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Genetic Architecture of Nodule Traits in Guar
No full textGuar (Cyamopsis tetragonoloba L.) is an annual, diploid legume crop cultivated primarily for the galactomannan gum contained in the seed endosperm, which is used widely in foods, pharmaceuticals, and other industrial applications. As a legume, guar forms root nodules to fix atmospheric nitrogen, but nodulation has been noted to be poor in the field. Phenotypic evaluation of 225 diverse guar accessions from the United States Department of Agriculture germplasm collection revealed significant variation among genotypes for plant height, nodule diameter, fresh and dry nodule weight and dry aboveground plant biomass. However, no significant differences were found for fresh aboveground plant biomass. Broad-sense heritability was highest for nodule diameter (92%) indicating potential for genetic gain through selection. Significant positive correlations occurred between nodule number and weight, and between plant biomass and nodule weight, revealing increased plant growth with greater nodule mass. Several promising genotypes such as PI 288747 were identified that can serve as parents in breeding initiatives to improve nodulation capacity. Population structure analysis on 225 accessions from India, Pakistan, and the United States genotyped with 7,000 SNPs revealed three main genetic clusters largely corresponding to geographic origin. In which Q1 included genotypes from all three countries while Q2 and Q3 included genotypes only from India. Genome-wide association studies using 19,007 filtered SNPs identified SNP markers associated with plant height, nodule number and diameter, nodule weight, and plant biomass across multiple models. Several SNPs were shown to be connected to various guar nodulation traits. SNP Chr6_56449898 showed a strong correlation with the weight and number of nodules. Chr2_51078296 and Chr2_51010513 SNPs showed a strong correlation with nodule characteristics as well as other variables including biomass and plant height. Together, these results further scientific understanding of guar genetic diversity, nodulation traits, and potential genomic regions controlling nodulation. This knowledge and germplasm can aid guar breeding programs seeking to improve nitrogen fixation and yields through enhanced nodulation, which would also increase ecological services. This research provides an important foundation and resources to enable marker-assisted breeding and selection for superior nodulation capacity and associated increased productivity in this economically important legume crop
Prototype of a Bi-Directional Digital Twin of an Industry 4.0 Smart Manufacturing Facility
No full textThis thesis presents a pioneering exploration into the development and practical application of a bi-directional digital twin prototype within the context of Industry 4.0 smart manufacturing. Bridging the gap between the physical and digital realms, this research addresses the emerging need for advanced digital replication and interaction mechanisms capable of enhancing operational efficiency and educational processes in modern manufacturing environments. By integrating technologies like Siemens TIA Portal, NetToPLCSIM, and OPC UA Server, with a sophisticated communication framework, the study establishes a seamless, real-time bi-directional communication between a physical model and its digital counterpart. The results demonstrate the system's capability to accurately mirror actions and movements across the physical and digital domains, highlighting its potential to revolutionize manufacturing processes, predictive maintenance, and training methodologies. This work not only contributes to the theoretical understanding of digital twin technologies but also showcases a tangible implementation, paving the way for future research and the broadening of digital twin applications across various sectors of the industrial domain
Assessing Orthodontic Compliance with Tailored vs Generic Reminders
No full textThe purpose of this study is to evaluate the effect of daily automated tailored reminders compared to generic ones on the improvement of oral hygiene compliance.
A blinded, prospective, randomized controlled trial was designed to evaluate the effects of the content of reminders. Subjects were recruited from patients undergoing orthodontic treatment at the Texas A&M University College of Dentistry, Department of Orthodontics and were treated with fixed full appliances in both arches. Subjects were randomly assigned to either a tailored text message group or a generic text message group. There were 68 subjects recruited who were 12 to 17 years of age. Oral hygiene was measured at the beginning of the study and again 8 weeks later.
The generic reminder group had significant improvements in oral hygiene compliance from timepoint 1 to timepoint 2. Decreases from T1 to T2 were 1.20 to 0.59, 1.88 to 1.10, and 3.56 to 2.90 for bleeding index (BI), gingival index (GI), and plaque index (PI), respectively (p<0.001). The tailored reminder group had significant improvements in oral hygiene compliance from timepoint 1 to timepoint 2 as well. Decreases from T1 to T2 were 1.33 to 0.58, 2.02 to 1.12, and 3.75 to 2.81 for bleeding index (BI), gingival index (GI), and plaque index (PI), respectively (p<0.001). All initial values in the tailored group and total decreases for all three periodontal tests were higher than in the generic group but were not statistically significant.
