Masters Theses

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

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Development of an Automated Coin Grading System: Integrating Image Preprocessing, Feature Extraction, and ML Modeling
Chen, Jianzhu (Virginia Tech, 2024-12-20)
For more than 70 years, the Sheldon Coin Grading Scale has been essential in quantifying the value of coins within the coin collecting industry. Traditionally, coin grading has relied on human graders who may deliver inconsistent results. This inconsistency leads to variations in coin values. In this thesis, we present an automated coin grading system that uses image preprocessing, feature extraction, and advanced machine learning techniques to predict the grade across different coin types. Our system employs synthetic reference masks to identify "expected" regions, like the contours of reliefs, and "unexpected" regions, such as surface non-uniformities. All detected significant elements and tiny elements, extracted from these regions, will serve as one of the feature sets. Additionally, we extract color histograms as another feature set to analyze color and texture in detail. Both feature sets from the obverse and reverse sides of the coins are processed using a multi-layer perceptron (MLP) model and a random forest model. The best-performing model is then selected to grade the coins by analyzing their overall wear patterns and color characteristics. Our grading system has demonstrated an accuracy of up to 91.3% in predicting the Sheldon Grading Scale across five coin types, allowing for a grading tolerance of ±4. For a single coin type (Franklin Half Dollar), it has achieved an accuracy of up to 95.1% with a tolerance of ±1.
An XR-Driven Digital Twin Platform for Cybersecurity Education
Lee, Anthony Sung Ning (Virginia Tech, 2024-12-20)
This thesis investigates the application of digital twins as an educational tool within the domain of cybersecurity, specifically targeting the infrastructure of water treatment plants. A digital twin is a precise virtual model of a physical asset, process, or system, capturing its state, behavior, and interactions in real-time. By integrating live sensor data, historical records, and predictive models, digital twins replicate their physical counterparts with high fidelity, enabling detailed simulations, monitoring, diagnostics, and analytics. This technology supports improved decision-making, predictive maintenance, and operational efficiency across industries by allowing safe testing and evaluation of modifications without altering physical assets. A case study is presented to demonstrate an immersive experiential learning platform that leverages digital twins to provide cybersecurity education. The platform aims to enhance user engagement and reinforce learning by offering hands-on experiences in a controlled virtual environment. In addition, we provide a cost-efficient hardware solution that represents the physical side of the digital twin as connecting it to the actual water treatment plant hardware is unfeasible. The study compares AI-guided learning, facilitated by a Conversational AI agent utilizing Large Language Models, against a non-AI-guided approach. This comparison evaluates the effectiveness of AI in guiding users naturally through the learning process, thereby examining the potential of digital twins to support efficient, cost-effective education across diverse sectors. The results show that presence is significantly increased with the help of an AI character while other qualities and factors remain unaffected. However, we see learning improvement overall and received positive feedback regarding the system. Users liked the digital twin concept and felt like it really helped them understand the concept thoroughly.
Supplementation of Chromium Propionate Positively Impacts Reproductive Performance of Beef Females
Vidlund, Trinity (Virginia Tech, 2024-12-19)
Return to estrus following the postpartum interval to achieve pregnancy success on time is a considerable obstacle for beef females. Chromium supplementation increases available glucose and insulin sensitivity within cells. Two experiments were conducted to investigate the effects of supplementing Chromium propionate (CrP) during the peripartum until weaning on productive and reproductive performance in Bos taurus beef cows. In Exp. 1, 62 Angus-based beef cows were stratified by predicted calving date, body weight (BW) and randomly assigned to one of two treatments: 1) CON, (n=30) supplementation of corn gluten, soy hull pellet feed (50:50) with a mineral pack at 1 kg -1hd-1d; or 2) TRT, (n=32) supplementation at 1 kg -1hd-1d of corn gluten, soy hull pellet feed (50:50) with a mineral pack containing 1.4 g of Chromium Propionate (KemTRACE® Chromium 0.4%, Kemin Industries, Inc., Des Moines, IA). Cows remained on a single pasture equipped with SmartFeed trailers for individual supplement intake (SmartFeed®, C-lock Inc., Rapid City, SD). The experiment lasted 98 days, starting 63 days pre-breeding to 35 post-fixed-time artificial insemination (TAI). Ovarian ultrasonography was performed on days -10, -3, 0 (TAI Day), and 7 to determine the diameter of the largest follicle and corpus luteum (CL) volume. Age, days postpartum (DPP), initial and final BW, and supplement intake were similar (P>0.05) between treatments. However, TRT cows had a larger follicle (P=0.028) on d 0, increased CL volume (P=0.038), and increased (P=0.0213) circulating progesterone (P4) on day 7. In Exp. 2, 953 beef cows across nine locations were assigned to one of two treatments: 