rigid grippers often cannot grasp small, numerous items. Combining the capabilities of rigid and soft grippers while equipped to the end effector of a robot arm could provide a larger range of capabilities for everyday tasks. For millions of adults with mobility limitations, eating meals is a daily challenge. A variety of robotic systems have been developed to address this societal need. These robots serve as a proxy for the human's arm: the user inputs the food they want to eat, and the robot autonomously picks up that food and brings it to the user's mouth. Unfortunately, end user adoption of robot-assisted feeding is limited, in part because existing devices are unable to seamlessly grasp, manipulate, and feed diverse foods. Recent works seek to address this issue by creating new algorithms for food acquisition and bite transfer. In parallel to these algorithmic developments, however, we hypothesize that mechanical intelligence will make it fundamentally easier for robot arms to feed humans. In this paper we therefore introduce two end effector designs. One of which is called the RISO, a mechanics and controls approach for unifying traditional RIgid end effectors with a novel class of SOft adhesives. When grasping an object, RISOs can use either the rigid end effector (pinching the item between non-deformable fingers) and/or the soft materials (attaching and releasing items with switchable adhesives). This enhances manipulation capabilities by combining and decoupling rigid and soft mechanisms. The second end effector design we propose is the Kiri-Spoon, a soft utensil specifically designed for robot-assisted feeding. Kiri-Spoon consists of a spoon-shaped kirigami structure: when actuated, the kirigami sheet deforms into a bowl of increasing curvature. Robot arms equipped with Kiri-Spoon can leverage the kirigami structure to wrap-around morsels during acquisition, contain those items as the robot moves, and then compliantly release the food into the user's mouth. Overall, Kiri-Spoon combines the familiar and comfortable shape of a standard, rigid spoon with the increased capabilities of soft robotic grippers. In this paper, we go on to outline the process used to develop these end effector designs. In addition, we show the experimental and user study results obtained suggest these grippers could improve the current capabilities of robot arms in assisting humans and performing everyday tasks.
Convergence Rates of Gradient Descent-ascent Dynamics under Computation Constraints in Solving Min-max Optimization
Do, Duy Anh (Virginia Tech, 2025-04-10)
This thesis is dedicated to providing a new analysis of the convergence rates of the Gradient Descent-ascent (GDA) method for solving the Min-max optimization (MMO) problems under computation constraints. In particular, we focus our study on two main classes of MMO: a continuous-time variant of the centralized Min-max problem where the GDA update only has access to the gradients of the objective function after some delay, as well as the Federated Min-max Learning (FML) problem, a special setting within the family of decentralized MMO under quantization constraints. Also known as the saddle point problem due to its objective being to find a saddle point of a function, the MMO problem, as well as its decentralized counterpart, has received wide attention due to its significant impact in different fields such as stochastic control and training generative adversarial networks. The GDA method is one of the most celebrated algorithms to find the saddle point of such functions, due to its computational efficiency and ease of implementation. Therefore, it is important that we delve into the analysis of the GDA approach in the MMO problem, not only in the traditional settings where information can be readily and perfectly available to the computation units but also under more practical scenarios. In this thesis, we focus our study on two special types of constraints. Firstly, understanding that calculating gradients of a function instantly is practically impossible, we tackle the continuous-time variant of centralized GDA under delayed gradients. We utilize the singular perturbation approach to obtain convergence rates in two non-convex settings of the objective function, namely, the two-sided and one-sided Polyak - Lojasiewicz (PL) conditions, by designing a coupling Lyapunov function to capture the interaction between the gradient descent and ascent dynamics subject to asynchronous gradients. Secondly, we study an important framework within decentralized MMO named Federated Min-max Learning, in which limited communication bandwidth requires information exchanged to be quantized. We apply a similar two-time-scale GDA technique to obtain convergence rates in three different settings, namely, the strongly-convex-strongly-concave case, and when it is subject to the two-sided and one-sided PL conditions. Finally, we provide numerical simulations to demonstrate the efficiency of our theoretical results.