Tailored daily reminders are not more effective at improving oral hygiene compliance than generic daily reminders
Crowding: An Exploration of the Effects of Methods and Sound
No full textVisitation rates continue to rise in the United States��� protected areas like national parks and national forests. This has raised management concerns for both impacts on the environment and visitor experiences. The Satisfaction Model postulates that as use levels and encounters rise in parks and protected areas, there is a threshold where the visitor experience is negatively impacted by the additional visitors. Nevertheless, decades of research indicate this relationship is complicated and multifaceted. As a result, the Satisfaction Model has evolved to include norms, use patterns and research measurement techniques as concepts that impact responses to encountering other individuals. Therefore, the purpose of this dissertation was to explore how people respond to research techniques and environment conditions, specifically the soundscape, when visiting protected areas.
Study one focused on starting point bias; a research bias where participant responses are systematically inflated or deflated due to research techniques. Research on crowding responses has frequently relied on visual methods where participants are shown a series of images with varying numbers of people visiting a protected area. The order in which the images are shown holds the potential to inflate or deflate results because the exposure to one treatment may impact responses to a subsequent treatment. This study���s findings revealed a starting point bias, but only on crowding ratings when moving from low setting density to high setting density.
Furthermore, the soundscape has become a fruitful topic for research on visitor experiences. However, little is understood about how sound impacts crowding norms. Study two explored how anthropogenic sound types impact crowding and acceptability ratings of a setting density. Direct human sounds like voices and children playing were expected to be rated more favorably than mechanical sounds. However, the rank of crowding and acceptability ratings were mixed with direct human sounds generally being rated more harshly than mechanical sounds.
Study three explored how sound loudness impacted crowding and acceptability ratings of the setting. Results indicated that loudness was only rated more harshly at the highest loudness levels. The studies are discussed in terms of their results and their impacts on theory and practice
High-Throughput Oxidation Prediction Framework and Assessment of Refractory High-Entropy Alloys and MAX Phases
No full textAs a mode of the design process, compensating for failure is every bit as important the solution itself. Regardless of our best and most innovative efforts, devices and systems breakdown and wear out. In this work, we will examine two regimes of advanced complex alloys, MAX phases and High-entropy alloys, and make predictions on the outcomes of oxidation decomposition. To meet this goal we devise a High-throughput Finite Temperature Phase-prediction framework for simulating oxidation environments for arbitrary metallic alloys. This end-to-end framework projects ground state alloy information into the finite temperature regime with the machine-learning fitted Bartel Model. Using a least-squares algorithm, we take our studied material and the possible secondary phases to minimize the total energy of the system following semi-grand ensemble constraints at rising temperatures. By incriminating allowable oxygen, we then get a map of rapidly determined material decomposition. As an application of design in the High-entropy alloy (HEA) space, or more specifically Refractory HEA space, we sweep the combinatorial alloys regime for MoWTaTiZr materials for favorable alloy candidates for oxidation resistance. From 10%-%30 variations of constituent element concentrations, we generate 51 unique BCC via Monte Carlo Special Quasirandom Structure (MCSQS) algorithm. Applying the framework and appending an innovative metric, area-under-the-curve2 (AUC2), presented in this work, We analyzed and rank ordered the structures using a pareto front method measuring survivability of the RHEA and it���s secondary phases. Oxidation Experiments were conducted at 1373K on four samples, measuring thickness scales and mass change. Finally, and a Pillings-Bedforth ratio based "lack of monotonicity" metric we utilized to make a final design determination. We predict and experimentally confirm that HEAs composition Mo10T a30T i30W10Zr20 is the most favorable alloy. We then use this same framework as an investigation tool for previously performed Oxidation Wedge experiments for T2AlC. We do confirm a substantial aligning of oxidation decomposition and evolution. Additionally we extend this analysis for 30 211-structured MAX phases whose components are governed by the formula Mn+1AXn, where "M" is an early transition metal, "A" is an A block element, "X" is Carbon or Nitrogen and n=1,2,3. We focus on discussions of Cr2AlC, Ti2AlC, and Ti2SiC. We thus conclude an effective low cost screening process for favorable MAX phases
Contrast-Independent Partially Explicit Time Discretization for Multiscale Problems and Its Application
No full textPartial differential equations are widely used in modeling and simulations. In many applications, there are high contrast changes in media properties. The implicit methods are typically used for temporal discretization and are unconditionally stable such that the time step size can be large during the computation. However, the implicit methods are more complicated to solve, especially for nonlinear problems. In contrast, the explicit methods are relatively easier to compute, while they require a much smaller time step size. This work focus on developing a contrast-independent partially explicit time discretization scheme for multiscale problems, specifically, nonlinear problems and also its application. The proposed method divides the spatial space into two components: contrast dependent (fast) and contrast independent (slow) spaces defined via multiscale space decomposition. Following this decomposition, temporal splitting is proposed that treats fast components implicitly and slow components explicitly. As a result, the scheme contains two equations, one implicit and the other explicit. The space decomposition and temporal splitting are chosen such that it guarantees a stability and formulate a condition for the time stepping. With the appropriate construction of spaces and stability analysis, we find that the required time step in our proposed scheme scales as the coarse mesh size, which creates a significant computational saving.