1) CON, supplementation of a mineral product at 113 g -1hd -1d (n=464 cows; 16 experimental units); or 2) TRT, supplementation of mineral product at 113 g -1hd -1d containing 1.4 g of CrP n=489 cows; 16 experimental units). Supplementation started approximately 37 days pre-calving and continued until weaning for 345 days. Age, DPP, d-10 body condition score (BCS), initial and final BW, BCS, calf birth and weaning weight, and mineral disappearance were similar (P>0.05) between treatments. However, CrP cows tended (P=0.081) to have greater estrus expression (68.3 and 60.2 ± 3.1 %, for CrP and CON, respectively) and greater (P=0.045) TAI pregnancy rates (55.2% vs. 49.9% ± 2, for CrP and CON, respectively). We conclude that supplementation of CrP to beef cows during the peripartum through weaning did not affect BW or BCS, but increased ovulatory follicle diameter, estrus expression, CL volume, and P4 concentration, and one or more of these positive effects of CrP likely contributed to the improvement in TAI pregnancy rate.
Structural Dynamics and Electrostatic Properties of the VEGF and PIM-1 Oncogenic Promoter G-Quadruplexes from Polarizable Molecular Dynamics Simulations
Fogarty, Rebekah Joy (Virginia Tech, 2024-12-19)
G-Quadruplexes (GQs) are higher ordered nucleic acid structures that form within regions of DNA and RNA that are enriched with guanine nucleobases. These structures are highly stable and have been shown to function in genomic maintenance and regulating key biological processes. Due to their role in regulating gene expression, GQs also contribute to a wide variety of human diseases including neurodegenerative conditions, premature aging disorders, and various cancers. Therefore, these structures have gained growing interest as the subjects of various research investigations to explore potential methods for targeting and disease management on transcriptional and translational levels. However, targeting efforts have been relatively unsuccessful due to the conserved GQ core structure, leading to compounds that cannot bind to their targets with sufficient specificity. Here, we employed conventional and enhanced sampling molecular dynamics simulations on two oncogenic GQ structures with the Drude polarizable force field to gain crucial insights into structural and electrostatic properties contributing to overall GQ stability and potential small-molecule binding sites. In addition to these simulations, we also subjected these structures to the Site Identification by Ligand Competitive Saturation workflow to determine the favorability of various functional groups and gain insights into preferential binding of these GQ structures.
Data driven modeling and MPC Based control for Pathological Tremors
Samal, Subham Swastik (Virginia Tech, 2024-12-19)
Pathological tremor is a common neuromuscular disorder that significantly affects the quality of life for patients worldwide. With recent developments in robotics, rehabilitation exoskeletons serve as one of the solutions to alleviate these tremors. For improved performance of such devices, we need to solve a few problems, which include developing a model for pathological tremors, and a safe control system that can conveniently incorporate constraints on the wrist's range of motion and it's input force/torque. Accurate predictive modeling of tremor signals can be used to provide alleviation from these tremors via various currently available solutions like adaptive deep brain stimulation, electrical stimulation and rehabilitation orthoses. Existing methods are either too general or too simplistic to accurately predict these tremors in the long term, motivating us to explore better modeling of tremors for long-term predictions and analysis. We explore towards the prediction of tremors using artificial neural networks using EMG signals, leveraging the 20- 100 ms of Electromechanical Delay. The kinematics and EMG data of a publicly available Parkinson's tremor dataset is first analyzed, which confirms that the underlying EMGs have similar frequency composition as the actual tremor. 2 hybrid CNN-LSTM based deep learning architectures are then proposed to predict the tremor kinematics ahead of time using EMG signals and tremor kinematics history, and the results are compared with baseline models. This is then further extended by adding constraints-based losses in an attempt to further improve the predictions. Then, we explore the application of model-based predictive control (MPC) for the full wrist exoskeleton designed in our lab for the alleviation of tremors. The main motivation for using MPC here relies on its ability to incorporate state and input constraints, which are crucial for the user's safety. We employ a linear MPC methodology, in which the forearm-exoskeleton model is successively linearized at each time sample to obtain a linear state space model, which is then used to obtain the optimal input by minimizing a convex quadratic cost function. This is then integrated with the tremor model developed via BMFLC and neural networks to provide tremor suppression. Simulation studies are provided to demonstrate the effectiveness of the control schemes. The numerical simulations suggest that the MPC framework is capable of accurate trajectory tracking while providing better tremor suppression than a PD controller without using any tremor model, while the neural network model outperforms the frequency-based BMFLC model. The findings could set up for devising physics-based Neural networks for pathological tremor modeling and experimentally evaluate the performance of the developed framework.
