K-State Electronic Theses, Dissertations, and Reports: 2004 -
Permanent URI for this collectionhttps://hdl.handle.net/2097/4
This is the collection for doctoral dissertations and masters theses and reports submitted electronically by K-State students. Electronic submission of doctoral dissertations was required beginning Fall semester 2006. Electronic submission for masters theses and reports was required beginning Fall 2007. The collection also contains some dissertations, theses, and reports from the years 2004 and 2005 that were submitted during a pilot test project. Some items before 2004 have been digitized and are available in K-State Electronic Theses, Dissertations, and Reports: pre-2004. Check the Library catalog for dissertations, theses, and reports not found in these collections.
All items included in this collection have been approved by the K-State Graduate School. More information can be found on the ETDR Information Page. Items within this collection are protected by U.S. Copyright. Copyright on each item is held by the individual author.
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Item Open Access Maintaining balance in the microverse: investigating microbial impacts on host gut inflammation(2024) Richie, TannerEnteric microbes can impact digestion, maintain the immune system, and have implications for overall health. Microbial mechanisms driving and alleviating gut inflammation have been speculated but challenging to decipher specific microbial mechanism. Using multi-omics from a systematic to individual microbial populations level, we showed microbial pathways that were responsible for persistent gut inflammation, as well as potential microbial products that might alleviate inflammation. Enterobacteriaceae are known to promote gut inflammation and blooms during dysbiosis, with little speculation on how they drive inflammation. Through a holistic host-microbe approach, we showed that Enterobacteriaceae members can utilize L-cysteine for increased growth, fueling the growth needed to drive inflammation. We further isolated and cultured Enterobacteriaceae Klebsiella pneumoniae and revealed increased expression of membrane proteins and attachment proteins. With this knowledge, we synthesized a peptide to limit attachment of K. pneumoniae to the host, thereby limiting a proliferative and inflammatory response and providing a potential therapeutic alternative to antibiotics. Finally, when investigating dysbiotic murine recovery, we observed an increase in Lachnospiraceae. Using metabolomics, we investigated products that may aid in inflammation alleviation from five strains of Lachnospiraceae. One of the strains, Eubacterium rectale, produced several amino acids and antioxidants that is essential for gut repair. We also showed that microbial metabolites significantly lowered inflammation as well as lower expression of host repair and inflammatory genes. Overall, these findings help us to better understand the gut microbial landscape and the contributions of specific microbes and subpopulations in maintaining the balance of the gut microbiota and gut health.Item Embargo Aerobic fermentation to treat and upcycle hydrothermal liquefaction aqueous phase(2024) Liu, MeicenHydrothermal liquefaction (HTL) is a promising wet thermochemical process for converting biomass feedstocks into biofuels. HTL aqueous phase (HTLAP), as one of major by-products of HTL, retains a significant portion of the biomass energy and requires treatment before discharge to the environment. Developing a method to treat HTLAP and recover its energy content is crucial for the economic viability of HTL, while the complexity, variability, microbial toxicity, and recalcitrance of HTLAP make it a challenging task. Biological treatment methods, such as algae cultivation, anaerobic digestion (AD), and aerobic treatment are environmentally friendly and straightforward options for treating HTLAP. Compared to algae cultivation and AD, aerobic treatment can tolerate higher concentrations of HTLAP and generally requires a shorter incubation time. Despite these advantages, aerobic treatment is less studied in literature. Therefore, it is of interest to develop an aerobic fermentation process for HTLAP that can effectively remove pollutants while producing valuable bioproducts. This work addresses the need to advance HTLAP treatment technology by developing an effective aerobic fermentation process while further promoting the sustainability of HTL for biofuel production. It begins with a comprehensive review of HTL technology, HTLAP characteristics, existing treatment technologies, and highlighting the benefits and drawbacks of each technology. The research journey of this dissertation embarks upon a comparative evaluation on the biological degradation of HTLAP with various bacterial and fungal strains. The study emphasized the significance of pH adjustment and mineral supplementation in enhancing COD removal during HTLAP fermentation. Bacterial strains tolerated a 10× dilution of HTLAP, while most fungal strains required a higher dilution of 20×. However, the fungal strains demonstrated effective degradation of a wide range of compounds, particularly phenolic compounds, achieving removal efficiencies between 60-100%. Among them, the filamentous fungi A. niger and T. versicolor were identified as the most promising strains, achieving approximately 50% COD removal in HTLAP fermentation. The research also highlighted the recalcitrance of sludge HTLAP, which was attributed to the presence of N-heterocycles, as these compounds were removed by less than 40% across all microbes tested. Building on these findings, we further evaluated three filamentous fungal strains in the fermentation of corn stover HTLAP. Among the tested fungi, A. niger showed the best biodegradation performance, converting approximately 50% of its consumed organic carbon into oxalic acid as a value-added product. To enhance HTLAP biodegradation, R. jostii was co-cultured with A. niger during fermentation. The oleaginous bacterium R. jostii consumed organic acids that were left behind by A. niger and accumulated cellular lipids. Together, this co-culture achieved consistent COD removal of approximately 70% in diluted HTLAP with COD values between 3.8 and 7.8 g/L. These findings demonstrated the promising fermentation capabilities of A. niger and R. jostii co-culture, highlighting their potential for HTLAP valorization to improve the sustainability of HTL processes. Lastly, we assessed the impact of HTL operating conditions on the chemical composition and biodegradability of the resultant HTLAP in A. niger and R. jostii fermentation. This study elucidated the variation in chemical composition of HTLAP derived from different HTL conditions and established a quantitative relationship between HTL operating parameters (temperature, retention time, and feedstock loading) and the biodegradability of HTLAP, highlighting that HTLAP derived from higher severities exhibited lower biodegradability. This reduced biodegradability was attributed to the increasing concentration of specific phenolic compounds in HTLAP produced at higher severities. Additionally, the research demonstrated the versatility of the A. niger and R. jostii co-culture in fermenting HTLAP derived from a wide range of HTL conditions, achieving COD removal rates of 45-70% depending on the specific HTL parameters