Towards Effective Long Conversation Generation: Dynamic Topic Tracking and Recommendation for Open-Domain Dialogue Systems
Ashby, Trevor Clark (Virginia Tech, 2025-04-08)
The dynamic nature of human conversation necessitates effective topic management and evo- lution in open-domain dialogue systems. This thesis presents EvolvConv, a novel approach for real-time conversation topic tracking and evolution in AI dialogue systems. EvolvConv addresses critical limitations in existing open-domain dialogue systems, which often exhibit performance degradation in extended conversations due to inadequate topic management. The system implements real-time tracking of both conversation topics and user preferences, utilizing this information to facilitate natural topic evolution and shifting based on con- versation state. Through comprehensive experimentation, we evaluate EvolvConv's topic evolution and shifting capabilities across increasing conversation lengths. Using the un- referenced evaluation metric UniEval, we demonstrate that EvolvConv maintains conversa- tion coherence while achieving a controlled topic shift rate of 5-8% at any point throughout the conversation. Comparative analysis shows that EvolvConv generates 4.77% more novel topics than baseline systems while maintaining balanced topic groupings. User evaluation studies validate the practical effectiveness of EvolvConv, with participants preferring its generated responses 47.8% of the time compared to baseline systems, positioning it as the leading artificial system among comparative baselines, second only to human responses. This research contributes to the advancement of more natural and engaging open-domain dialogue systems capable of sustained, evolving conversations.
Quality Assurance for Chip Seals Using Mean Profile Depth
Tsogt-Ochir, Norovbanzad (Virginia Tech, 2024-09-20)
Chip sealing has numerous benefits as a pavement preservation treatment. The quality of the chip seal is assessed through various parameters, including texture depth, skid resistance, and visual evaluation. Current practice reveals that transportation agencies conduct quality assurance after construction, while contractors are typically responsible for chip seal placement and quality control. However, existing quality assurance procedures predominantly depend on visual inspection, and lack well-established methodologies. This study used Mean Profile Depth (MPD) as a macrotexture metric for the quality assurance of chip seals. Field data were collected using state-of-the-art equipment from the Virginia Department of Transportation (VDOT) area. Considering both qualitative (visual assessment) and quantitative (MPD analysis) approaches, this study delineates definitive categories representative of chip seal quality. These categories included good quality chip seals, with minimal to no signs of flushing and aggregate loss and MPD values ranging from 1 to 1.2 mm. Fair-quality seals had MPD values between 0.6 and 1 mm, while poor-quality seals were identified with MPD values below 0.6 mm. This structured classification enhances preventive maintenance strategies, improving chip seal pavements' overall sustainability and longevity.
Getting Ready and Getting Unready: How Queer College Women Navigate Their Casual Sexual Experiences
Hodges, Elizabeth (Virginia Tech, 2025-03-03)
The purpose of this study is to explore how queer women, or people that identify with the label ‘woman’, navigate their casual sexual experiences while attending college or university. Previous studies acknowledge that, while college campuses are arenas for sexual experimentation and identity development for many young adults, students across gender and sexual identities engage with these experiences in both similar and unique ways. Data for this study came from twelve semi-structured interviews with undergraduate and graduate college-attending queer students. These data were analyzed using thematic analysis with a queer theory perspective to broaden the understanding of how sex takes place on campus. By considering experiences beyond the traditional gender binary and heteronormative assumptions, this research suggests more inclusive sexual health and safety practices and recommendations to ensure effective public health and safety for all college students.
First-In-DOg HISTotripsy for Intracranial Tumors Trial: The FIDOHIST Study
Vezza, Christina Renny (Virginia Tech, 2025-04-01)
Objective: Brain tumors represent some of the most treatment refractory cancers, and there is a clinical need for additional treatments for these tumors. Domesticated dogs are the only other mammalian species which commonly develop spontaneous brain tumors, making them an ideal model for investigating novel therapies. Histotripsy is a non-thermal ultrasonic ablation method that emulsifies tissue through acoustic cavitation. The primary objectives of this prospective study were to assess the feasibility and safety of histotripsy to ablate naturally occurring canine brain tumors. Secondary endpoints included characterization of magnetic resonance imaging (MRI) responses to histotripsy treatment, and exploratory immunogenomic tumor response analyses. Methods: The study design utilized a treat and resect paradigm, where tumors were approached using craniotomy, partially ablated with histotripsy delivered through the cranial defect, imaged with MRI, and then resected. Dogs were evaluated with clinical, brain MRI, immunopathologic, and genomic examinations before treatment, intraoperatively, and 1, 14, and 42 days post-treatment. Here we report the results of the three dogs with meningiomas, all of which were treated with a custom eight element 1 MHz histotripsy transducer at a pulse repetition frequency of 100 Hz and a treatment dosage of 400 pulses/point. Results: Histotripsy was successfully delivered to all dogs, resulting in histopathologic evidence of ablations that were sharply demarcated from untreated tumor, with measured treatments approximating planned volumes in 2/3 dogs. One dog experienced an adverse event consisting of transient cerebral edema that was possibly attributable to histotripsy. Histotripsy ablations could be grossly visualized and identified on MRI, with features consistent with hemorrhage and necrosis. Significant expression or upregulation of the damage associated molecular pattern HMGB1, cytokine-cytokine receptor interaction, and NF-b signaling pathways were observed in histotripsy treated tumors. Conclusion: Ablation of canine meningiomas with histotripsy through an open cranial window was feasible and clinically well tolerated.