Characterizing the Movement of Contaminant Liquid Metal Particles when subject to Electric Pulses, within the Context of Z-pinch Fusion Reactors
Sridhar, Vignesh (Virginia Tech, 2024-12-18)
This thesis examines the behavior of contaminant liquid metal particles, known as ejecta, which are expelled from magnetically-accelerated liquid metal surfaces, in Z-pinch fusion reactors. These ejecta present significant contamination risks to plasma fuel in fusion reactors. The study employed a liquid electrode setup, exposing a eutectic tin-bismuth mixture to high-current pulses. Particle motion was tracked and analyzed using high-speed camera footage processed with advanced techniques, including contrast limited adaptive histogram equalization (CLAHE), neutral-density filtering, and Sobel edge detection. The research integrates automated particle tracking algorithms with connected-component labeling and two-point correlation functions to monitor ejecta trajectories. Results reveal an average ejected particle velocity of 2.54 m/s (±3.58 m/s), with particle formation likelihood peaking at velocities around 1.14 m/s. These findings indicate that particle ejection dynamics are influenced by factors such as initial temperature, vacuum conditions, and electrostatic forces. This research provides crucial insights for optimizing reactor design and mitigating contamination in future fusion energy applications. The study also proposes directions for further investigation, including the temperature dependency of particle ejection dynamics and the implementation of improved heating systems for experimental setups.
Development of Open-Source Gantry-Plus Robot Systems for Plant Science research
Kaundanya, Adwait Anand (Virginia Tech, 2024-12-19)
Affordable and readily available automation options for plant research remain scarce, however with the availability of such a system, many research tasks can be streamlined. In this project, we demonstrate a prototype of such an open-source, low-cost, heterogeneous robotic system called Mini T-Rex. We combine two over-the-counter robots and leverage the ROS2 framework to control this heterogeneous system. This system provides a unique advantage of sensor-to-plant method to capture multi-view images at any angle and distance within the workspace. We demonstrate how making a digital twin in ROS2 can help to control a heterogeneous system via abstracted hardware control. We also talk about I2GROW Oasis which is a robotic system consisting of a remotely controlled robot with the ability to capture top-view images. In this thesis we describe the hardware and software design of both these robotic systems. To use this robotic system, the plants can be grown on a growth bed or a hydroponic system below the Mini T-Rex robot, and the camera will approach the plant without any contact with the plants due to the precise control of the robotic manipulator. We used the system to capture several large data sets of 3D phenotypic data for Solanum lycopersicum, Lactuca sativa, and Thlaspi. In conclusion, we have developed a 9-degree of freedom, fully open-source heterogeneous robotic system capable of multi-view, camera-to plant image capture for plant 3D model reconstruction called Mini T-Rex. We show how to use gantry like robots for phenotyping and create longitudinal datasets by automating these high precision robotic systems.