used. In conclusion, this dissertation advances HTL technology by developing a promising aerobic fermentation process for HTLAP upcycling. The aerobic fermentation process outlined in this dissertation represents a significant step forward in making HTL technology more economically and environmentally sustainable.Item Open Access Leveraging a natural language processing approach towards a more informed vulnerability documentation process(2024) Anshutz, BreAnnCybersecurity vulnerabilities are an ever-increasing threat to the current cybersecurity landscape. It has been previously suggested that Twitter is a robust data source for gathering Cyber Threat Intelligence data. This includes cyber vulnerabilities which can be retrieved via their Common Vulnerabilities and Exposures (CVE) identifier. However, the culture of post-disclosure vulnerability discussion is changing to sometimes include a ”nickname”, or a short name utilized instead of the CVE identifier. This trend poses a significant challenge to the retrieval of CVE-relevant information as not all text includes the CVE identifier. To address this challenge, a system was designed by utilizing an off-the-shelf machine learning model to link tweets that do not explicitly mention a CVE Identifier to their corresponding CVE. The system was tested utilizing several datasets and metrics to determine parameters required to obtain satisfactory performance with regards to retrieved information. The results show that machine learning makes it possible to retrieve relevant information corresponding to a specific CVE in the absence of the CVE identifier.Item Open Access Enteric methane emissions mitigation in U.S. beef production: Industry adoption, public perception, and potential market impacts(2024) Luke, JaimeChapter 1 Cattle are ruminant livestock that emit enteric methane (CH4) as part of their natural digestive process. The U.S. beef cattle industry is receiving pressure to reduce greenhouse gases emissions, including methane. The U.S. Roundtable for Sustainable Beef has set a target for the U.S. feedlot sector to reduce emissions by 10% per pound of beef by 2030. Feed additive 3-Nitrooxypropanol (3-NOP) has been developed to help mitigate methane emissions. While not yet approved for use in U.S. beef production, adoption of 3-NOP in U.S. feedlots upon approval remains unknown as no widespread economic incentive currently exists in the marketplace to spur adoption. The objectives of this study are: (1) to determine potential 3-NOP adoption by U.S. feedlot cattle producers given various marketplace incentives and (2) to explore how differing approaches to reducing emissions from beef production to achieve sector targets impact social welfare. Our study uses data from a U.S. feedlot producer survey to estimate willingness-to-adopt (WTA) measures. Regression results are used to map potential adoption of 3-NOP given various market and policy scenarios. The survey sample is then split into small producers (<2000 head sold in last 12 months) and large producers (2000+ head sold in last 12 months) to determine differences in WTA based on operation size. We find that producers prefer incentives in the form of processor premiums over government subsidies. The incentive level needed to spur adoption increases as the implementation cost of 3-NOP increases and decreases if net profit estimations are included in the messaging. On average, small producers require a higher incentive to adopt 3-NOP than large producers. Improving the emissions reduction efficacy of 3-NOP reduces the level of incentive needed to achieve aggregate emissions targets. The least expensive avenue to achieve emissions reduction targets results in greater outlays to large producers as compared to small producers. The marginal cost to society of feeding 3-NOP to an additional steer or heifer in the feedlot increases with each animal. As such, it may be that improving the efficacy of 3-NOP through increased investment in research and development is less costly than spurring more producers to adopt the additive in their feed rations. Ultimately, producers, processors, beef consumers, voting residents, taxpayers, and policymakers all have influence in shaping how the beef industry tackles the emissions reduction conundrum. Chapter 2 U.S. beef cattle producers have been receiving pressure to reduce methane emissions from beef production as climate change concerns mount. Demand for “climate-friendly” beef could create economic incentives that spur U.S. cattle producers to adopt emissions reducing practices. Beef products with varying climate claims have recently been introduced in the retail sector, stemming from various countries-of-origin. This study quantifies differences in U.S. consumer willingness-to-pay (WTP) for distinct climate claims on ground beef and ribeye steak products, accounting for country-of-origin (COO) impacts. Using data from a nationally representative survey of the U.S. public, no statistical difference in WTP estimates is found among climate claims within beef product categories. This suggests U.S. consumers are, on average, indifferent among the climate claims on beef products included in this study. U.S. consumers are willing to pay a higher price premium for climate claims on ribeye steak (up to 5.3%) as compared to ground beef (up to 2.8%), and they prefer U.S. beef products as compared to those from Australia, Canada, and Uruguay. Imported beef products with climate claims are unlikely to have major implications on the demand for U.S.-produced beef. Ultimately, beef producers should consider the cost of implementing climate-focused production practices to determine if garnering market premiums for climate claims on beef products would be a profitable endeavor. Chapter 3 Many countries have begun introducing policies aiming to reduce emissions from beef production. Several strategies are being researched and developed to reduce such emissions. This study explores U.S. public sentiment toward various beef production emissions-reduction strategies and quantifies support for potential policy measures. Using data from a nationally representative survey, it is found that feeding seaweed to cattle is the most preferred strategy followed by letting either the beef industry or the U.S. Department of Agriculture decide how to reduce emissions from beef production. The U.S. public shows greater support for subsidies versus mandates if they recognize that mandates could increase the price of beef. The strategy with the highest estimated subsidy support, as funded by the U.S. public, is seaweed. However, these subsidy levels are less than the projected cost of the product in practice, so producer adoption of seaweed as triggered by a U.S. public-supported subsidy is unlikely. A more plausible solution to subsidizing a subset of beef producers to reduce emissions may be connecting climate-concerned residents who are willing to fund a subsidy to producers who are willing to adopt climate-focused practices.Item Embargo Electronic Transport and Low Frequency Noise in Atomically Thin Graphene and Hexagonal Boron Nitride Two-Dimensional Heterostructure Field Effect Transistors(2024) Behera, Aroop KField Effect Transistors (FETs) form the basis for modern integrated circuits (ICs), that are the building blocks of every computing and communication device. The need for faster and energy efficient