Disdoc: AI Teaching Assistant for Computer Science Courses
Doney, Brendan Robert (Virginia Tech, 2025-03-31)
As enrollment in Computer Science grows, traditional help-seeking opportunities for students, such as office hours and forums, become less effective due to rising student-to-teaching assistant ratios. To address this issue, research has investigated large language models (LLMs) to provide individualized help to students at scale. However, prior research primarily targets introductory computing courses, does not fully connect LLMs to course material, and does not expose relevant course material to students. As a result, existing approaches do not adapt well to advanced computing courses and limit opportunities for students to develop self-sufficiency. To address this, we present Disdoc, an LLM-based question and answer tool for students in advanced computing courses. Disdoc presents snippets of course material relevant to student questions and generates answers using an LLM. To include course-specific information in answers, we connect the LLM to all course material through retrieval-augmented generation (RAG). To ensure the RAG system retrieves the most relevant information, we organize course material into question categories. We evaluated Disdoc in a research study on a 340-student Computer Systems class at Virginia Tech, where we tracked student reviews, activity, and exit survey responses. Students indicated that Disdoc was helpful, particularly for questions about course assignments. Usage data revealed that students strongly preferred to see LLM-generated answers and rarely clicked on outgoing links, suggesting they were satisfied with the LLM-generated answers and snippets of relevant course material.
A Study of Interference Suppression Using Deep Learning Methods
Malolan, Badhrinarayan (Virginia Tech, 2025-03-31)
This thesis investigates a Deep Learning model for interference suppression in wireless communications. By exploiting the structure of Convolutional Neural Network-based autoencoders, we develop an approach for interference suppression with no prior knowledge on characteristics or the exact location of interference. Traditional interference suppression techniques are heavily reliant on specific domain knowledge, thus their applicability in dynamic wireless environments is limited. This thesis proposes a CNN-AE (Convolutional Neural Network - Autoencoder) model that consists of an encoder, which captures the latent space representation from the input data, and a decoder that reconstructs the desired signal to suppress interference effects. We investigate the performance of a QPSK-based wireless communication system with three explicit interference scenarios, namely, %in-band tone, out-of-band tone, single frequency tone interference with two cases of in-bandwidth and out-of-bandwidth, and wideband interference from a dataset that captured over the air communication signals. A study is performed for different SNR values along with the SINR values to observe the effectiveness of the approach at different levels. The results of our approach are quantified using popular metrics such as bit error rate (BER), error vector magnitude (EVM), and Signal to Noise-Interference Ratio (SINR). The proposed model outperforms the baselines with classical techniques such as matched filtering and least squares adaptive filtering consistently over these several metrics. The thesis also investigates the latent space behavior of the autoencoder; which is used to provide an interpretation of how the network classifies between the desired signal and interference. We use this contextual information to pursue future directions in interference suppression performance by exploiting cyclostationarity properties of our desired signal to our advantage. One of the important contributions in this work involves carrying out thorough analysis with respect to the generalization capability of CNN-AE for different types of interference and signal conditions. The results presented herein illustrate the potential of a deep learning-based approach in enabling more robust and adaptive wireless communication systems that would be capable of autonomously managing complex interference scenarios without human intervention.
All Kinds of Touching
Mitchell Reford, Lotte Maxine Agnes (Virginia Tech, 2020-03-31)
Poems about skin, fucking, death, disaster, growing up, blood, caves, art and general stickiness.
