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Addressing the Escalating Impact of Rice Kernel Smut: Insights into Genetic Diversity, Fungicide Resistance, Seed Endophytes, and Cultivar Resistance for Effective Management
No full textOnce considered a minor disease, rice kernel smut, caused by Tilletia horrida, has become one of the most economically important rice diseases in the US. With a lack of resistant rice cultivars, management heavily relies on the midseason preventive applications of propiconazole-based fungicides. However, the effectiveness of fungicide application seldom effective due to gaps in knowledge of the disease���s biology and epidemiology. We address, for the first time, the genetic diversity, fungicide resistance, seed endophytic microbiome, and host resistance, with the target to develop an effective kernel smut management program. We performed multi-locus sequence analysis to explore the genetic diversity of 63 T. horrida isolates collected from across the US. These isolates were grouped into five clades, with 59% of the isolates clustering together. The ITS region phylogeny grouped the 22 Tilletia spp. from eight different countries (Australia, China, India, Korea, Pakistan, Taiwan, The US, and Vietnam) together. Of the 63 US T. horrida isolates tested here, >80% were found to be tolerant at the baseline propiconazole concentration of 0.2 mg/L. While over 60% of the isolates displayed no inhibition at a concentration of 10 mg/L, 22% of the isolates tolerated at 25 mg/L, and one single isolate was not inhibited even at 50 mg/L. The T. horrida genomes sequenced, assembled, and annotated as part of this thesis revealed the presence of a single copy of Cyp51, a target gene for demethylation inhibitors (DMIs). In the cyp51 protein sequences of propiconazole-resistant isolates, five amino acid substitutions, G22A, R183K, V279A, L387I, and G494S, were observed. An efficient artificial inoculation method was established for T. horrida infection. Direct injection with pathogen spores onto developing panicles at the late-boot stage resulted in a higher level of disease infection compared to other inoculation methods evaluated. Field evaluation of 32 rice cultivars identified nine cultivars with partial resistance to kernel smut. An amplicon-based analyses of seed endophytic microbial diversity revealed differences between organic and conventional rice farming systems, with higher bacterial diversity in the latter and increased fungal diversity in the former. In parallel the cultivable endophytic bacteria isolated from rice seeds were identified based on the full-length 16S rRNA gene sequences. Among the 31 unique bacterial isolates tested in vitro, the strains Bacillus sp. ST24, Burkholderia sp. OR5, Pantoea sp. ST25, and Pseudomonas sp. OR4 exhibited antagonistic activities against T. horrida and, three seedling blight pathogens (Marasmius graminum, Rhizoctonia solani AG4, and R. solani AG11). The findings of this study provide insights into genetic diversity, fungicide resistance, and seed endophytes, and cultivar resistance for effective management of kernel smut in rice
Motion Control Analysis of Hydrofoil-Based Autonomous Surface Vehicle: An Integrated Approach Utilizing Moving-Mass-Actuated Stabilizer and Variable RPM Propeller Modeled with Computational Fluid Dynamics and Auto-Control Algorithm
No full textHydrofoil-based Surface Vehicles (HSVs) have garnered significant attention for their potential to achieve high speeds, low hydrodynamic resistance, and reduced energy consumption. This efficiency is primarily due to the vehicle���s hull being elevated above the waterline, leaving only the hydrofoils and propeller submerged to generate the necessary lift force to counterbalance the vehicle���s weight at operational speeds. This study aims to extend these advantages by developing an autonomous control system, thereby enhancing the operational capabilities of these vehicles.
This dissertation dedicates to overcoming the inherent stability challenges in the in-house developed hydrofoil-based Autonomous Surface Vehicle (HASV). This battery-powered HASV leverages hydrofoil to lift its superstructure above the water, significantly decreasing drag and improving efficiency at cruising speeds. However, the design, which incorporates a single mast connecting the superstructure to the substructure, introduces notable stability issues. These challenges primarily arise from nonlinear flow loading on the hydrofoil substructure and external environmental factors such as ocean waves and currents. This complexity necessitates the development of an effective control system.