microprocessors has enabled continuous development of novel device architectures with the state-of-the art micro/nano-fabrication technologies, aiming for a transistor feature size of ≈3 nm and beyond. However, scaling down the device dimension below 3 nm brings added challenges due to various fundamental physical limits, thus demanding a paradigm shift in materials and device fabrication techniques. Two-dimensional (2D) atomically thin materials along with their heterostructures have shown exceptional electrical, optical, mechanical, thermal, and chemical properties that provide avenues to innovate newer and smaller devices with promise for delivering higher performance and energy efficiency. However, fundamental understanding of carrier transport in terms of their microscopic origin and their scattering mechanisms are necessary to elucidate the device physics of such 2D transistors. This dissertation work delves into the design and fabrication of 2D heterostructure graphene field effect transistor (2D-HGFET) devices, understanding their electrical transport properties by investigating various carrier scattering mechanisms, and characterize the fluctuations in the carrier transport by studying the electrical noise also called as 1/f low-frequency noise. This dissertation work provides the first comprehensive study on the correlation between electrical transport and 1/f noise in 2D-HGFET devices relating to graphene and hexagonal boron nitride materials. Both noise analysis and transport properties were used to estimate the trap energy levels in the devices. The use of hexagonal boron nitride as the substrate and encapsulation material to a graphene conductive channel has been shown to produce high mobility transistors due to its similar crystal structure with minimal lattice mismatch, reduced impurities, lower interface trap states, and high difference in electrical conductivity. Furthermore, it has also been demonstrated that one-dimensional electrical contacts (called ”1D-edge contacts”) to 2D-HGFETs provide high current injection with low contact resistances that are necessary for high performance devices. This dissertation employs 2D-HGFETs with 1D-edge contacts to study the electrical transport, 1/f noise, and their correlation. Despite extensive transport measurements on heterostructure transistor devices, the coupling between atomic layers, particularly hexagonal boron nitride (hBN) and graphene, in terms of the phonon modes of hBN on electronic transport in graphene is in infancy. In this study, temperature dependent electrical transport and low frequency noise measurements were performed to understand the effect of phonon modes in hBN/graphene/hBN heterostructure and its conductance as 2D-HGFET device channel. The effect of longitudinal acoustic (LA) phonons in graphene conductive channel is found to be the dominant carrier scattering mechanism at lower temperatures, whereas remote interfacial polar phonons in hBN dominate carrier scattering at elevated temperatures. Furthermore 1/f noise in 2D-HGFETs were modeled to the carrier-phonon scattering and the average density of trap states in hBN/graphene/hBN heterostructure systems were calculated from which the trap energy (both electronic and hole) were extracted. The origin, impact, and engineering of phonons on the electronic and noise characteristics constitute the most significant contribution of this dissertation and it provides further avenues for the investigation of engineered 2D heterostructures and their devices.Item Embargo Capturing coronavirus: Comparison and optimization of methods for recovery of infectious coronavirus from environmental surfaces(2024) Quintana, TheresaThe unprecedented global impact of the COVID-19 pandemic posed unparalleled challenges to modern-day public health, necessitating the development of effective prevention and control measures across various settings. A critical need has been the development of rigorous and reliable environmental monitoring (EM) strategies to help understand and mitigate viral transmission, especially in crowded working conditions. COVID-19 outbreaks disproportionately affected meat and poultry (M&P) processing facilities early in the pandemic, endangering human and animal health and disrupting the food supply chain. Moreover, an increased reliance on viral RNA detection as a proxy for infectious virus particles has potentially led to inaccurate assessments of the risk of SARS-CoV-2 infection in these environments. Therefore, I evaluated commonly used EM sampling materials and innovative alternatives to identify the most effective material for recovering infectious SARS-CoV-2 particles from non-uniformly inoculated, stainless-steel surfaces, similar to those found in these facilities. Utilizing a systematic approach, I compared the performance of cellulose acetate, hydrophilic polyurethane, and a novel hydrophobic polymer material (MANO) sponge across different sampling sizes and with a variety of eluants. I found significant variations in different materials’ efficacy to recover or release SARS-CoV-2 particles. Polyurethane consistently outperformed the other materials, particularly when compared to MANOs. Specifically, surface sampling with polyurethane at 929 cm2 and 1,858 cm2 using a pH 7 beef extract buffer (BEB) solution yielded significantly superior recovery of infectious virus particles compared to MANO recovery from similar surface areas, supporting polyurethane’s potential as a preferred material for SARS-CoV-2 EM in M&P environments. Conversely, no statistically significant differences were observed in the comparisons between cellulose and polyurethane at various sizes, as well as between cellulose and MANO at certain sizes, highlighting the complexity of material performance and the need for nuanced, application-specific considerations. This research provides empirical evidence to guide the selection of EM materials for infectious SARS-CoV-2 recovery, thereby enhancing the efficiency and reliability of viral detection and surface decontamination strategies. By aligning these insights with the overarching goal of developing, validating, and delivering science-based cleaning and disinfection solutions, this thesis contributes to the broader public health effort to safeguard workers in M&P settings from SARS-CoV-2 exposure.Item Open Access Evaluating the impact of nitrogen-fixing microbial inoculants on nitrous oxide emissions and nitrogen dynamics in corn cropping systems(Kansas State University, 2024) Wanithunga, IroshaNitrogen (N) is a critical nutrient for plant growth and essential for synthesizing amino acids, proteins, and nucleic acids, which are fundamental for crop yield and quality. The introduction of N fertilizers in the early 20th century significantly boosted agricultural productivity, meeting the demands of a growing global population. However, the widespread use of N fertilizers has led to environmental concerns, particularly the emission of nitrous oxide (N2O), a potent greenhouse gas with a global warming potential approximately 300 times greater than carbon dioxide (CO2). Our comprehensive research study integrates three related investigations, each exploring different aspects of N management in corn cropping systems, including the efficacy of N-fixing microbial inoculants in mitigating N2O emissions, enhancing fertilizer N recovery, and modeling N