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Improving Maize (Zea mays) Production: Assessing Impacts of Seed Cleaning, Treatment, and Novel White Sub-Tropical Inbred Varieties
No full textMaize is an important crop in the U.S. and globally and grain yield continues to increase from both agronomic and genetic technologies. Farmer���s successful crop establishment of maize has relied on treated and cleaned seed to reach plant population targets of healthy plants. The purpose of the first study was to determine the effects that cleaning and treating seed had on both the plant population and yield of a crop in small plots for a breeding program. Ten diverse experimental maize cultivars were chosen based on seed availability and subject to different combinations of cleaning and treating. The cultivars were tested across two years (2021 and 2022) and multiple environments. A reengineered Mater Continuous Seed Blower was used to clean the seed. Cruiser 5FS and Maxim 4FS were blended following recommendations and used as a seed treatment. Overall, the results of this experiment showed a positive correlation between cleaning and treating seeds and an increase in yield and plant population. The results help to reinforce the use of cleaning and treating techniques in seed preparation for small plot research.
The second study involved preparing a release of novel white inbred lines for white maize hybrids. The varieties proposed for release include Tx131, Tx133, Tx134, Tx148, Tx149, Tx150, Tx160 and Tx161. All inbreds listed are derived from white sub-tropical germplasm and can improve yield and genetic diversity white maize in the U.S. Current white maize varieties are limited, and this release will increase the genetic diversity providing farmers with more options for planting. These lines were crossed with a variety of commercial and within Texas A&M program testers, and resulting hybrids were grown at multiple locations over several years. Hybrids from each line produced yields that met or exceeded those of current commercial hybrids
Feasibility of Mixed-Integer Linear Programming for Onboard Planning in Distributed Earth Observation Satellite Systems
No full textPlanning is traditionally completed on the ground, which limits swift response to events of interest. As a result, there is an expanding need for onboard planning for satellites. This sparks exploration of the feasibility of reactive Earth observation using optimization for onboard, edge processing of plans. Reactive Earth observation lowers response time to events of interest, such as tropical storms or the formation of algal blooms.
This thesis compares a global optimization method, mixed-integer linear programming (MILP), with two less computationally expensive local optimization methods: greedy and forward search algorithms. This planning problem models a satellite constellation operating within an inland water quality remote sensing mission over a 24-hour simulation period. The water quality mission poses a challenge due to the small swath of relevant onboard instruments used to observe the large quantity of water bodies.
Two respective cases of 5- and 10-satellite constellations are applied to investigate scalability of the problem. On average, the local optimization methods achieve 64% of MILP performance. Smaller 1- to 2-hour planning horizons generally have little effect on MILP performance. Additionally, the MILP successfully solves problem sets of up to 580,000 variables for the 5-satellite problem and up to 900,000 variables for the 10-satellite problem. The MILP achieves these metrics in planning horizons of up to 8-hours for the 5-satellite problem and in planning horizons up to 4-hours in the 10-satellite problem.
A Raspberry Pi was employed to characterize the feasibility of using MILP for onboard planning. The Raspberry Pi could solve problems that did not exceed its memory capacity and was able to solve at least half as many problems as the PC was able to solve, depending on the size of the problem set.
Performance results build an argument to use small planning horizons to create plans in a reasonable amount of time. Raspberry Pi results suggest that smaller problem sets that do not exceed memory capacity during solving ensure that onboard planning is feasible for MILP. Overall, results suggest that MILP is a great method for satellite planning for relatively small problems
John Bickham field notebook: AK15001-AK15500.pdf
No full textBound book, each page corresponds to a karyotype slide data.Data pages for AK15501-AK16000 corresponding to unique identifiers of specimens/samples examined for biological research. Specimens are primarily housed at Texas A&M University; Biodiverstiy Research and Teaching Collection
John Bickham field notebook: AK24501-AK25000.pdf
No full textBound book, each page corresponds to a karyotype slide data.Data pages for AK25001-AK25500 corresponding to unique identifiers of specimens/samples examined for biological research. Specimens are primarily housed at Texas A&M University; Biodiverstiy Research and Teaching Collection
Optimizing Produced Water Treatment in the Permian Basin: The Role of Indirect Evaporative Cooling in Hydraulic Fracturing
No full textThis study investigates the novel application of Indirect Evaporative Cooling (IEC) for treating produced water in hydraulic fracturing operations within the Permian Basin. Utilizing a lab-scale IEC system, we conducted fifteen experiments to assess Total Dissolved Solids (TDS) and volume reduction in produced water, considering variations in initial water volume, composition, and TDS concentration. The experiments included a range of synthetic samples with salinities from 0 to 70,000 ppm and real-produced water from different Permian Basin regions (Delaware, North Midland, and South Midland).