In response to these challenges, the study introduces an innovative control system utilizing the Proportional-Integral-Derivative (PID) algorithm to regulate the HASV���s pitch, roll, and heave stability. It includes a novel moving-mass-actuated (MMA) stabilizer in conjunction with an adjustable revolutions-per-minute (rpm) propeller. The MMA stabilizer enables dynamic adjustment of the HASV���s center of gravity, enhancing control over pitch and roll movements. Simultaneously, the propeller���s rpm is continuously modulated to manage thrust, thereby adjusting the lift force generated by the HASV substructure, which is crucial for controlling heave motion stability.
Previous research on HASV stability control primarily relied on either physical model testing or mathematical modeling. While physical model testing is comprehensive, it is often prohibitively expensive and time-consuming. In contrast, mathematical models, though efficient, require significant simplifications, frequently failing to fully capture complex physical processes, especially those with strong free surface effects. Given the HASV���s limited stability and pronounced free surface effects, there is an urgent need for a more effective and practical approach to investigate and optimize the PID control system. Addressing this need, the study proposes a more accurate Unsteady Reynolds-Averaged Navier-Stokes (URANS) CFD-based control investigation approach. This approach integrates a PID controller into the URANS CFD model, combining the detailed analysis capabilities of CFD with the precision of PID control. This integration ensures that the performance of the proposed control system can be precisely investigated and optimized under different diverse operational scenarios.
This study first starts with the CFD-based hydrodynamic performance analysis of a 2D dual hydrofoils with different configuration and generated a dataset, the dataset is then used to train an Artificial Neural Network (ANN) in order to use the ANN to interpolate within the interesting range and generate a finer resolution results. This analysis helps to have a basic understanding of how the wing and tail can interact with each other and laying the ground for the design of the submerged portion (substructure) of the HASV. Later, this study continues with a scaled-down experimental setup and procedure designed to test the hydrodynamic characteristics of the HASV���s substructure. This experimental study resulted in an understanding of the drag and lift behavior of the substructure, which is be used in the validation of the 3D CFD model. Then, this study continues to use the validated 3D CFD model as a tool to apply the CFD-based control investigation approach, mentioned above, to optimize the performance of the PID controller for regulating the HASV���s roll, pitch and heave motion. Then the effectiveness of the optimized control system on these three DOFs is tested using the CFD-based control investigation approach under different loading scenarios (calm water and waves)
How Likely Is a Repeat of the February 2021 Winter Storm Event in Texas, Really? An Impact-Driven Analysis of Compound Cold-Weather Variables in the Context of the Changing Climate
No full textThe extreme cold that led to the failure of the Texas electrical grid in February 2021 has been extensively analyzed in terms of both its likelihood and its predictability. Other authors have pointed out that the severity of its impact ��� power lost to 4.5 million households representing 10 million people for roughly 3 full days ��� is disproportionate to the severity of the cold. Because the return periods of individual extreme weather variables did not correlate with the extreme impacts experienced, we analyzed combinations of cold-weather variables known to affect residential heating demand, electrical generation, and fuel supply. We evaluated these combinations using cold-weather indices which include multiple variables and copulas which combine individual variables.
We weighted temperatures by the population of the metropolitan area in which they occurred to reflect their impact on the electrical grid and adjusted these temperatures to reflect the changing climate of Texas. We found that the conditions experienced at the time of failure, February 15th, 2021, at 2am Central Time, could reasonably be expected between every 3 and 18 years, with the most extreme combination being low temperatures and time spent at or below freezing. After scaling past temperatures to reflect a warming climate, this combination has been more extreme in Texas four or more times over the past 80 years than what was experienced when the electrical grid was not able to produce enough electricity to meet demand for it.
Cold-weather conditions were additionally analyzed over the locations where these generation resources and their fuel supply had the highest failure rates. We found significant differences in the likelihood of the February 2021 conditions both in individual variables and combinations.