dynamics using the Denitrification-Decomposition (DNDC) model. The first study assessed the effectiveness of N-fixing microbial inoculants in reducing N2O emissions and improving N recovery in corn production. Conducted from 2021 to 2023 at the Agronomy North Farm at Kansas State University, the experiment employed a randomized complete block design with N application rates of 0, 56, 112, and 168 kg N ha-1, both with and without bioinoculants. N2O fluxes were measured bi-weekly using the static chamber technique. N2O emissions increased significantly with higher N fertilizer rates, with the highest cumulative emissions reaching 1.13 kg N2O-N ha-1 in 2022. Most emissions occurred within the first 40 days after planting, particularly in 2022, when precipitation events were more frequent. In 2023, N2O emissions were lower and more evenly distributed throughout the growing season. Bioinoculant-treated plots consistently exhibited lower N2O emissions than untreated plots, although these reductions were not statistically significant. Emission factors (EF%) remained below the IPCC default emission factor value of 1% across all treatments in 2022 and 2023. N2O emission-yield curves had a significant linear trend with yield without bioinoculant application. However, there was no apparent trend with bioinoculant application. These findings highlight the significant role of N fertilizer rates in driving N2O emissions. The effect of bioinoculant on N2O flux was not clear and non-significant. The second study focused on N recovery in above-ground biomass and soil N dynamics using a stable isotope 15N technique, which explored the effects of N-fixing microbial inoculant across different growth stages and N application rates. Conducted over the same period and location as the previous study, this study used micro-plots to accommodate 15N fertilizer treatments. In 2022, fertilizer N recovery ranged from 6.7% to 11.62% at R6 corn growth stage, with the bioinoculant having minimal impact. However, in 2023, N recovery in biomass increased significantly, ranging from 17% to 30.9%, with the greatest recovery observed at the 168 kg N ha-1 rate with bioinoculant application. The lower N recovery in 2022 might be due to significant early-season precipitation, which can potentially increase N losses by way of leaching and denitrification. N2O emissions were higher in 2022, and more fertilizer N was recovered at the 15-30 cm soil layer. Well-distributed precipitation in 2023 facilitated better N recovery in above-ground biomass. Nitrogen immobilization was profoundly greater at 15-30 cm compared to 0-5cm and 5-15cm, especially at lower N rates. Fertilizer N recovery as organic N ranged from 8.7% to 28.3% at the harvest stage in 2023. Overall fertilizer N recoveries for both above-ground biomass and below-ground soil pools were lower than most other 15N research studies. The effect of N-fixing bioinoculant in N recovery was not clear. Long-term field trials need to be implemented to investigate the impact of N-fixing microbial inoculant on N recovery in corn biomass and soil profiles. The third study employed the Denitrification-Decomposition (DNDC) model to simulate cumulative N2O emissions under no-till, continuous corn management without bioinoculants. The objective was to evaluate the model's accuracy in predicting N₂O emissions and to assess its sensitivity to varying environmental conditions, particularly precipitation. The model was calibrated using 2022 data, incorporating daily precipitation and temperature inputs and fine-tuning soil parameters such as bulk density, pH, and soil organic carbon obtained from the Web Soil Survey database. The calibration process demonstrated strong accuracy, with a Mean Absolute Error (MAE) of 0.016, Root Mean Squared Error (RMSE) of 0.023, Mean Bias Error (MBE) of 0.016, and a Coefficient of Determination (R2) of 0.997. Validation using 2023 data showed a slight decline in accuracy, with MAE of 0.056, RMSE of 0.067, MBE of -0.051, and R2 of 0.934, indicating a minor underprediction bias. The model effectively captured the influence of precipitation on N2O emissions, accurately reflecting emission peaks during periods of high rainfall. These findings validate the DNDC model's reliability in predicting N2O emissions under specified agricultural practices and highlight the need for continuous evaluation and recalibration to maintain predictive accuracy across different growing seasons. In conclusion, these studies provide comprehensive insights into N management in corn cropping systems, emphasizing the critical role of N application rates, the potential benefits and limitations of bioinoculants, and the utility of modeling tools like the DNDC model in predicting greenhouse gas emissions. The findings underscore the importance of integrating N-fixing bioinoculants with precise N management strategies to enhance N use efficiency, improve crop yields, and mitigate environmental impacts. However, the stage-specific and N rate-dependent effectiveness of bioinoculants and the need for continuous model recalibration highlight the complexity of N dynamics in agricultural systems and the necessity for ongoing targeted research to optimize these practices for sustainable agriculture.Item Open Access Toward meaningful communication: Engineering semantics with the geometry of conceptual spaces(Kansas State University, 2024) Wheeler, Dylan T.As the demand for data transmission around the globe continues to rise, we face a challenge brought about by the fundamental limits of communication imposed by Claude Shannon's information theory. Modern communication systems are able to operate with a rate close to the theoretical channel capacity, meaning we must increase this capacity in order to transmit additional data. This is achieved by either increasing the power of the signal or bandwidth of the channel, neither of which are desirable options. Alternatively, what if we could communicate more efficiently given the resources we already have? This is the goal of semantic communication, which is a paradigm that aims to communicate a given meaning rather than exactly reproduce bits at the receiver, potentially resulting in efficient semantic representations that can communicate the same meaning using less physical bits. However, modern approaches to semantic communication suffer from a lack of consensus on how to quantify and optimize a system around the notion of "meaning" and a reliance on black-box machine learning models that obscure the true functionality of semantic communication modules. In this dissertation, we introduce an approach termed "semantic communication with conceptual spaces," which is based on the geometric conceptual spaces model of meaning. Conceptual spaces offer a general, expressive, and interpretable mechanism by which a system can utilize semantic knowledge and optimize its operation around this knowledge. We first lay the mathematical groundwork for such a system, and demonstrate its potential for efficient semantic communication via simulations. We then address a significant challenge of the conceptual space-based approach, which is obtaining the conceptual space model itself. To overcome this challenge, we develop a novel machine learning architecture capable of learning the complex domains of a conceptual space model using only high-level semantic property information. Next, we introduce