Frequency Domain Authentication Using Piezoelectric PZT Disks as Hardware IDs
Trapani, Anson Marco (Virginia Tech, 2025-03-28)
The increasing interconnectedness of the modern world in the consumer, commercial, and military realms has emphasized the necessity of authenticating devices to prevent impostor attack vulnerabilities from being exploited. However, conventional digital methods used to store identification information in various devices are susceptible to various methods that can potentially expose the information to bad actors. Therefore, the avenue of hardware IDs, whose analog properties dictate its identity via in-situ bit string generation, is increasingly being explored as an alternative, given that they do not store a digital identifying key, are resistant to invasive attacks, can be cost-effective, are relatively tamper-evident, and can be difficult to clone. In this thesis, the focus will be on utilizing a lead zirconate titanate (PZT) thin cylinder (disk) as a hardware ID, based on the response of its various entropy sources to a frequency sweep. Those entropy sources explored by this work are the admittance peak magnitudes, resonance frequencies, quality factor of the admittance peaks, impedance peak magnitudes, anti-resonance frequencies, and quality factor of the impedance peaks. These sources exhibit a wide degree of variation in response to minute changes in the PZT disk dimensions, which are varied in this work. This experiment is conducted in the COMSOL Multiphysics simulation environment to obtain unique IDs for each disk. The entropy source values for each disk are digitized via bins of a corresponding continuous distribution constructed from the sample values by Gaussian kernel density estimation, in which six sets of 10-bit IDs—one for each entropy source—are created, and a set of 60-bit IDs is created via concatenation of the 10-bit sets. Then, these bits are evaluated for security, and demonstrate uniqueness values that consistently approach 0.5, average minimum entropies per bit around 0.9 bits, and maximum entropy and no collisions in the 60-bit ID set. The minimum sampling rate for a subsequent hardware implementation beyond the scope of this work is found to be 946528 Hz, with a minimum frequency resolution capability of 6 µHz, and a minimum magnitude resolution of 2799.9306 siemens and 3748.1494 ohms. Additionally, total correlation and dual total correlation is found to be low as a fraction of the 10 bits present in the 10-bit ID sets but is skewed in a negative direction due to the small sample size compared to the number of possible bit combinations in the 60-bit ID set. However, in all cases, these metrics prove to be useful for assessing the total information quantity and thus security of the set of devices in the context of Kerckhoff's principle. These results show that PZT disks, and the piezoelectric quirks associated with the material and geometry, are conducive to viable hardware IDs that can serve as the backbone for a secure system in the contemporary world.
Extraction of Blood Volume Pulse Morphology from Facial Videos Using an LSTM-Based Temporal Encoder-Decoder Model
Tyler, Jonathan David (Virginia Tech, 2025-03-28)
This thesis introduces a method for extracting blood volume pulse (BVP) signals from facial videos, moving beyond basic heart rate estimation to capture full pulse waveforms. Our approach adapts techniques from audio signal separation and applies them to video, using a machine learning model capable of processing complex time-based data. By incorporating both regular RGB (red, green, blue) and infrared (850nm, 940nm) video, we enhance the quality of the extracted signals, making signal extraction more reliable under different lighting conditions. This method not only improves accuracy in measuring real-time heart rate but also captures unique heart patterns that could support biometric identification. Our findings show that this approach effectively recovers detailed BVP shapes from video, paving the way for advancements in health monitoring and identity verification technologies.