Wind power was reduced by turbine icing and was unable to restart for over a week. Using freezing duration following icing conditions for most stations in a cluster resulted in return periods which seemed to align with the impacts of this storm, yet still resulted in the turbine icing component of this interconnected failure being the least likely aspect to occur again
Shock-Driven Multiphase Mixing Physics in High-Speed Flows
No full textThe Shock-Driven Multiphase Instability (SDMI) occurs when a multiphase (particle-gas) medium is instantaneously accelerated by the passage of a shockwave. It has applications in detonation-driven propulsion engines, explosive dispersal of particles, hydrometeor impacts in hypersonic flight, and astrophysics events. The SDMI involves several phenomena that occur concurrently across overlapping length and time scales, from the mesoscales (cloud-scale) to the microscale (particle-scale). At the larger scales, the problem involves turbulent mixing due to acceleration across pressure and density gradients, like the classic Richtmyer-Meshkov Instability; however, including effects of larger particle or droplet sizes results in longer equilibration times and decreased mixing. At the microscale, in the case of liquid droplets, particle-scale mixing occurs due to droplet breakup and evaporation at a high Weber number. The concurrent phenomena under these conditions are complex and poorly understood, warranting research in numerous physical systems.
Considering this, I will present the findings derived from recent experiments that quantify the multiphysics aspects of the SDMI. I will dive into the impact of the particle velocity relaxation time on hydrodynamic evolution to enhance the accuracy mixing predictions from circulation deposition models. Additionally, I will explore particle-scale mixing, encompassing droplet breakup and vaporization, in quasi-1D experiments to gain a deeper understanding of their influence on hydrodynamic mixing. Moreover, I will explore the effects of high particle evaporation rates on multiphase hydrodynamic mixing. Ultimately, this work intends to develop models that accurately predict cloud-mixing time and length scales, as well as the dynamics of particle-scale mixing
An Economic Analysis of Dynamic and Causal Impacts on the Red Meat Market in the United States
No full textThe red meat market has recently undergone several significant changes in response to changing market conditions, supply chain shocks, and policy impacts. The overall objective of this study is to contribute a better understanding of how segments of the red meat industry respond to shocks. Specifically, this study (1) uses a vector error correction model and directed acyclic graphs to analyze the dynamic interactions and causal effects between cold storage stocks, prices, imports, and exports in the red meat market (2) analyzes the impacts of California���s Proposition 12 animal welfare law on retail and wholesale prices using a difference-in-differences framework and (3) analyzes the dynamic interactions and causal inference patterns of U.S. pork exports to Mexico to further investigate the causes of changing pork export patterns and the impact of the price spread between bone-in and boneless hams on pork exports (volume) to Mexico.
The first essay finds that imports and exports are primary drivers of changes in pork and beef cold storage stocks. The second essay finds that California���s Proposition 12 and Massachusetts��� Question 3 have resulted in price increases ranging from 6% to 21% throughout the supply chain. Finally, the last essay finds that a shock in price spread would cause a long-term impact on bone-in ham exports to Mexico, that a shock in price spread would only account for a small amount of forecast error variation in bone-in exports, and that there is not a direct contemporaneous causal relationship between the two. Ultimately, this study provides valuable information to producers, policy-makers, consumers, and other stakeholders regarding how segments of the red meat industry responds to changing market conditions, supply chain shocks, and policy impacts
Remote and Proximal Imaging Methods for Cotton Nitrogen Status Estimation
No full textNitrogen (N) is an essential plant nutrient and also a major environmental pollutant. Plants require N for amino acid synthesis, chlorophyll health, canopy growth, and yield. In cotton, both N deficiency and excessive N applications affect plant growth and yield. Studies showed that the growth period between squaring and peak flowering corresponded to peak N uptake in cotton plants. Here we explored remote and proximal imaging methods to quantitatively estimate N status in cotton.
