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Novel experimental techniques for pressure transients
No full textThis dissertation introduces novel experimental techniques for analyzing pressure transients, focusing on dynamic strain and pressure measurements to advance the understanding and application of Pulse Pressure Amplification in percussive devices. The research evaluates a high-speed linescan extensometer utilizing linescan Digital Image Correlation (DIC) on a Kolsky Bar to obtain strain data at high strain rates. This data is critical for accurate material characterization and simulations to model dynamic behavior. Additionally, a novel method for measuring dynamic pressure waves in pipe systems has been developed, enabling direct assessments of pressure amplification in devices such as tapered pipe systems. The findings contribute significantly to material testing and dynamic analysis, providing robust tools for evaluating and optimizing percussive devices in industrial applications
Design, development, and validation of an automated hole-punching system for precision irrigation in lay-flat poly pipe tubing
No full textIncreasing pressure on water resources in agricultural regions, particularly the Mississippi Delta, demands innovative irrigation technologies to optimize water usage while maintaining crop productivity. This research presents the design, development, and validation of an automated hole-punching system for lay-flat poly pipe tubing used in precision irrigation applications. The system ensures accurate and consistent hole placement through an integrated rotary cutting die, a PLC-based control architecture, stepper motor actuation, and distance-sensing and position-synchronization. The software logic maintains precise hole spacing despite variations in tubing speed. System performance was evaluated through extensive testing and statistical analysis of hole-spacing accuracy, repeatability, and sagitta-based indexing. Results show high consistency, with a maximum hole-spacing standard deviation of 0.520 mm (CV 0.1342) and sagitta indexing standard deviation of 0.285 mm (CV 0.034). This automated system demonstrates high accuracy and repeatability, effectively addressing the variability issues associated with manual hole-punching processes
Evaluating the effects of biostimulants on apple coloration
No full textApple peel color is one determining factor of apple quality and is a highly sought after trait by both growers and consumers. Historically plant growth regulators (PGRs) such as daminozide and auxins have been used to induce a greater amount of red color in the peel, but these products typically have negative production impacts and raise environmental concerns. One new area of interest is in called biostimulants. This study investigated the effects of two experimental biostimulants (MCEN1 and MCEN2) applied at varying rates and timings, as well as a commercially available plant growth regulator (Blush® 2X), on ‘Honeycrisp’ apple color and quality characteristics at two locations (Virginia and Michigan). Results indicated that MCEN2 showed potential to influence apple coloration, especially early in the season. MCEN2 when applied twice, showed potential to influence color without negatively influencing other quality traits
Predicting mesoscale rapid-onset drying patterns in Michigan
No full textDewpoint bombs are a mesoscale, rapid-onset drying pattern which can impact fire weather predictions yet forecasting them remains challenging. This study uses surface observations to investigate dewpoint bomb occurrence in the National Weather Service’s Marquette Weather Forecasting Office (WFO) Fire Weather Area of Responsibility (AOR) between March 1 and November 30 from 2006 to 2024 across 14 sites. Sounding data is used to classify all bomb events as synoptically benign or not. Dewpoint bombs from high-fire risk days are modeled using logistic regression and support vector machine models and random forests. Results show that dewpoint bombs occur on 52% of days that meet relative humidity criteria for Red Flag Warning within the study period and that 13.4% of dewpoint bombs occur during synoptically benign conditions. Model results indicate that resolving lower free atmosphere, entrainment zone, and planetary boundary layer characteristics throughout bomb evolution is key to accurately forecasting dewpoint bombs
Nanocarbons for electrodes and electrolytes of sustainable and solid-state energy storage systems: materials, electrochemical characterization, and lifecycle analysis
No full textThe growing global demand for clean, efficient, and scalable energy storage technologies has intensified the search for sustainable alternatives to conventional battery and supercapacitor systems. Traditional electrochemical energy storage devices often rely on finite and environmentally harmful materials, toxic electrolytes, and rigid architectures that limit their applicability in emerging domains such as flexible electronics and wearable technologies. Additionally, the leakage of liquid electrolytes poses a persistent safety and reliability challenge, particularly for long-term and real-world applications. This research addresses these pressing challenges by developing a new class of green, flexible, and high-performance electrochemical energy storage systems using bio-derived materials. The study is structured around three core objectives: First, cellulose nanofibers extracted from pine wood were engineered into flexible electrode architectures for use in wearable supercapacitors, with optimization of their mechanical resilience and electrochemical properties. Second, a solid-state biofilm electrolyte was synthesized from lignin via environmentally benign methods, providing a leak-proof, flexible alternative to conventional liquid electrolytes for supercapacitor integration. Third, activated carbon derived from red oak biomass was developed as a sustainable anode material for lithium-ion batteries, demonstrating high surface area, favorable porosity, and stable electrochemical performance. Each component of this work contributes toward building a holistic platform for sustainable energy storage, one that emphasizes green processing, renewable feedstocks, and functional flexibility without compromising device performance. The integration of these bio-sourced materials into flexible solid-state devices demonstrates the feasibility of replacing conventional, unsustainable components with eco-friendly, high-performance alternatives. In conclusion, this dissertation presents a blueprint for a novel and interdisciplinary approach to electrochemical energy storage by uniting materials science, green chemistry, and energy engineering. The outcomes provide foundational insight into the development of next-generation, sustainable, and flexible electrochemical systems that are well-positioned to meet the demands of a low-carbon and circular economy
Leveraging Google Earth Engine for computationally efficient pixel-level analysis and vector delineation from satellite data.