μJUNITER: Automated Unification of Microservices into Modular Monoliths for Versatile Software Architecture Migration
Martin, Joshua Scott (Virginia Tech, 2024-12-19)
Although software architecture aims for long-term stability, architectural migrations may be necessary to adapt to evolving business needs and resource availability. In recent years, numerous migrations have occurred within backend architectures. While many of these migrations partition monoliths into microservices, several prominent enterprises have reported having to unite microservices into monoliths to reduce management and communication overheads. The required unification is typically manual, substantial, tedious, and error-prone for complex systems. This thesis presents a novel automated architectural refactoring that transforms a microservice system into a functionally equivalent monolith. Our refactoring relies on abstract syntax tree merging and control flow bridging to unite distributed components into a centralized system. We have implemented our approach as μJUNITER, an automated refactoring tool that operates on Java source files of Spring Boot microservices, producing Spring Boot modular monoliths. Evaluations conducted with third-party and synthetically generated microservice applications, with up to 100 microservices, demonstrate that μJUNITER's refactoring preserves the original system's functionality, as verified by end-to-end and back-end unit testing. The refactoring also reduced latency in certain cases and resource consumption in all cases. μJUNITER's refactoring saves manual programming effort proportional to the number of microservices and their level of inter-service interaction. As software architectures must adapt to changing trends, our approach can complement and enhance the existing automated toolset for architectural migration.
Transforming Free-Form Sentences into Sequence of Unambiguous Sentences with Large Language Model
Yeole, Nikita Kiran (Virginia Tech, 2024-12-17)
In the realm of natural language programming, translating free-form sentences in natural language into a functional, machine-executable program remains difficult due to the following 4 challenges. First, the inherent ambiguity of natural languages. Second, the high-level verbose nature in user descriptions. Third, the complexity in the sentences and Fourth, the invalid or semantically unclear sentences. Our first solution is a Large Language Model (LLM) based Artificial Intelligence driven assistant to process free-form sentences and decompose them into sequences of simplified, unambiguous sentences that abide by a set of rules, thereby stripping away the complexities embedded within the original sentences. These resulting sentences are then used to generate the code. We applied the proposed approach to a set of free-form sentences written by middle-school students for describing the logic behind video games. More than 60% of the free-form sentences containing these problems were sufficiently converted to sequences of simple unambiguous object-oriented sentences by our approach. Next, the thesis also presents "IntentGuide," a neuro-symbolic integration framework to enhance the clarity and executability of human intentions expressed in freeform sentences. IntentGuide effectively integrates the rule-based error detection capabilities of symbolic AI with the powerful adaptive learning abilities of Large Language Model to convert ambiguous or complex sentences into clear, machine-understandable instructions. The empirical evaluation of IntentGuide performed on natural language sentences written by middle school students for designing video games, reveals a significant improvement in error correction and code generation abilities compared to previous approach, attaining an accuracy rate of 90%.
EASE-E: Edge-AI based System for Energy-Efficiency in Autonomous Driving (ADAS/AD)
Kothari, Aadi Jay (Virginia Tech, 2024-12-16)
The rise of Software-Defined Vehicles (SDVs) has rapidly advanced the development of Advanced Driver Assistance Systems (ADAS) and Autonomous Vehicle (AV) technology. However, as compute and sensing architectures for SAE Level 2 vehicles increasingly lean towards fully centralized systems, significant concerns arise regarding their energy demands. This shift may have a negative impact on one of the most critical purchasing factors for Battery Electric Vehicles (BEVs): electric driving range. This thesis investigates the potential benefits of decentralization in automotive Electrical/Electronic (E/E) architecture, powered by System-on-Module (SoM) Edge-AI boards. By facilitating efficient deep learning processing locally, the proposed EASE-E (Edge-AI based System for Energy Efficiency) solution achieves up to a 5x reduction in power consumption while maintaining high processing performance. Through a combination of bench testing and Software-in-the-Loop (SiL) simulations, this research demonstrates that EASE-E enhances energy efficiency by 32.8% in highway driving, and 10.8% in urban environments. EASE-E also offers greater scalability and resilience when compared to the existing E/E architectures: distributed, domain, and zonal. The findings underscore the potential of this solution to preserve and extend the driving range of BEVs, presenting a compelling alternative to a fully centralized approach. These insights are crucial for the future design of scalable, energy efficient, and autonomous software-defined vehicles.