Our findings reveal that IEC's efficiency in TDS removal, achieving near 100% effectiveness for both synthetic and real produced water samples, is primarily influenced by the presence of the most abundant salts rather than the overall TDS concentration. This highlights the system's capability to handle high salinity and diverse impurities typical in oil and gas production waters. Additionally, the IEC system proved to be significantly less energy-intensive compared to traditional thermal evaporation methods.
The economic assessment of IEC versus standard evaporation methods for treating one barrel of water further demonstrates its viability. The study concludes that IEC offers a highly effective, environmentally sustainable, and economically feasible solution for high TDS oil field water treatment. It stands out as a promising alternative to conventional technologies, with potential applications extending across various industrial facilities. This research paves the way for future exploration to maximize the potential of IEC in addressing the wastewater challenges in hydraulic fracturing
Image-Based PV Soiling Quantification and Defect Detection Using Machine Learning
No full textSolar energy, a rapidly growing renewable energy source, has garnered significant global attention in recent years. Achieving high efficiency and maintaining the optimal performance of PV panels is crucial. In addition to the material properties and design of the solar cells, PV efficiency is also significantly affected by system losses and degradation.
Soiling loss, an important system loss, cannot be improved solely through design modifications and requires periodic inspection and cleaning. In this thesis study, a novel image-based method for estimating soiling loss has been proposed, utilizing key feature extraction and linear regression techniques. Two datasets were collected for this purpose: an in-lab simulation dataset and a dataset obtained from an outdoor PV testing field. The proposed method was tested with both datasets using measured soiling loss/power loss as a gold standard. The method achieved an r-squared value of 0.98 and the root mean squared error of 0.01, which showed its significant potential for cost-effective soiling monitoring purposes.
In addition to soiling loss, this study also addresses the problem of PV cell defects, which can come from degradation. A computer vision-based method is developed for detecting PV defects. The method utilized the State-of-the-Art (SOTA) object detection algorithm You Look Only Once V8 (YOLOV8), with U-net architecture and feature pyramid network to improve the accuracy. In addition, the model is compressed with Layer-Adaptive Magnitude-based Pruning to improve the computational efficiency, Additional improvement including the adoption of a better loss function inner-CIoU and the activation function MiSH. To test the proposed method, an open-source Electroluminescent PV defect dataset PVEL-AD was used. The method is compared with several existing algorithms in terms of accuracy and efficiency. The proposed method outperformed all reported work in accuracy and ranked No.2 only in efficiency. It reached mean Average Precision under IoU of 50% (mAP50) of 93.1%, and mean Average Precision under IoU from 50% to 95% (mAP50:95) of 68.7%, which improved about 8-15% comparing to the best existing algorithm. The model���s detection speed is 85.3 Frame per Second (FPS) which ranked in 2nd place among all the existing works. In addition, the model is trained on multiclass detection, the fastest method with FPS of 94.34 only trained on selected classes.
In summary, the novel methods developed in this thesis provide effective tools for estimating soiling loss and detecting defects in PV panels. With improved efficiency and accuracy, these developments have the potential to significantly improve the overall efficiency and maintenance of solar energy systems
International Trade Impact on Milk Product Market in the U.S. and LCA with Economic Analysis on Converting Lignin Waste to Sustainable Products
No full textThis dissertation investigates some economic issues regarding international trade and the production of byproducts in lignocellulosic ethanol production. This work is reported in three essays.