No full textAnalyzing large-scale, high-resolution satellite imagery is a computationally intensive task requiring time and computing resources. This can be accelerated using cloud computing platforms such as Google Earth Engine (GEE) where computational and storage requirements can be scaled based on demand. However, cloud-based platforms for processing high-resolution imagery remain underutilized in environmental applications such as agriculture, and forest health. This thesis explored the application of GEE to two geospatial problems in agricultural conservation and disease mapping in forestry: 1) Extraction of agricultural field boundaries from Sentinel-2 satellite imagery, for use in conservation, precision agriculture, land management, and organization, etc., and 2) Mapping and evaluation of the phenology of Brown Spot Needle Blight (BSNB) (Mycosphaerella dearnessii) disease in Loblolly Pines (Pinus taeda) in Mississippi using Sentinel-2 imagery from 2019-2024, which can be used to support management actions for forest health. Computed agricultural field boundaries were compared with digitized polygons created by a subject matter expert, and the results showed high delineation accuracy ( 84This study aimed to speed up the analysis of large satellite images relevant to detecting and monitoring Lecanosticta acicola. It did this by developing efficient workflows within the Google Earth Engine (GEE) platform. By utilizing GEE’s cloud-based processing and extensive collection of multispectral and temporal satellite data, the study demonstrated that high-resolution geospatial analyses can be conducted quickly and at a large scale. This greatly lowered local computing needs. Combining satellite images with GEE enabled detailed mapping, improved the accuracy of mapping disease vectors, and made it possible to automate important analytical tasks. These improvements facilitate faster and more informed decision-making in managing agriculture and forestry, particularly in areas with limited resources or sensitive ecosystems
Viral dynamics in commercial honey bee queen production system
No full textViruses negatively impact the health of honey bee queens; however, their transmission dynamics in commercial queen production operations remain poorly understood. This study offers a comprehensive analysis of viral infection dynamics, transmission routes, and physiological stress responses in queens during developmental, mating, and storage phases. By combining field surveys and controlled experiments, we demonstrate that multiple viruses—particularly DWV-B, BQCV, and LSVs—are widespread and transmitted through vertical, horizontal, and sexual pathways. Mating and banking were identified as key periods of viral exposure and physiological stress. Although viral loads did not follow a consistent pattern during banking, the immune and oxidative stress responses decreased over time, indicating that queens mainly experienced stress from storage conditions rather than from virus infection. Overall, these findings improve understanding of how viruses and management stressors interact to shape queen health, highlighting the need for better practices to enhance queen quality and colony resilience
Mechanical properties of oak timbers and mats and toughness of loblolly pine after 0 to 6 months on the ground.
No full textTesting wood in various ways is important to be able to understand the strength and recovery values. The first part of this study aims to assess the design values of mixed oak in-grade, 8 in. thick timbers and mats under various design and loading scenarios. Graded timbers were drilled, assembled into mats, and tested. Results indicated that the mat with a line load across all timbers, was nearly equivalent to the mat with a load applied to the middle timber only. Drilling did not negatively impact strength as compared to solid non-drilled timbers. The second part of this study investigates toughness as an early indicator of deterioration. Approximately 38-year-old loblolly pine trees were felled and removed from the forest at 0, 3, 4, 5, and 6 months time on the ground. Specimens were prepared and tested. Statistically significant decreases in toughness were observed with increasing time on ground
Agricultural Producers’ Perceptions of a Regional Extension Agent System
No full textCooperative Extension in the United States plays a pivotal role in disseminating research-based knowledge to the public. This study investigates the impact of a regional Extension agent system on agricultural producers’ perceived access to Extension resources in Georgia. This research combines quantitative and qualitative data collection methods. Results reveal that Georgia agricultural producers strongly prefer locally stationed Extension agents for direct and personal communication. The study indicates that a regional agent system would limit perceived access to Extension resources, potentially leading producers to opt for private scouting services. Participants expressed concerns about restricted access under a regional system. The findings underscore the necessity of maintaining the county-based delivery system unless robust evidence demonstrates the success of regional agent systems. To remain relevant, Cooperative Extension must prioritize the preferences of agricultural producers and sustain the dynamic balance between in-person and online educational resources. This study contributes valuable insights to Extension services, advocating for continued local accessibility in the face of potential structural changes
Uncertainty-Aware Navigation for Offroad Robotics
No full textAutonomous off-road navigation presents challenges due to the unpredictable and unstructured nature of real-world terrains, where traditional and learning-based navigation systems often fail to adequately address uncertainty and complex robot-terrain interactions. This thesis surveys recent advances in off-road navigation frameworks, focusing on self-supervised terrain traversability estimation, uncertainty quantification in deep learning, and the integration of physics-based robot-terrain interaction models. It compares geometric, semantic, and hybrid methods for traversability estimation, highlighting the limitations of each in handling deformable and novel terrains. This work emphasizes the necessity of explicit end-to-end uncertainty quantification to distinguish between aleatoric and epistemic uncertainty-to improve risk-aware planning and decision-making in off-road environments. Building on recent developments, the thesis proposes future work on a unified, self-supervised navigation framework that incorporates evidential deep learning into a differentiable, physics-informed architecture. This approach enables the estimation and propagation of uncertainty in terrain parameters, enhancing the reliability of downstream planning and control. By integrating geometric and semantic cues, self-supervision from proprioceptive and physics-based feedback, and advanced uncertainty modeling, the proposed framework lays a foundation for robust, adaptable off-road robotic navigation in unstructured environments