AI-Driven Pig Monitoring System: Behavior and Weight Analysis
Ranjan, Pranjal (Virginia Tech, 2024-12-12)
This thesis advances automated pig monitoring through novel machine learning approaches in behavior analysis, weight prediction and forecasting. For behavior analysis, we introduce a preprocessing framework that addresses data leakage in time series analysis through non-class-based windowing and chronological sampling, achieving up to 15% improvement in accuracy over conventional methods. For current weight prediction, we develop an automated pipeline using the Segment Anything Model (SAM) with deep learning, where our Xception-Net architecture achieves a mean absolute percentage error of 7.42%. For weight forecasting, we propose multi-input deep learning architectures combining spatial and temporal features, achieving a mean absolute percentage error of 5.56%. These methods demonstrate robust performance in real-world conditions while minimizing animal stress and manual labor requirements, contributing significantly to precision livestock farming practices.
A study of the history of standardizing Virginia apples
Motz, F. A. (Virginia Agricultural and Mechanical College and Polytechnic Institute, 1929)
Evaluating the Capability of ICON-MIGHTI to Detect Plasma Bubbles in the Ionosphere
Lech, Brenden (Virginia Tech, 2024-12-09)
The MIGHTI airglow imager onboard the ICON spacecraft in LEO was built to make remote thermospheric windspeed measurements at low latitudes. The MIGHTI team, when reviewing the data, observed variations in day-to-day brightness potentially indicative of plasma bubbles: regions of low-density E-region plasma which rise through the F-region and cause radio scintillation that interferes with communications and GPS performance. Here, we explore the possibility of MIGHTI observing plasma bubbles by using its red-line airglow measurements to attempt to detect this phenomenon. Small-scale structuring indicative of plasma bubbles is searched for by comparing measurements between MIGHTI's two identical imagers, which make remote airglow measurements at the same region from perpendicular directions. The usability of the two imagers for this purpose is assessed, given they are not calibrated to measure absolute airglow brightness, and it is determined that the level of disagreement between them does not prevent these comparisons. The evolution of the ionosphere in the time between the two instruments' measurements is accounted for using seasonal medians of expected behavior. Co-located measurements where the two MIGHTI imagers disagreed significantly were found, filtering out disagreements in measurement not likely to have a significant underlying ionospheric cause, although none were indicative of plasma bubble observations. These significantly differing measurements were most common shortly after dusk and in regions near the equator, especially between -30 to 70 degrees longitude. Simulations show the lack of definitive plasma bubble detections is likely due to MIGHTI's long image exposure time averaging out the effect of plasma bubbles as ICON orbits. More is now known about the potential for making comparative red-line airglow measurements between MIGHTI's imagers, and this information could be used in future work to explore larger-scale ionospheric structuring within the MIGHTI dataset.
Reinforcement Learning for the Cybersecurity of Grid-Forming and Grid-Following Inverters
Kwiatkowski, Brian Michael (Virginia Tech, 2024-12-06)
The U.S. movement toward clean energy generation has increased the number of installed inverter-based resources (IBR) in the grid, introducing new challenges in IBR control and cybersecurity. IBRs receive their set point through the communication link, which may expose them to cyber threats. Previous work has developed various techniques to detect and mitigate cyberattacks on IBRs, developing schemes for new inverters being installed in the grid. This work focuses on developing model-free control techniques for already installed IBR in the grid without the need to access IBR internal control parameters. The proposed method is tested for both the grid-forming and grid-following inverter control. Separate detection and mitigation algorithms are used to enhance the accuracy of the proposed method. The proposed method is tested using the modified CIGRE 14-bus North American grid with 7 IBRs in PSCAD/EMTDC. Finally, the performance of the detection algorithm is tested under grid normal transients, such as set point change, load change, and short-circuit fault, to make sure the proposed detection method does not provide false positives.