COVID-19 Variant Analyzer through Genomic Sequences and Jaccard Similarities
Bharadwaj, Atul Narasimha Murthy (Virginia Tech, 2025-03-26)
The COVID-19 pandemic has underscored the urgent need for efficient genomic surveillance to track the emergence and spread of SARS-CoV-2 variants. This study developed a novel computational framework to enhance variant detection by leveraging a database-driven approach and genomic sequence analysis. The framework utilizes MySQL database architecture where each variant is stored in distinct tables, enabling rapid comparison and classification of new variants through Jaccard similarity calculations. The innovative aspect of this research lies in its unique database structure and classification method. Unlike traditional clustering approaches, this system creates individual tables for each variant, allowing for dynamic updates and efficient comparisons. When a new variant is introduced, the framework calculates Jaccard similarity scores between the new variant and existing variant tables, automatically creating new tables for potentially novel variants that fall below-established similarity thresholds. This approach enables real-time variant tracking and classification, adapting to the evolving nature of the virus. The system employs advanced bioinformatics tools including sourmash for signature generation and NumPy for computational analysis, alongside Python-MySQL connectors for seamless database interactions. It implements similarity thresholds of 0.817 for primary classification and 0.867 for secondary validation to determine variant group membership. Whole-genome data was analyzed to compare its effectiveness in identifying variants of concern, with the database structure accommodating genomic data. The results demonstrated the framework's ability to accurately detect and classify SARS-CoV-2 variants with high sensitivity and specificity. The study highlighted the potential of whole-genome sequences as a cost-effective alternative for variant detection in resource-limited settings, while also revealing their limitations compared to whole-genome analysis. This research contributes to global genomic surveillance efforts by providing scalable database tools for rapid variant identification, aiding public health strategies, vaccine development, and therapeutic interventions.
Zero-Shot Scene Graph Relationship Prediction using VLMs
Dutta, Amartya (Virginia Tech, 2025-03-24)
Scene Graph Relationship Prediction aims to predict the interaction between the objects in an image. Despite the recent surge of interest in open-vocabulary and zero-shot SGG, most approaches still require some form of training or adaptation on the target dataset, even when using Vision-Language Models (VLMs). In this work, we propose a training-free framework for the VLMs to predict scene graph relationships. Our approach simply plugs VLMs into the pipeline without any fine-tuning, focusing on how to formulate relationship queries and aggregate predictions from the object pairs. To this end, we introduce two model-agnostic frameworks: SGRP-MC, a multiple-choice question answering (MCQA) approach, and SGRP-Open, an open-ended formulation. Evaluations on the PSG dataset reveal that well-scaled VLMs not only achieve competitive recall scores but also surpass most trained baselines by over 7% in mean recall, showcasing their strength in long-tail predicate predic- tion. Nonetheless, we identify several practical challenges: the large number of potential relationship candidates and the susceptibility of VLMs to choice ordering can affect con- sistency. Through our comparison of SGRP-MC and SGRP-Open, we highlight trade-offs in structured prediction performance between multiple-choice constraints and open-ended flexibility. Our findings establish that zero-shot scene graph relationship prediction is feasi- ble with a fully training-free VLM pipeline, laying the groundwork for leveraging large-scale foundation models for SGG without any additional fine-tuning.
Enhancing Layout Understanding via Human-in-the-Loop: A User Study on PDF-to-HTML Conversion for Long Documents
Mao, Chenyu (Virginia Tech, 2025-03-24)
Document layout understanding often utilizes object detection to locate and parse document elements, enabling systems that convert documents into searchable and editable formats to enhance accessibility and usability. Nevertheless, the recognition results often contain errors that require manual correction due to small training dataset size, limitations of models, and defects in training annotations. However, many of these problems can be addressed via human review to improve correctness. We first improved our system by combining the previous Electronic Thesis/Dissertation (ETD) parsing tool and AI-aided annotation tool, providing instant and accurate file output. Then we used our new pipeline to investigate the effectiveness and efficiency of manual correction strategies in improving object detection accuracy through user studies, including 8 participants, comprising a balanced number of four STEM and four non-STEM researchers, all with some background in ETDs. Each participant was assigned correction tasks on a set of ETDs from both STEM and non-STEM disciplines to ensure comprehensive evaluation across different document types. We collected quantitative metrics, such as completion times, accuracy rates, number of wrong labels, and feedback through our post-survey, to assess the usability and performance of the manual correction process and to examine their relationship with users' academic backgrounds. Results demonstrate that manual adjustment significantly enhanced the accuracy of document element identification and classification, with experienced participants achieving superior correction precision. Furthermore, usability feedback revealed a strong correlation between user satisfaction and system design, providing valuable insights for future system enhancement and development.