We first apply this approach on the parabolic diffusion reaction equations. We present numerical results and show that the proposed methods provide accuracy similar to implicit methods and the required time step size is independent of the contrast.
Nonlinear time fractional partial differential equations has wide application in physics and engineering. For the case of time fractional diffusion equations, the constraints on time steps are more severe and we extend our partially explicit methods to help alleviate this problem.
In our scheme, the implicit solution part can still be expensive, especially for nonlinear problems. Therefore, we introduce a modified partial machine learning algorithm to replace the implicit solution part of the original algorithm. Then we compute the explicit part of the solution using our splitting strategy. In addition, we use Proper Orthogonal Decomposition based model reduction to further improve our algorithms.
We extend the proposed scheme to solve multi-physics problems. We propose a partially explicit scheme with physics-based splitting. We take the convection diffusion equation as an example. In this scheme, we do a physics-based splitting, i.e. the convection equation is solved using the exact solution, and then we solve the diffusion equation via the partially explicit scheme
Inducing Potential Mutants in Industrial Hemp (Cannabis sativa L.) via Physical and Chemical Mutagenesis
No full textCannabis sativa is a multi-use crop with applications in food, fiber, construction, and medicinal industries. Cannabis plants with low THC concentrations (hemp) have recently been decriminalized by multiple nations across the globe, but farmers are still on the fence about its legality. To avert the risk and constant regulatory pressures due to potential THC contents, we aim to develop a Cannabis variety with zero cannabinoids - Type V Cannabis, using physical and chemical mutagenesis. We selected EMS as the chemical mutagen and E-beam radiation as the physical mutagen and identified the LD50 doses for three hemp varieties ��� HT, 4X, and HCP. The variations in seed germination percentages among mutagen treatments were found to be highly significant (P < 0.05). The M0 seeds were treated with the LD50 dosage of the mutagen to produce mutant M1 populations. The M1 plants displayed a wide array of mutant phenotypes and were tested for their trichome profiles. Selection of M1 plants was done based on mutant phenotypic traits and trichome production, followed by self-hybridizations to produce M2 seeds. The obtained line- Type V is expected to be 100% compliant with all regulations. This promising line presents a new, versatile, and sustainable crop with applications in food, fiber, and construction industries, addressing the growing needs of the nation
Soil-Structure Interaction: From Bearing Capacity to Tolerable Movement
No full textThe increasing number of construction projects is anticipated to play a significant role in driving economic growth, emphasizing the importance of sustainable and cost-efficient built-environment and infrastructure projects. Substructure works constitute a substantial portion of the overall construction cost, averaging around 30 to 50%. This range of values rises notably with taller structures. Shallow foundations continue to be the most economical option compared to deep foundations. However, design challenges linked to shallow foundations persist, particularly in calculating the bearing capacity of shallow foundations on clays (ULS) and determining tolerable movement in building structures (SLS).
This study aims to provide valuable guidelines for designing economical and safe shallow foundation systems that meet LRFD performance criteria, with potential to influence the development of codes and standards. One specific goal is to determine the critical ultimate bearing capacity of shallow foundations on fine-grained soil. Empirical analyses, comparing results of case histories of shallow foundation load tests compiled in the TAMU-SHAL-CLAY-Load Test database to predicted bearing capacities using established theories, along with the comparison of predicted drained and undrained bearing capacities using the information from the database supplemented with data from Houston, Texas soils, revealed that the long-term/drained bearing capacity is more critical for clays with undrained strength greater than 120 kPa, while short-term/undrained bearing capacity is critical for clays with undrained strength less than 120 kPa.
By utilizing information from the database and conducting reliability analysis, geotechnical resistance factors corresponding to a specific probability of failure were proposed for the Ultimate Limit State design of shallow foundations on clays in the LRFD framework. The average of the proposed geotechnical resistance factors yielded a back-calculated FS consistent with those currently adopted in design practice.