a causal reasoning-based mechanism into the proposed system, which allows the semantic communication system to determine which semantic elements are most important for performing a given task. This reasoning mechanism enables the system to achieve even greater efficiency by transmitting only the semantics that are important for the task at hand. Finally, we connect our approach to traditional information theory by deriving an upper bound for the goal-oriented rate distortion function. This bound is based on a fixed threshold for the semantic distortion within the system and the novel notion of distortion discrepancy. In short, this dissertation proposes, for the first time, a method of semantic communication based on a general and expressive model of meaning that allows for automated learning of semantics, causal reasoning-based optimization, and theoretical analysis similar to classical information theory.Item Open Access Prospect to Practice: Field Trials of Phytoremediation at Landfills(Kansas State University, 2024) VanCleave, BradleyHuman contamination poses environmental concerns at many different levels. One such level is in the waste we generate on a day-to-day basis, waste that usually ends up in a municipal solid waste (MSW) facility for long term storage, well beyond typical human life expectancy. To ensure these MSW’s do not begin to pose environmental risk in the future we must develop ways to assist the Earth in self-cleaning these facilities after humans have closed them and moved on to post closure management. Phytoremediation is one such avenue that would provide a cost effective and eco-friendly approach to assisting in this management. This review looks at several recent phytoremediation projects that have been field applied at landfills around the world in pilot and full scales. Leachate and gas emissions combined to form the major components of landfill contamination, so hence this review will be limited to projects tackling these two contaminants. The author hopes that this review will serve as a guide to landfill workers to get ideas for potential phytoremediation options for their landfill before and after closure.Item Open Access Treating first-year students like adult learners: Exploring first-year seminar instructors’ classroom environments, teaching techniques, and philosophies(Kansas State University, 2024) Parzyck, AndrewThe transition from high school to college is a crucial period for students, marked by personal and academic growth as they adapt to a more independent higher education environment. First-year seminars are offered by colleges and universities to support this transition, emphasizing the importance of instructors recognizing and treating students as adult learners. The purpose of this study is to gain insight into the strategies employed by first-year seminar instructors within 4-year higher educational institutions in the New England region of the United States (CT, MA, ME, NH, RI, and VT) to cultivate adult-like learning environments and to delve into the potential outcomes associated with this approach. This research drew upon three key adult learning concepts to form the theoretical framework for this study: (a) andragogy (Knowles, 1970, 1973/1978, 1980), (b) the Psychological Teaching Environment (Brockett, 2015), and (c) the Learning Partnerships Model (Baxter Magolda, 2012). The primary goal of this investigation was to identify the significant characteristics that contribute to the establishment of adult learner-focused learning environments within first-year seminars. To achieve this, the research was conducted through a basic qualitative study utilizing semi-structured interviews, observations, and document analysis as the research method. Three rounds of coding cycles were employed: provisional, thematic coding, and pattern coding. The results found that first-year seminar instructors treat their students as adult learners, making it crucial to integrate adult learning theories into their training and teaching practices to enhance educational outcomes. Supporting first-year seminar instructors in understanding and applying these principles is essential due to the significant role they play in shaping the first-year college experience.Item Open Access Essays on forecasting time series with machine learning techniques(Kansas State University, 2024) Lashgari, AliThis dissertation consists of three essays on forecasting time series with machine learning techniques, delving into different financial and economic domains. The first essay investigates the forecasting accuracy of the S&P 500 stock market index during the pandemic, utilizing text mining and technical analysis. It finds that the LSTM model, which uses numerical data rather than financial news, offers superior accuracy in predicting price movements, showing the effectiveness of machine learning over traditional analysis methods. The second essay tests the hypothesis that the Federal Reserve responds to data revisions when setting monetary policy. To deal with the large number of data revisions that the Federal Reserve can potentially respond to, we use four machine learning techniques, Lasso, ridge regression, elastic net, and post-Lasso. When using our preferred method, elastic net, we conclude that the Federal Reserve responds to five types of data revision. We discuss the implications of this finding for theoretical macroeconomic models in the context of the signal extraction problem. The third essay addresses the predictability of West Texas Intermediate crude oil prices, influenced by macroeconomic factors. By comparing the performance of Long Short-Term Memory (LSTM) and Random Forest (RF) models, they are superior capability in both short and long-term forecasts during significant economic shocks like the 2008 financial crisis and the COVID-19 pandemic. The inclusion of SHAP analysis further enriches the understanding of how historical prices and macroeconomic indicators like the Consumer Price Index and exchange rates play pivotal roles in forecasting.Item Open Access Intergenerational linkages in debt delinquency behaviors among young adults(Kansas State University, 2024) Gan, Lena Su YuinThis dissertation investigates the intergenerational factors that influence financial debt delinquency behaviors in young adulthood, focusing on the roles of parent-child relationships, parental socioeconomic status, individual attitudes, norms, and perceived financial control. Guided by the Theory of Planned Behavior (TPB) and Family Financial Socialization Theory (FFST), the study employs path analysis to explore how these variables mediate financial behaviors. Using data from the National Longitudinal Survey of Youth 1979 (NLSY79) and its Child and Young Adult Survey (NLSCYA), the research examines key pathways, including the mediating effects of risky attitudes, parental debt norms, and perceived financial control on the relationship between parental influences and debt delinquency. The findings reveal that while parental socioeconomic status and debt norms significantly affect young adults' financial behaviors, the influence of parent-child relationships on perceived financial control is more nuanced. Effect size analyses highlight the varying impact of different predictors, revealing that perceived financial control exerts a substantially greater influence on debt delinquency compared to other factors, emphasizing the importance of targeted interventions. The study underscores the importance of considering both direct and indirect effects in understanding financial socialization. Practical implications suggest that policymakers, financial educators, and practitioners should focus on enhancing perceived financial control and shaping healthy debt norms within families to mitigate financial delinquency. Future research should address the limitations of secondary data and further refine the measurement of constructs to capture the complex dynamics of financial behavior socialization.Item Open Access Bayesian regularized quantile mixed models for longitudinal studies.