Optical Nanoantennas Integrated with 3D Microelectrode Arrays: Hybrid Photonic-Electronic Modalities for Nano-Bio Interfacing
Mejia, Elieser A. (Virginia Tech, 2024-11-08)
The human body is dynamic and understanding such complexity for accurate diagnostics and therapies remains a challenge due to lack of minimally-invasive biotechnologies capable of long-term measurements of various biochemical and bioelectrical signals simultaneously from single cells to cell networks. Biocompatibility is a major challenge but recent advancements in micro- and nano-fabrication has shown that patterned protruding pillars from surfaces at the micro- and nano-scale can mimic intrinsic biological structural cues to trigger strong cell adhesion, engulfment, and growth, providing a means by which to engineer the biocompatibility for controlled cell-device bio-interfaces. Here, we sought to leverage the unique biocompatibility of engineered three-dimensional (3D) features with integrated biochemical and bioelectrical sensor arrays to create a multi-modal platform for complex systems biological research. For the biochemical sensor, we introduced a tunable optical nanoantenna that is driven wirelessly by incident laser light (photons) to create a highly localized electric field capable of enhancing the photon scattering rate of nearby chemical bonds, a unique signature that provides a means to fingerprint the local biomolecular ensembles depending on the color of detected scattered photons. By a novel scalable fabrication technique, we merged such nano-sensors with 3D micropillar electrode arrays to create a device with hybrid biophotonic and bioelectronic functionality. We revealed the unique optical properties by micro-reflectance measurements and numerical simulations and verified by spectroscopic measurements a million-fold enhancement to the scattered photon signature from a standard chemical monolayer. We showed favorable bioelectrical properties by electrochemical impedance spectroscopy and cyclic voltammetry, revealing a stable electrochemical interface and reduced resistance due to 3D geometry enabling improved transduction of electrical signals, useful for higher signal to noise ratios in bioelectrical measurements. Overall, we demonstrated the scalable fabrication and unique optical and electrical properties suitable for next generation multi-modal bio-interfacing platforms.
Enhancing Capabilities of Assistive Robotic Arms: Learning, Control, and Object Manipulation
Mehta, Shaunak A. (Virginia Tech, 2024-11-11)
In this thesis, we explore methods to enable assistive robotic arms mounted on wheelchairs to assist disabled users with their daily activities. To effectively aid users, these robots must recognize a variety of tasks and provide intuitive control mechanisms. We focus on developing techniques that allow these assistive robots to learn diverse tasks, manipulate different types of objects, and simplify user control of these complex, high-dimensional systems. This thesis is structured around three key contributions. First, we introduce a method for assistive robots to autonomously learn complex, high-dimensional behaviors in a given environment and map them to a low-dimensional joystick interface without human demonstrations. Through controlled experiments and a user study, we show that this approach outperforms systems based on human-demonstrated actions, leading to faster task completion compared to industry-standard baselines. Second, we improve the efficiency of reinforcement learning for robotic manipulation tasks by introducing a waypoint-based algorithm. This approach frames task learning as a sequence of multi-armed bandit problems, where each bandit problem corresponds to a waypoint in the robot's trajectory. We introduce an approximate posterior sampling solution that builds the robot's motion one waypoint at a time. Our simulations and real-world experiments show that this approach achieves faster learning than state-of-the-art baselines. Finally, to address the challenge of manipulating a variety of objects, we introduce RIgid-SOft (RISO) grippers that combine soft-switchable adhesives with standard rigid grippers and propose a shared control framework that automates part of the grasping process. The RISO grippers allow users to manipulate objects using either rigid or soft grasps, depending on the task. Our user study reveals that, with the shared control framework and RISO grippers, users were able to grasp and manipulate a wide range of household objects effectively. The findings from this research emphasize the importance of integrating advanced learning algorithms and control strategies to improve the capabilities of assistive robots in helping users with their daily activities. By exploring different directions within the domain of assistive robotics, this thesis contributes to the development of methods that enhance the overall functionality of assistive robotic arms.