Another goal is to determine the tolerable movement of tall building structures for designing foundations, considering the Serviceability Limit State in the LRFD Framework. Numerical simulations, explicitly accounting for soil-structure interaction, were conducted to determine limiting angular distortions to prevent structural damage to buildings on spread footings foundations and mat foundations. The results of these simulations were used to develop plots of normalized differential settlement and normalized stiffness, enabling the prediction of anticipated differential settlement between column locations or obtaining the allowable settlement for buildings on spread footings foundations and mat foundations. Reliability and the probability of exceeding limiting angular distortions were assessed using the Response Surface Method to estimate the implicit performance function, and the surrogate function was analyzed with the First Order Reliability Method. Fragility curves, providing an estimate of the probability of exceedance for a specific limiting angular distortion, were developed using the results of reliability analyses. Finally, a simple method to hand-calculate the maximum settlement of a cluster of spread footings foundation was developed by comparing the deformation behavior of a cluster of spread footings foundation and an equivalent mat foundation through numerical simulations
High-Throughput Oxidation Prediction Framework and Assessment of Refractory High-Entropy Alloys and MAX Phases
No full textAs a mode of the design process, compensating for failure is every bit as important the solution itself. Regardless of our best and most innovative efforts, devices and systems breakdown and wear out. In this work, we will examine two regimes of advanced complex alloys, MAX phases and High-entropy alloys, and make predictions on the outcomes of oxidation decomposition. To meet this goal we devise a High-throughput Finite Temperature Phase-prediction framework for simulating oxidation environments for arbitrary metallic alloys. This end-to-end framework projects ground state alloy information into the finite temperature regime with the machine-learning fitted Bartel Model. Using a least-squares algorithm, we take our studied material and the possible secondary phases to minimize the total energy of the system following semi-grand ensemble constraints at rising temperatures. By incriminating allowable oxygen, we then get a map of rapidly determined material decomposition. As an application of design in the High-entropy alloy (HEA) space, or more specifically Refractory HEA space, we sweep the combinatorial alloys regime for MoWTaTiZr materials for favorable alloy candidates for oxidation resistance. From 10%-%30 variations of constituent element concentrations, we generate 51 unique BCC via Monte Carlo Special Quasirandom Structure (MCSQS) algorithm. Applying the framework and appending an innovative metric, area-under-the-curve2 (AUC2), presented in this work, We analyzed and rank ordered the structures using a pareto front method measuring survivability of the RHEA and it���s secondary phases. Oxidation Experiments were conducted at 1373K on four samples, measuring thickness scales and mass change. Finally, and a Pillings-Bedforth ratio based "lack of monotonicity" metric we utilized to make a final design determination. We predict and experimentally confirm that HEAs composition Mo10T a30T i30W10Zr20 is the most favorable alloy. We then use this same framework as an investigation tool for previously performed Oxidation Wedge experiments for T2AlC. We do confirm a substantial aligning of oxidation decomposition and evolution. Additionally we extend this analysis for 30 211-structured MAX phases whose components are governed by the formula Mn+1AXn, where "M" is an early transition metal, "A" is an A block element, "X" is Carbon or Nitrogen and n=1,2,3. We focus on discussions of Cr2AlC, Ti2AlC, and Ti2SiC. We thus conclude an effective low cost screening process for favorable MAX phases
Comparing Single-Level Regression Based Methods for Analyzing Nested Data When the Extent and Location of Clustering Are Systematically Varied
No full textCollecting and analyzing clustered data is inevitable in educational research. Often, applied researchers will use traditional multilevel modeling to account for the non-independence of observations that result from clustering. However, multilevel models are more complex and have additional data requirements or assumptions relative to a single-level model that applies a correction to the standard errors. Using Monte Carlo Simulations, this work examines the efficacy of various single-level regression-based alternatives for analyzing clustered data when the data-generating model assumes predictors at the lowest level of clustering have both within and between-level variance as well as the effect of different centering choices ��� aspects missing from many contemporary works. The results of this study indicate that correcting standard errors using Taylor Series Linearization or the CR2 correction are the most flexible single-level regression-based methods for analyzing clustered data, as long as within-level predictors are group-mean centered, and the group mean for each cluster is re-introduced. Under these conditions, unbiased standard errors for within and between level estimates are recovered, even with few clusters. However, if within-level predictors are not centered, these corrections lead to increasingly negatively biased standard errors for the within-level estimate as the extent of clustering in the predictors increases, especially when the number of clusters is small. Next, fixed-effects modeling always recovered unbiased standard errors as long as slopes could be assumed to be non-randomly varying; even slight violations of this assumption lead to negatively biased standard errors for within-level estimates. Finally, examining nested data without correcting the standard errors (regular ordinary least squares regression) or using the DEFT correction is not recommended. These methods only lead to unbiased standard errors for within or between-level estimates under conditions that are not likely to be satisfied in real data analysis