A multi-year N management field experiment was conducted to observe cotton development between vegetative growth and early flowering stages. Extraction of precise spatiotemporal features and robust modeling were the topics emphasized in this dissertation. Objective 1 determined the effect of exposure settings on image radiometric accuracy. The results favored the use of fixed exposure settings for UAV flights and the ideal exposure time and gain were empirically determined for the camera. The object-based empirical line calibration method was proposed for images acquired with fixed exposure settings. Objective 2 explored the systematic integration of the downwelling light sensor (DLS) to compensate for changing illumination conditions. The proposed DLS-based methods effectively removed radiometric errors due to illumination changes in fixed and auto-exposure images.
Objective 3 assimilated results from Objectives 1 and 2 to extract calibrated spectral and morphological cotton canopy features to quantify canopy N and predict stress levels. Plant biological parameters ��� plant N concentration, plant N uptake, dry biomass weight ��� were best estimated when spectral and morphological features were combined through random forest regression and gradient boosting regression models. Model estimated parameters were used to derive nitrogen nutrition index to predict stress levels with good precision and recall (F1 = 0.75). Objective 4 explored extracting spectral and morphological features from oblique ground-based images. The goal was to see if cotton had differences in spectral vegetation indices in the top and bottom canopy layers due to N stress. An algorithm was developed to correct perspective distortion in oblique images. The results showed scope for further exploration of oblique images for early detection of N stress
Characterization of a Hypersonic Turbulent Boundary Layer Using the VENOM Technique
No full textModeling fluctuating flow parameters of high speed laminar and turbulent boundary layers is essential for understanding the thermal loading experienced by vehicles re-entering the atmosphere. Hypersonic turbulent flows in particular are challenging to model due to the strong coupling of velocity and energy which drive heat flux. Experimental data of the relationship between these fluctuating quantities are limited due to the required simultaneous measurements of velocity, temperature, and density. Laser diagnostics have become key to measuring these fundamental parameters in unsteady hypersonic flows due to their non-intrusive nature and ability to provide fluctuating measurements, which can be used to assess current predictive turbulence models.
Mean and instantaneous velocity and temperature measurements using the ���Invisible Ink��� Vibrationally Excited Nitric Oxide Monitoring (VENOM) laser diagnostic are presented to characterize the hypersonic boundary layer above a 2.75 degree half-angle wedge test article. The measurements were performed in the Actively Controlled Expansion (ACE) blow-down wind tunnel under both laminar and turbulent conditions. A double dependent Gaussian fitting velocimetry algorithm was developed to account for laser reflections at the wall, and intensity fluctuations due to turbulent motion. Additionally, rotational temperature profiles were using a thermometry algorithm in conjunction with velocity information extracted from each image. The results demonstrated a uniform laminar flow across the flat plate which broke down to turbulence in response to inserted mechanical trips. Freestream fluctuations compare favorably to previous measurements. The laminar boundary layer fluctuations peak around 12% for the velocimetry results and 20% for the thermometry results. The turbulence fluctuations peaked around 18% and 24% for the velocimetry and thermometry results respectively. Using the laminar results as a baseline for the technique uncertainty, it is estimated the true turbulence fluctuations are on the order of 10���15%. The mean and instantaneous measurements qualitatively agree with previous separate velocity and temperature measurements and simulations. The generated database of instantaneous parameters can be used to directly determine the variable heat flux of the flow over the wedge surface using a constant specific heat model to assess current predictive turbulence models. The current measurements are analysis can also serve as a basis for future multi-component velocity and temperature boundary layer measurements, contributing to a comprehensive understanding of turbulence, a complex 3D phenomenon
Improved Whitecap Quantification and Prediction Using Shipboard Remote Sensing and Machine Learning
No full textWhitecaps generated by wave breaking and air entrainment can be classified as active (stage A) or residual (stage B). Discrimination and measurement of each stage individually are essential for accurate parameterization of air-sea interaction processes, but conventional methods used for separation in visible images are subjective. This study provides a novel method to identify whitecap stages based on visible imagery using particle image velocimetry (PIV). A linear relationship was established between the lifetime of stage A and the timescale of averaged velocity. This novel method characterizes stage A whitecap lifetime using whitecap velocity and provides an objective approach to separate whitecap stages.