(Kansas State University, 2024) Fan, KunIn longitudinal studies, the same subjects are measured repeatedly over time, leading to correlations among the repeated measurements. Properly accounting for the intra-cluster correlations in the presence of data heterogeneity and long tailed distributions of the disease phenotype is challenging, especially in the context of high dimensional regressions. Here, we aim at developing novel Bayesian regularized quantile mixed effect models to tackle these challenges. In the first project, we have proposed a Bayesian variable selection method in the mixed effect models for longitudinal lipidomics studies. To dissect important lipid-environment interactions, our model can simultaneously identify important main and interaction effects on the individual and group level, which have been facilitated by imposing the spike-and- slab priors through Laplacian shrinkage in the Bayesian quantile hierarchical models. The within- subject dependence among data can be accommodated by incorporating the random effects. The Gibbs sampler has been developed along with the Markov Chain Monte Carlo (MCMC). We have established the advantage of the proposed method over multiple compet- ing methods in extensive simulation studies and a high-dimensional lipidomics study with repeated measurements. In the second project, we further extend the sparse Bayesian quantile mixed models to nonlinear longitudinal interactions. Specifically, the proposed Bayesian quantile semipara- metric model is robust not only to outliers and heavy-tailed distributions of the response variable, but also to the misspecification of interaction effect in the forms other than non- linear interactions. We have developed the Gibbs sampler with the spike-and-slab priors to promote sparse identification of appropriate forms of main and interaction effects. Simula- tion results reveal superior performance in identification, estimation and statistical inference. In particular, the proposed method that incorporates the spike-and-slab priors can enable exact statistical inference by yielding Bayesian credible intervals with nominal coverage prob- abilities on parametric and nonparametric fixed effects simultaneously. Application of the proposed method on high dimensional longitudinal biomedical studies shed novel insight on disease etiology. The Bayesian regularized quantile mixed models proposed in this dissertation aim to tackle challenges arising from longitudinal gene environment interaction studies under the linear and nonlinear interaction assumption in chapter 2 and chapter 3, respectively. In a regression analysis framework, gene-environment interactions can be divided into linear and non-linear types, based on whether the effect of genetic factors on disease traits can be represented by linear or non-linear functions of these genetic factors. The two models proposed in this dissertation fill a significant technical gap since robust identification and inference of the two types of interactions has rarely been explored in published longitudinal studies. We have also developed C++ based R packages mixedBayes on CRAN to facilitate re- producible and fast computations using all methods under comparison in this dissertation.Item Open Access The effects of surface water availability on corn production in Weld County, Colorado(Kansas State University, 2024) Worrall, CortlandThe agriculture industry in Colorado relies heavily on the amount of snowpack and surface water to be successful and to be profitable. In Colorado they use reservoirs and ditches to disperse water to farmers and the rest to other uses and cities. The amount of water that is allocated to a single farmer can vary from year to year due to unpredictability of availability of water from snowpack and rainfall. In addition, the state has seen multiple years of population growth that has caused more of the surface water to be allocated to the rapidly growing cities instead of to farmers for agriculture use. In this study, I focus on Weld County Colorado because it is the largest agriculture producing county in Colorado and one of the fastest growing counties in terms of population Colorado. This study will look at historical data of surface water used for irrigation and the effects it has on farmers and their decision to plant corn over the last thirty years. I study the impacts on corn because corn is a major crop in Weld County and needs significantly more water than some other crops. The surface water data will come from the Colorado’s Decision Support System (CDSS) that provides all the historical and current water levels of all the reservoirs and ditches. Corn data was collected from the United State Department of Agriculture (USDA) National Agricultural Statistic Service (NASS). This study examines the correlation between surface water available for agricultural irrigation use and the amount of corn planted that year and corn yield. A regression analysis is used in this study to see how the available surface water from year to year will affects farmers and their decision to plant corn and the yield. Other variables included in the analysis to help understand the correlation between surface water and corn production are temperature and precipitation.Item Open Access Effects of vitamins, calcium, and phosphorus in nursery pig and sow diets on bone mineralization and growth performance(Kansas State University, 2024) Becker, LarissaThis dissertation is comprised of 5 chapters consisting of a literature review on calcium (Ca) and phosphorus (P) requirements in reproducing sows, two experiments evaluating the effects of vitamin D metabolites in nursery pig diets, two experiments evaluating over-supplementing folic acid in nursery pigs, a meta-regression analysis of narasin in grow-finish pigs, and an experiment evaluating two different farrowing systems during lactation. In chapter 1, a literature review was conducted to summarize Ca and P requirements in gestation and lactating sows derived from empirical and factorial models. The large variation among results of empirical studies and factorial models makes it difficult to define precise Ca and P requirements for gestating and lactating sows. However, with the most recent data a minimum level of 6.0 g/d of STTD P during gestation and 22.1 g/d of STTD P during lactation appear to meet basal requirements. The objective of chapter 2 was to determine the effects of added 25(OH)D3 with three levels of STTD P on nursery pig growth performance, bone and urine characteristics, and serum vitamin D. Overall, added 25(OH)D3 had limited effect on growth performance; however, an increase in serum concentrations of 25(OH)D3 and 24,25(OH)2D3 was observed. The