The first essay addresses how recent expansions in international trade affect Class I spatial milk price differentials in the U.S. Five international trading scenarios were simulated using a milk sector movement and processing model. The results reveal notable regional differences in the spatial characteristics of farm-level raw milk prices. We find the greatest effects are in the Eastern U.S. where we see significantly higher prices. We also see domestic milk price disparities, in areas like Idaho and New Mexico. Furthermore, the influence of international trade is clearly seen in prices of U.S. milk products that are exported or imported, especially for the products like Cheddar Cheese and Butter. The study emphasizes the need for a possible update in spatial milk price differentials considering the growing impacts of a globally interconnected market.
The second essay examines the net greenhouse gas emissions and market penetration implications of using the lignin byproduct from a lignocellulosic biorefinery to make carbon fiber. The results show that using lignin as a precursor for producing carbon fiber leads to a reduction in CO2 emissions compared to the conventional process of producing carbon fiber. We also analyzed the price and size of the market if the lignin-based carbon fiber was entered into existing carbon fiber markets under various elasticities. Here we found large scale production would lead to substantial carbon fiber price declines.
The third essay examines alternative choices for lignin utilization across a set of alternative downstream products. Namely, lignin can be used to make carbon fiber, asphalt binder modifier, PHA, and biodiesel lipids, when this is done our analysis finds a number of substantial economic and environmental benefits, most notably in reducing CO2 emissions. Market analysis under various scenarios, including the consideration of carbon emission prices, indicates that the optimal market entry of these products depends on production costs and carbon pricing. The findings suggest a strategic approach to lignin utilization, where prioritizing lignin-based carbon fiber production and adjusting outputs based on carbon emission costs can lead to maximum profitability while contributing positively to environmental sustainability
Geometric Deep Learning for Science: Prediction, Generation, and Symmetries
No full textDeep learning has significant potentials in accelerating the progress of science research. However, the data in most science problems are geometric data, or graph data, which brings many unique challenges. First, designing label-invariant data augmentations for geometric data is challenging. Second, regular deep generative models need to be dramatically modified to suit for 2D molecular graphs, 3D molecular geometries, and periodic materials. In this dissertation, we study these challenges and propose several novel methods to tackle them.
We first propose GraphAug, a novel automated data augmentation method aiming at computing label-invariant augmentations for graph classification. GraphAug uses an automated augmentation model to avoid compromising critical label-related information of the graph, thereby producing label-invariant augmentations at most times. To ensure label-invariance, we develop a training method based on reinforcement learning to maximize an estimated label-invariance probability. Second, we propose GraphDF, a novel discrete latent variable model for 2D molecular graph generation based on normalizing flow methods. GraphDF uses invertible modulo shift transforms to map discrete latent variables to graph nodes and edges. We show that the use of discrete latent variables reduces computational costs and eliminates the negative effect of dequantization.
Third, we propose G-SphereNet, a novel autoregressive flow model for generating 3D molecular geometries. G-SphereNet employs a flexible sequential generation scheme by placing atoms in 3D space step-by-step. We propose to determine 3D positions of atoms by generating distances, angles and torsion angles, thereby ensuring both invariance and equivariance. In addition, we propose to use spherical message passing and attention mechanism for conditional information extraction.
Finally, we propose SyMat, a novel symmetry-aware periodic material generation method. SyMat generates atom types and lattices with a variational auto-encoder model. In addition, SyMat employs a score-based diffusion model to generate atom coordinates based on a novel coordinate diffusion process. We show that SyMat is theoretically invariant to all symmetry transformations of materials.
We demonstrate the effectiveness of our proposed methods with comprehensive benchmark experiments. In the future, we will explore developing novel predictive models for the prediction of Hamiltonian matrices and accelerating the generation of SyMat by stochastic differential equation based diffusion models
Exploring the Career Aspirations of China���s Generation Z (Gen Z): A Basic Qualitative Study
No full textThe topic of career aspirations has attracted increasing attention from scholars and practitioners given its crucial role as one of the most useful predictors of eventual career. While ample studies were conducted to explore the career aspirations of Gen Z living in the West, few were situated in the context of the non-Western world. This critical group outnumbers Gen Z in the West. However, we have limited understanding of attracting, engaging, and retaining the emerging workforce in the non-Western world. For human resource development (HRD) field, whose core mission is developing people, limited research attention has been given to Gen Z in the non-Western world. With jobs changing and the workforce shrinking, talent competition will be fierce, employers need to prepare differently to win the global talent market. Therefore, this study aimed to explore the career aspirations of China's Gen Z members. Specifically, three research questions guided this inquiry: First, what are the career aspirations of China's Gen Z? Second, what influences the career aspirations of China's Gen Z? Third, what do China's Gen Z expect from their prospective employers?