Aircraft Anti-Icing Analysis: Water Droplet Dynamics Under High-Frequency Atomization and Superhydrophobic Effects
Thomas Fernandez, Kevin (Virginia Tech, 2025-03-21)
Structural icing is a significant engineering challenge that has prompted extensive research into thermal and mechanical preventive measures. Common solutions involve the spraying of de-icing chemicals and high-power consumption heating systems for larger aircraft that add to the weight. Still, complexities arise from water droplets freezing at supercooled levels. A novel approach uses the structure's vibration to induce atomization, a proposed active anti-icing method using high-frequency Piezoelectric Transducer (PZT) vibration and combines it with the passive method of surface roughness variation by fabricating superhydrophobic surfaces. The study analyzes the droplet impact at 3 speeds. The impact is recorded with high-speed imaging using selected resonant frequencies (between 6 kHz and 25.6 kHz) to determine the optimal range for atomization. The study of the active method of atomization involved adjusting the frequency applied (as single and a sweep of frequencies) to the transducer material attached to an aluminum flat plate at a constant AC voltage supply, and variation of droplet velocity parameters. The best actuators are selected and determined through the analysis of frequency response and the magnitudes of the amplitude of vibration that are generated. The effect of single and sweep frequencies on the droplet dynamics is studied by analyzing 3 quantities: the Spread factor, the Volume ejected per ms (Vatomized), and the total energy (Eatomized) of the atomized droplets. The combination of the three helps to determine three key outcomes: The dynamics of the droplet, the change in dynamics due to vibrations, and the most effective atomization. It is observed that during atomization, Wenzel state (Hydrophilic) pining becomes more prevalent in the droplet as opposed to a non-vibrating static surface. Vibration also promotes spreading, meaning thinner droplet lamella (droplet height on the surface) and more surface area contact, thereby higher wetting. Furthermore, the more it spreads, the larger the volume of water is ejected. It was observed that the total energy (sum of Kinetic and potential energies) of ejected droplets have an inverse relation with the increase in Reynolds number. As the droplet speed increases in Re from ≈ 548 to ≈4797, the Eatomized reduces. Most notably, due to pinning, suggesting an increase in surface energy that promotes hydrophilic behavior and also the higher energy required to eject a droplet from a wider cross-section area (as the spreading increases with increase in Re). This research examines droplet interaction using parameters from both single-frequency and swept-frequency atomization, including the spread factor, Vatomized and Eatomized, to study droplet interaction. Here, swept frequencies exhibited less spatial dependency on droplet deposition while maintaining atomization rates, volumes, and energy levels comparable to those of single frequencies. Additionally, it explores the effects of combining atomization with a superhydrophobic surface, further improving the anti-icing characteristics. The study also establishes protocols for Abaqus FEA to simulate the frequency response of a PZT attached to a flat plate and outlines the design and construction of a supercooling chamber.
Gait Phase Estimation and Foot Trajectory Prediction During Dynamic Walking Using Gated Recurrent Units
Wisman, Hayley (Virginia Tech, 2025-03-21)
In the field of assistive robotics and exoskeletons, foot trajectory prediction has the potential to play a pivotal role in improving the functionality and user experience of worn devices. Rather than operating as a reactive system which only responds to user movement, a de vice which predicts future foot position can anticipate an action before it occurs, reducing latency and moving with the wearer for a more natural, uninhibited motion. While previous studies have focused on predicting continuous motion, they often overlook critical transitions between walking and standing, which are essential for natural locomotion. We propose in this study a foot trajectory prediction approach which leverages a recurrent deep learning architecture to make predictions based on sequential walking data. The first of the two ma chine learning models predicts the gait phase as a value between 0 and 1, while the second model leverages the gait phase prediction output to predict foot position in three dimensions. The models were trained and evaluated on IMU sensor data collected from three subjects instructed to walk on a treadmill at speeds varying from 0.5 mph to 1.5 mph. The result ing mean absolute error on gait phase percentage across subjects and velocity was 1.92%. For foot trajectory prediction, the cross-subject trained model achieved mean distance er ror of 2.85±2.89 cm, 3.29±2.82 cm, 4.15±4.12 cm, 5.33±5.46 cm, and 6.92±6.56 cm with prediction horizons of 0.1s, 0.25s, 0.5s, 1s, and 2s, respectively.