addition of 25(OH)D3 to P-deficient diets increased percentage bone ash. Increasing STTD P to 100% of NRC (2012) requirement estimate increased growth and 130% of NRC maximized bone ash. The objective of chapter 3 was to determine the response to supplementation of 1,25(OH)2D3-glycoside (active form of vitamin D) provided from a plant extract on nursery pig growth performance, mortality, bone characteristics, and blood measurements. Overall, supplementation of 1,25(OH)2D3-glycoside had minimal impact on growth or serum parameters; however, increasing 1,25(OH)2D3-glycoside increased percentage bone ash. In chapter 4, two studies were conducted to determine the effect of folic acid on growth performance, SCFA concentrations, and serum homocysteine concentrations. Overall, the addition of folic acid resulted in reduced growth performance with the greatest negative impact being observed when pigs were fed 20 mg/kg. Folic acid supplementation altered SCFA concentrations and increased serum homocysteine. In chapter 5, a meta-regression analysis was conducted to evaluate the effects of added narasin in growing-finishing pig diets to predict growth rate, feed efficiency, and carcass yield. The models developed suggest important variables for predicting the percentage change in growth performance and carcass yield for pigs fed diets with added narasin include the narasin feeding duration, average weight, and growth performance of pigs fed diets without narasin. This meta-regression analysis provides equations to predict responses on growth performance and carcass yield. Lastly, in chapter 6, the effects of a pre-weaning socialization system during the lactation period on piglet growth and livability, pig lifetime performance, and subsequent sow performance were evaluated. Pigs raised in the conventional farrowing system had increased livability, lifetime growth performance, and carcass characteristics compared to the pre-weaning socialization system.Item Open Access An evolutionary consideration of stress response, biodiversity, and anthropogenic context(Kansas State University, 2024) Sharpe, Sam LipsonVariation is inherent in nature and underlies all evolutionary processes, with organismal diversity representing both a response to and a prerequisite for these processes. This dissertation considers fundamental concepts of evolutionary biology, including biodiversity, categorization of similarity and difference, and environmental heterogeneity, against the backdrop of profound threats to ecological diversity and marginalized human populations due to anthropogenic activities. In this way, evolutionary considerations of biodiversity in the Anthropocene epoch occur at the interface of nature and culture and can be understood through Donna Haraway’s term natureculture. My research uses a naturecultural framework to investigate stress response across populations of a wild plant species and explore strategies for improving representation and inclusion of sex, gender, and sexuality diversity in biology classrooms. To assess how local adaptation can impact physiological and transcriptomic responses to anthropogenically mediated sources of abiotic stress, I utilized climatically diverse populations of the wild foxtail millet, Setaria viridis. This species has many advantageous qualities as a model system, including widespread populations across a range of habitats, high genetic diversity, short generation times, small size, high rates of self-pollination, copious seed production, and a relatively small, sequenced diploid genome. Using 9 populations of S. viridis from geographically and climatically diverse home ranges, I compared photosynthetic response to drought stress in a greenhouse dry down experiment. With a subset of these populations, I assessed root surface area, aboveground biomass, and sensitivity to drought stress across plants of different ages. I found significant differences in drought tolerance by population which was not correlated with climate of origin but may be partially explained by differences in total plant size and ratio of root surface area to aboveground biomass. For S. viridis plants within the same population, plant age at the start of drought impacted both overall drought sensitivity and total production of inflorescences. To further address questions of local adaptation and abiotic stress response, I selected 2 S. viridis populations that demonstrated divergent physiological responses to drought stress and compared transcriptomic response across drought, chilling, and salinity stress conditions. I found substantial variability in the number and function of genes significantly up or down regulated between populations and treatment conditions, with only a few genes and gene functions showing similar response patterns across both populations or multiple stress conditions. There was, however, notable alignment of differently expressed genes from both S. viridis populations with a database of nearly 200 molecularly characterized drought tolerance genes. In an additional consideration of diversity, interface between science and culture, and anthropogenically mediated stress and resilience, I explored current issues in biology education that exclude or mischaracterize sex, gender, and sexuality diversity, and discussed emerging alternate pedagogical strategies for increasing inclusivity. I drew parallels between the oversimplified presentations of sex across taxa as a universal male/female binary in many biology classrooms, the ongoing social, legal and medical harms faced by queer, transgender, and intersex people, and the use of appeals to just such a universal sex binary to justify and perpetuate these harms. Importantly, biology education also offers many powerful opportunities to challenge such harmful misconceptions through providing more comprehensive, nuanced, and inclusive conceptualizations of sex, gender, and sexuality diversity across the tree of life. Through the integration of evolutionary, ecological, genomic, pedagogical, and naturecultural approaches, the research presented in this dissertation has important implications for understanding biodiversity, stress response, and resilience to anthropogenic contexts for human and non-human organisms.Item Open Access Impact of seismic reprocessing with an emphasis on improving static errors corrections on 3-D land seismic data in Ellis Co., Kansas(Kansas State University, 2024) Totten, CodySeismic datasets play an important role in petroleum exploration and development in the mid- continent. The role of good quality seismic data processing of 3D seismic surveys cannot be underestimated, as it is foundational to the creation of reliable interpretation of seismic data. It is imperative that processing is done properly, as inaccurate data can cause a multitude of problems, from simply unusable data to dry holes drilled due to incorrect placement of seismic reflectors in the subsurface. This research focuses primarily on seismic time corrections “statics” problems with processing, as well as interpreting, a seismic horizon of interest. Specifically, this project investigates the impact of remaining static errors on the fidelity of the geometrical structural seismic attributes (i.e. amplitude) and evaluates the impact on seismic resolution. As this study shows, inaccurate static-correction processing of the seismic data can have a great impact on