To address these questions, I used a basic qualitative research approach. Informed by this design, I recruited 27 Gen Z participants from a comprehensive university in Eastern China. I conducted in-depth, face-to-face interviews. I analyzed 520 pages of interview data using the thematic analysis (TA) method.
This study revealed three major findings. First, the career aspirations of China's Gen Z participants could be categorized into three types: talent-based, needs-based, and value-based. Second, the shaping factors include contextual factors and personal factors. Lastly, China's Gen Z expects to work with tolerant leaders who are tolerant of mistakes in a positive environment. They favor face-to-face communication and individual tasks.
This study provided significant implications for HRD practice and research. For organizational leaders and HRD professionals, this study proposed actionable strategies that can effectively attract, engage, and retain China's Gen Z. For HRD scholars, this study opened the door to an almost uncharted territory (China's Gen Z) and outlined a new research agenda
Investigating Deformation and Sediment Dispersal During Andean Mountain Building in the Western Cordillera of Southern Peru
No full textThe central Andes are the archetypal modern cordilleran margin. An assessment of the timing, style, and position of deformation and associated sediment dispersal remains incomplete, especially in the Western Cordillera and forearc. Here, much of the region is overlain by Neogene extrusive igneous products from the modern Andean arc that obscure the exposures and evidence of crustal deformation. This study exploits exposures in deeply incised canyons that provide key insights into the deformational and depositional records of the Western Cordillera and forearc in southern Peru. New U-Pb zircon geochronology, structural field mapping, aerial drone 3D modeling, sediment provenance modeling, and fault kinematic forward modeling results are integrated to constrain deformation timing and style, and sediment provenance of the forearc basin. Geochronologic results from two outcrops of thrust faults that verge toward the subduction trench, paired with outcrop interpretations and forward modeling, provide detailed accounts of fault kinematics in the forearc. One fault, termed here the Aplao thrust fault, shows evidence of at least three distinct slip events throughout a long-lived history: initial compressional deformation is constrained here to between early Cretaceous and ending prior to 45.24 Ma; syndepositional deformation between 45.24 Ma and ending between 30 and 26.67 Ma; and a final episode ending before 26.67 Ma. The other structure, termed here the Toran fault, also displays multiple phases of deformation: the structure initiated as a Jurassic normal fault related to pre-Andean extension, followed by normal fault inversion and three identified compressional slip events constrained to (1) between early-middle Jurassic and ending prior to 26.67 Ma, (2) 15.98���14.07 Ma, and (3) post14.07 Ma. Sediment provenance modeling reveals upsection unroofing of the Western Cordillera and recycling of forearc basin fill, with minimal contribution from distal sources in the Altiplano or Eastern Cordillera. Results from this investigation are integrated with published depositional and deformational age constraints to place forearc deformation into context with broader orogenic controls on Andean deformation. Protracted compression in the forearc was coincident with an Eocene���early Miocene episode of flat or shallow slab subduction. This ancient slab shallowing event also drove inboard deformation of the Eastern Cordillera. This new record of shortening and unroofing in the forearc while deformation was also ongoing in the Eastern Cordillera is evidence of widespread and distributed out-of-sequence deformation and consistent with the Andean orogen being in a protracted phase of subcritical taper. The observed thrust fault geometries are consistent with a broadly bivergent Andean cordilleran system and emphasize the role of selective reactivation and inversion of inherited structures on deformation localization. These results contribute towards a complete characterization of Andean deformation, emphasizing the need for additional investigation into the cause of this long-lived compressional deformation in the forearc, even when deformation was focused far inboard from the trench