To estimate active whitecap fraction, we introduced a pipeline for active whitecap fraction measurement. In this pipeline, a new horizon detection method is developed to stabilize and rectify images and a deep learning model based on U-Net is trained and validated to identify and extract active whitecaps. The model is applied to 48 hours of video footage collected during a cruise in Gulf of Mexico. It is determined that, as a function of wind speed, active whitecap fraction has significant variability and disparity compared to previous research. This finding indicates that secondary factors should be considered for accurate whitecap parameterization. This is explored using principal component analyses and random forest, which indicate sea surface temperature, swell and wave age are important to active whitecap fraction. The precise impact of sea surface temperature is further explored using analyses of variance (ANOVA), which suggest it has a positive correlation with active whitecap fraction.
The decaying stage B foam with significant variability has been found to contribute 1.5 to 40 times more to total whitecap fraction than stage A foam. In this study, we present a novel model that describes the relationship between whitecap fraction and the evolution of whitecap area, providing a method to quantify the whitecap lifetime scale. The same data from a Gulf of Mexico cruise is processed using this method. The stage B lifetime scale shows weak positive correlation with active whitecap fraction and no correlation with sea surface temperature and wind speed
Non-Destructive Viability Testing of Cotton Seeds Using Raman Spectroscopy and Optical Coherence Tomography
No full textIn recent years, the field of plant breeding has been quick to adopt new technologies for the analysis of field and crop systems, with optimistic results and massive quantities of data generated. In this thesis two such optical technologies, Raman spectroscopy and Optical Coherence Tomography (OCT), are used to produce quantifiable characteristics of cotton seed that may be used for analysis of viability and determination of the features that correlate to survivability. It was found that spatial variability of the Raman spectrum, as well as the occurrence or absence of specific delineations in the OCT tomograms correlate with seed viability.
Raman spectroscopy has proven to be an invaluable tool in the nondestructive analysis of biological materials. As it was previously found that fatty acid and carbohydrate content could be linked to seed viability, Raman signatures of these compounds are of particular interest. The ability of OCT to provide three-dimensional morphological structural details non-invasively, suggests its potential use for the analysis of internal seed structures. The features of interest include damage to the seed coat as well as clear demarcations in the seed interior, or subsurface features that could impact the germination of the seed. A comparison between the features documented by both technologies on each seed paired with a germination test allows correlations about the impacts that each feature and their combination have on the survivability of seeds.
We predict seed viability using the correlation of the observations we make in the Raman and OCT measurements with seed viability. With both cotton varieties (Tamcot 73 and G11) we find that when we select only seeds predicted to be viable, the germination rate is higher than for randomly selected control seeds. While the Tamcot 73 seeds we predicted to be viable germinated slightly better than the control seeds of the same variety, the overall low germination rate of these seeds indicates that other factors not measured by our techniques can have a large remaining impact. For the most recently harvested Tamcot G11 seeds, the germination rate of the control seeds was about 85%, leaving not much room for improvement, and thus a lower level of significance. The level of significance was highest for G11 seeds harvested in 2019, indicating that combined OCT and Raman measurements of seeds can be used to predict seed viability and to improve agronomic outcomes by selecting seeds with a higher expected chance to be germinating
A Study of Texture Characterization of Fiducial Markers for Visual Navigation
No full textA full six degree-of-freedom state estimation is an important problem in robotics, augmented reality, and autonomous navigation. Obtaining such information using visual features has been challenging since such a task always needs information-rich images. Getting access to a visually rich environment is not often possible. In such scenarios, artificial visual references like fiducial markers are used. This thesis conducts a systematic study of the texture of such fiducial markers. The study provides insight into the fiducial markers��� visual characteristics and design patterns. A general implementation of the ArUco marker detection and estimation system is created to understand the fiducial marker design process fully. The thesis reports results and lessons from both the set of tasks. Based on the study of other markers, a new fiducial marker-based on the Spidron pattern is proposed. A detection and pose estimation system is developed for this Spidron based marker. The system is tested in an experimental setup simulating refraction, motion blur due to rotation and jitters, and transformation due to scaling