the overall success of exploratory drilling. Land seismic data of the midcontinent present unique static time-effects challenges that must be resolved. Among the most prominent are challenges posed by both topographic elevation statics, as elevation differences arising from differentially weathered carbonates and anhydrites in the near-surface, and velocity statics arising from near surface lateral velocity variations. These types of statics need to be removed to improve the overall quality of the processed seismic. When unresolved, these issues have a large negative effects on seismic amplitude and structural fidelity of the post-stack seismic datasets. This project developed a processing workflow specific to the challenges of seismic datasets in Kansas. An iterative process was used and the processing was highly tailored to deal with the residual statics affecting seismic horizons of rock formations surfaces seen in Kansas. These formations are primarily composed of carbonates and evaporites, and so greatly affect the static solutions needed. Mapped first arrival travel-times were used as the input for travel-time tomographic inversion, creating a velocity model of the near surface. Applying this to the original dataset improves the quality of the seismic cube and removes the obvious topographic imprint present in the original post-stack interpretation. An additional benefit of this workflow was a greatly increased signal-to-noise ratio and significantly improved seismic resolution. Interpretation of this reprocessed dataset more closely corresponds to well data in the area, giving a high level of confidence in the accuracy of the reprocessing workflow.Item Open Access Assessing CyAN Satellite Accuracy: A Closer Look at Kansas' Aquatic Boundaries(Kansas State University, 2024) Angot, JordanCyanobacteria, commonly known as blue-green algae, pose significant environmental and public health challenges, producing toxins that degrade water quality and affect human health, local economies, and ecosystems. In Kansas, bodies of water like Marion Reservoir, Milford Lake, and Webster Lake are notably affected. Traditional in-situ sampling methods by the Kansas Department of Health and Environment (KDHE) often rely on public reports, potentially missing broader occurrences of harmful algal blooms (HABs). This study evaluates the efficacy of the Cyanobacteria Assessment Network (CyAN) satellite remote sensing technology as a method of bolstering existing efforts, examining its accuracy against localized in-situ measurements at the three eutrophic Kansas reservoirs. Through comparative analyses, this research assesses CyAN’s performance in estimating cyanobacteria concentrations at the water’s edge as well as when classified these into KDHE’s HAB levels: Watch, Warning, and Hazard. Findings from the cell concentration analysis demonstrate that the satellite data's accuracy varies significantly with lake geometry and spatial processing techniques, such as grid cell aggregation and zonal statistic type. However, in the KDHE HAB level classification analysis accuracy improved across all waterbodies. This research supports the potential of integrating remote sensing into existing monitoring frameworks to enhance HAB surveillance and management, providing a more comprehensive understanding of public health and environmental threats along the water’s edge in Kansas's freshwater systems.Item Open Access Contributions to ontology reasoning and modeling(Kansas State University, 2024) Eberhart, AaronThis dissertation comprises research in diverse areas related to both ontology reasoning and ontology modeling. While these two topics differ in certain important ways, fundamentally data representation and modeling is central to the development of common-sense and useful reasoning methods, and useful reasoning applications enhance and enrich semantic models with ontologies. The contributions to both topics in this document are approached separately from a unified perspective, where ontology modeling and reasoning do not merely exist as adjacent topics, but instead inform each other. The five internal chapters each correspond to unique and related publications that all orbit this core topic and are contextualized in the introduction with central themes, which are then re-examined in the conclusion.Item Open Access The impact of K-lactobionate and Bacillus subtilis on soil water retention in sandy loam soils(Kansas State University, 2024) Lincoln, MichaelDue to a combination of expanded agricultural production, global warming, and depleting water resources, semi-arid agricultural regions of the world have come under increased water stress. Such water stress results in less freshwater available for crop production and impacts the economic foundations of rural agricultural areas. One such area is the western part of Kansas. Western Kansas is an agriculturally dependent region that produces much of the nation’s crops. This area is located on top of the Ogallala aquifer, which is the water source for the large-scale irrigation farming that occurs in the area. However, due to its overuse, the aquifer has been in the process of depletion, with some areas reporting 100-foot drops in areas of pumpable water. This has spurred a need for water saving techniques. One such method is through improving soil water retention by adding amendments to the soil. Two amendments that show promise for retaining soil moisture and reducing water use requirements are Bacillus subtilis and K-lactobionate. B. subtilis is a Gram-positive bacteria that can wet soil through production of a biosurfactant molecules as secondary metabolites. K-lactobionate is a chemical compound produced from mixing lactobionic acid and KOH, and lactobionate derivatives are potentially available as a biproduct from the dairy industry. In this thesis, four studies were performed, with the goal of determining if K-lactobionate-B. subtilis amendments can be used to improve water penetration in soils, reduce runoff, and reduce water loss by increasing water retention during drought like conditions. First, water penetration and aggregation test of the two amendments in sandy loam soil was performed, which revealed that addition of a combination of K-lactobionate and B. subtilis reduced water penetration times to increase water infiltration into treated soils, and also caused the formation of large soil aggregates. B. subtilis and K-lactobionate was also studied as a means to reduce water evaporation in sandy loam soils through evaporation experiments. It was found that the addition of K-lactobionate with and without B. subtilis to the soil produced less evaporation and high water rention, while B. subtilis alone produced similar evaporation and water retention as unamended soil. Finally, an experiment testing the growth of B. subtilis when using K-lactobionate as a substrate showed that adding K-lactobionate could stabilize B. subtilis cells in otherwise nutrient-poor environments, suggesting that it could enhance the application of B. subtilis amendments while in soil or when added to irrigation water for distribution. Overall, the two paired amendments showed promise as a potential way to prevent water losses in cropland. Future studies in non-sterile environments more representative of cropland are needed as a next step to continue to evaluate the potential of these soil amendments.