Virginia Logging Business Economic Sustainability Survey Including Perspectives from Across the Forest-based Supply Chain
Barkman, Rebecca Ann (Virginia Tech, 2025-03-20)
Logging businesses are an essential component of the forest-based supply chain. They are the connection between forest landowners, who grow the raw materials, and forest product mills that produce primary forest products. They are confronted with many operational challenges and issues that can make operating sustainably, producing a profit, and obtaining long-term economic viability seem unattainable. Although other businesses have similar operational challenges, logging businesses are somewhat unique in that they have minimal influence over delivered prices or the cost of stumpage, so changes in variable input costs can have large impacts on businesses' economic sustainability. Logging business operational challenges include increasing input costs such as equipment purchase costs, fuel costs, and equipment maintenance and repair costs. The economic sustainability of logging businesses affects the entire forest-based supply chain because one segment cannot function successfully without the others. This project evaluated operational characteristics, challenges, and issues related to the economic sustainability of logging businesses in Virginia from May through July 2023. A comparison was conducted using opinions and perspectives from professionals in other segments of the forest-based supply chain, on their outlook for the logging industry in Virginia concerning economic sustainability. Mail questionnaires, following the Dillman Method, were used to collect data from the survey populations which included logging business owners, consulting foresters (landowner representatives), and mill owners or procurement representatives. The response rates for logging businesses, mills, and consultant foresters were 27, 40, and 69 percent respectively. The top two challenges logging businesses faced in Virginia were fuel related. The number one challenge reported by logging businesses was fuel costs for in-woods harvesting equipment followed by fuel costs for trucks. Only 32.9% of logging businesses reported they were profitable in the past year. Only 26.1% of businesses had an outlook that their business was economically sustainable while 38.8% reported that their business was not sustainable. A greater percentage of mills (56.3%) and consultants (68.9%) reported their outlook for logging businesses was not economically sustainable. There were many neutral perspectives from all populations, however small positive changes in market conditions could move responses to the positive side of neutral. This study identifies the challenges in the industry as well as perspectives on the future of the forest industry's economic sustainability. The results of the study should be used as a catalyst encouraging segments of the industry to work together to address challenges and find solutions.
Role of PERM1 in the Development of Insulin Resistance and Diabetic Cardiomyopathy During High-Fat Diet Feeding
James, Amina N.'Kechi (Virginia Tech, 2025-03-20)
Heart failure is a leading cause of death in the United States, impacting approximately 6.7 million people. Several comorbidities are associated with heart failure, contributing to adverse clinical outcomes. Among these comorbidities, diabetes is highlighted as a prominent risk factor for heart failure, with approximately 20-40% of heart failure patients having type 2 diabetes. As the prevalence of heart failure continues to rise, there is a need for novel therapeutic methods to address this concern. PPARGC1 and ESRR Induced Regulator in Muscle 1 (PERM1) is a striated muscle-specific regulator of mitochondrial bioenergetics, predominantly expressed in skeletal and cardiac muscle. Our group has previously demonstrated that PERM1 is downregulated in both human and mouse failing hearts, and that Perm1-knockout mice exhibit reduced cardiac contractility and energy reserve. However, the role of PERM1 in cardiac dysfunction in diabetes remains unknown. We hypothesized that loss of PERM1 increases vulnerability to metabolic insults and exacerbates diet-induced insulin resistance and cardiac dysfunction. To test this, C57BL/6N male wild-type (WT) and Perm1-knockout (Perm1-KO) mice were fed either a normal diet or a high-fat diet (HFD; 60% calories from fat) for up to 43 weeks. We found that PERM1 expression was upregulated in the hearts of WT mice after 8 weeks of HFD feeding, coinciding with an increased level of carnitine palmitoyltransferase 2 (CPT2), a key enzyme involved in mitochondrial fatty acid uptake. Importantly, both WT and Perm1-KO mice exhibited similar increases in total body weight, fat mass, and fasting blood glucose levels throughout 43 weeks of HFD feeding, suggesting that loss of PERM1 did not accelerate the development of either obesity or diabetes. Echocardiographic assessments showed that WT mice maintained systolic and diastolic function, despite moderate cardiac remodeling, manifested as a subtle but significant increase of left ventricle posterior (LVPW) wall thickness. Unexpectedly, 8 weeks HFD feeding partially restored systolic function in Perm1-KO mice with no change in LVPW thickening. These findings show that while HFD feeding induced obesity and insulin resistance, its effect on cardiac function was relatively moderate and neither was exacerbated by the loss of PERM1. Unexpectedly, this study suggests that HFD feeding in Perm1-KO mice could partially compensate for cardiac dysfunction.

Browse

Recent Submissions