Seeds That We Keep: Grounding Seedkeeping Praxis for Growing Black Food Futures in the Mid-Atlantic
Madden, Justice Makynzee (Virginia Tech, 2024-12-03)
Reform within food justice initiatives calls for emergent strategies and practices that align with pursuits of justice, health equity, ecological sustainability, and collective social change. Examining historical and contemporary Black geographies of the Mid-Atlantic region of the United States offers valuable lessons on what grows and thrives in opposition to plantation logic. As both material and immaterial representations of the genesis of life, seeds serve as catalysts for understanding stories of praxis, where seedkeeping traditions and contemporary experiences radically reimagine and contest the imposition of colonial legacies. Theoretically grounded in Black feminist futurities, this research illuminated the relationship between radical tradition and radical imagination to understand the complex landscapes of Black liberation through stories of past, present, and future relationships to seeds. The everyday stories from Black seedkeepers articulate visions for equitable food systems and provide specific insights into how a seedkeeping praxis manifests and forms of community cultural wealth and self-determination that challenge the ongoing commodification of seeds. Focusing on the Virginia, Maryland, and Washington, D.C. where these geographies are deeply shaped by colonial sites with legacies of slavery, land theft, and a genesis of American agriculture that created the foundation for global capitalism, this project delved into the narratives of 17 Black seedkeepers from. By engaging with seedkeepers' memories and motivations this inquiry also lays the foundation for understanding how narratives articulate collective hopes for food sovereignty through seeds.
Improving communication in the aspiring stand-up comedy community
Reis Farina, Katharina (Virginia Tech, 2024-12-03)
This thesis aims to fill a gap in academic literature regarding stand-up comedy, particularly research about relationships between comedians in a social media setting. The research analyzes such relationships with the uses and gratifications approach, which ties the use of a particular medium of mass communication with gratifications expected by the audience. The first objective is to understand whether the gratifications that comedians expect from social media when networking and looking for performance opportunities are fulfilled. The second is to propose a design solution that could better provide these gratifications. The research included four different methods: a survey, a series of interviews, a prototype and a focus group. Results from the survey showed that the gratifications sought by comedians are obtained in current social media platforms. The interviews revealed that there were still specific features missing that could benefit the comedians. The information gathered in the first two phases informed the features in the prototype of a mobile application. In the fourth phase, comedians participated in a focus group where they analyzed the prototype, with an overall positive impression. The comedians also gave suggestions for improvements that can inform future research.
The Influence of Environmental Conditions and Co-Occurring Parasites on Blood Physiology in Eastern Hellbenders (Cryptobranchus alleganiensis)
Slack, Katherine Louise (Virginia Tech, 2024-12-02)
Climate change, habitat degradation, and infectious disease are major drivers of global amphibian declines. Amphibians are particularly susceptible to these factors due to their unique physiology and habitat requirements. Thus, investigating components of amphibian physiology and evaluating the influence of environmental conditions, perceived threats, and encounters with infectious agents is essential to conserving imperiled amphibian species. Here, I measured hematocrit, hemoglobin, relative proportions of polychromatic red blood cells, and white blood cell differentials in a wild population of male Eastern Hellbender salamanders (Cryptobranchus alleganiensis) which remain with their nest for up to ~8 months and have frequent encounters with parasitic leeches (Placobdella appalachiensis) that transmit hematophagous endoparasites (Trypanosoma sp.), often resulting in coinfection. Results indicate that hematocrit and hemoglobin increase in response to acute stress and with temperature. Additionally, the magnitude of the stress-induced hemoconcentration response was greater at lower temperatures. Hellbenders exhibited an increase in the proportion of neutrophils and eosinophils in circulation as temperatures decreased while the proportions of lymphocytes and basophils had an inverse effect. Furthermore, the proportion of neutrophil precursors also increased as temperature decreased, which signifies recruitment of innate immune cells during seasonally cold periods. Coinfection of the leech and trypanosome parasites resulted in decreased hematocrit and hemoglobin and a marked increase in polychromatic red blood cells which is indicative of regenerative anemia in the hellbender host. However, these effects were not present in individuals only infected with trypanosomes, implicating the leech vector as the key contributor to anemia in hellbenders. Moreover, the proportion of neutrophils and eosinophils increased, while lymphocytes decreased, in response to leech attachment. However, as parasitemia of leeches and trypanosomes increased concurrently, the proportion of lymphocytes increase in circulation while neutrophils and eosinophils decrease, underscoring the complexities associated with coinfection and multi-parasite interactions. Together, this research provides novel insights into the blood physiology of an imperiled salamander by establishing reference values essential for population surveillance while also describing how these values fluctuate across season and in response to extrinsic factors, with an emphasis on co-occurring hematophagous parasites.
A continuous-process grain drier
Chambliss, Campbell Good (Virginia Polytechnic Institute, 1950)

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