Scholars Junction - Mississippi State University Institutional Repository
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Evaluating feed sanitizers on pathogen reduction during feed mixing and assessing feed mill hygiene through microbial detection methods
No full textMicroorganisms present in feed represent a potential food safety hazard by entering the human food chain. The Food Safety Modernization Act has brought attention to the importance of research in preventative and control measures to ensure clean feed is being manufactured. Experiment 1 analyzed the inclusion of two feed sanitizers, independently and in combination, in mash feed contaminated with a Salmonella Infantis. Salmonella reduction from feed sanitizers as well as Salmonella carryover between treatments were evaluated by collecting feed and swab samples from each treatment. In Experiment 2, ingredient and feed samples were collected from feed mills located in the Southeastern region of the United States. Traditional culture methods were followed to analyze prevalence of Salmonella and Campylobacter in all samples, and culture positive samples were confirmed by PCR. The presence of E. coli, aerobic colony counts, and coliform prevalence were evaluated on CompactDry™ plates for analysis of feed mill hygiene
Use of mid-infrared spectroscopy for estimating soil chemical, physical, and hydrological properties in Mississippi and Texas
No full textAccurate and efficient estimation of soil properties is essential for advancing sustainable agriculture, water management, and soil survey applications. Traditional laboratory methods and pedotransfer functions (PTFs) are widely used but are often constrained by high costs, time requirements, and limited scalability. As an alternative, mid-infrared (MIR) and visible–near-infrared (vis–NIR) spectroscopy provide cost-effective, rapid, and non-destructive approaches for soil analysis. However, variability among spectrometer types, geographic regions, and soil scanning conditions poses challenges to model transferability and generalizability. This dissertation evaluated strategies to overcome these challenges and enhance the prediction of soil chemical, physical, and hydrological properties in Mississippi and Texas. Datasets were derived from the USDA National Soil Survey Center–Kellogg Soil Survey Laboratory (KSSL) spectral library and regional collections from both states. Study 1 examined approaches for improving the transferability of spectral data across spectrometers and regions to enhance soil property prediction. Although no single correction technique proved universally optimal, combinations of baseline correction, standard normal variate, and spiking yielded the most robust models. Study 2 evaluated the transferability of models across soil scanning conditions and demonstrated that correction techniques are essential when predicting non-fine-ground soil spectra using fine-ground spectral libraries. Among the evaluated approaches, spiking with extra weighting was the most effective, improving transferability while minimizing the need for labor-intensive grinding. Study 3 compared the predictive accuracy of spectroscopy-based models with PTF-based models (Rosetta 3) for estimating Mualem–van Genuchten hydrological parameters and derived properties. Spectroscopy consistently outperformed PTFs, achieving notably higher accuracy for predicting field capacity and permanent wilting point. The MIR-based models proved superior to vis–NIR, while spectra from fine-ground and non-fine-ground soils yielded greater stability than fresh soil spectra. Overall, these studies confirmed that MIR spectroscopy, integrated with optimized correction techniques, modeling approaches, spectrometer configurations, and soil scanning conditions, provides a reliable framework for accurately predicting soil properties. These findings underscored the feasibility of leveraging spectral libraries for regional-scale soil prediction, enabling USDA-Natural Resources Conservation Service field offices to minimize reliance on traditional laboratory methods, streamline soil analysis workflows, and enhance the timeliness and spatial resolution of soil assessments
Spatiotemporal ecology of Mycobacterium ulcerans and mycolactone functions in French Guiana and Southeastern United States aquatic environments
No full textMycobacterium ulcerans, the causative agent of Buruli ulcer, produces mycolactone, a polyketide toxin traditionally studied for its role in human pathogenesis. This dissertation presents a comprehensive spatiotemporal analysis of M. ulcerans and mycolactone producing mycobacteria (MPM) ecology in the Americas and also reveals mycolactone\u27s role in the environment as a microbial community regulator. Through comparative studies in French Guiana (endemic) and the southeastern United States (non-endemic), combined with experimental microcosm investigations, this research contributes to environmental pathogen ecology understanding. Environmental surveillance across 2,226 samples from French Guiana revealed complete MPM restriction to freshwater environments, with zero detections from 1,226 coastal samples despite extensive sampling. Temporal analysis demonstrated dramatic seasonal fluctuations, with concentrations varying by 34,000-fold within single ecosystems in the southeastern United States. Spatial distribution patterns consistently showed downstream concentration effects, with lower river reaches yielding 31.5% detection rates compared to zero to 0.6% in upper reaches across all river systems studied. Comparative analysis revealed a regional difference where the southeastern United States harbors genetically diverse MPM populations, identical to those causing endemic disease, without corresponding human transmission. Variable Number Tandem Repeat analysis identified M. ulcerans genotypes A through D and M. liflandii across non-endemic regions, indicating broader global distribution than previously recognized. Microcosm experiments of mycolactone\u27s ecological functions provided evidence that this compound serves as an environmental weapon. Mycolactone treatment caused site-specific differences in microbial community composition, with ten taxa showing significant differential abundance. The selective reduction of Gram-negative bacteria possessing quorum sensing machinery, combined with enrichment of metabolically versatile taxa, demonstrates that mycolactone provides competitive advantages through targeted suppression of bacterial competitors. This research transforms mycolactone from an understood virulence factor into a recognized environmental effector molecule, opening new research directions for understanding pathogen ecology and developing targeted intervention strategies
Conventional vs. non-conventional instrument transformers: A study of evolving technologies in power systems
No full textThis study examines the evolving technologies of conventional and non-conventional current and voltage transformers (CTs and VTs) and their role in shaping modern power systems. Conventional CTs and VTs have been fundamental to power measurement, protection, and control in traditional electrical grids. However, they face limitations in modern dynamic systems due to issues such as inaccuracy, mechanical stress, and sensitivity to environmental factors, which reduce their effectiveness in high-precision and flexible applications. Non-conventional CTs and VTs, such as optical current transformers and capacitive voltage transformers, offer significant advantages in terms of accuracy, reduced size, and digital compatibility, making them ideal for applications in smart grids and other advanced power systems. Through a comprehensive experimental analysis, this research compares the thermal performance, accuracy, load-handling capabilities, and adaptability of both conventional and non-conventional transformers. The results demonstrate that while conventional CTs and VTs continue to offer reliability in certain settings, they struggle to meet the demands of modern power systems. Non-conventional transformers, while promising in terms of performance, face challenges in integration, calibration, and long-term durability within existing infrastructure. This study highlights the need for further research to address these challenges, focusing on dynamic testing, improved calibration methods, and enhanced durability, to support the future of power systems requiring higher accuracy, efficiency, and adaptability
Improving artificial intelligence literacy among middle school students using tangible objects
No full textGiven the rapid advancement of Computer Science (CS) and Artificial Intelligence (AI), introducing these disciplines early and effectively in education is essential. Many middle school students, however, struggle to understand these subjects when taught with traditional, abstract, and screen-based methods, which often fail to engage students or build deep conceptual understanding. This study argues that tangible, hands-on learning tools and interactive activities make AI and CS concepts more accessible, concrete, and engaging for students. The findings show that, compared to traditional lectures, hands-on activities lead to substantially improved comprehension, retention, and enjoyment. While the sample size poses limitations, the evidence supports that interactive methods foster collaboration, critical thinking, and curiosity—skills essential for mastering AI and CS. The results show that game-based, interactive learning approaches clarify technical AI concepts and promote responsible use of AI. Expanding these methods, along with teacher training and institutional support, can further enhance students’ understanding, critical thinking, and preparedness for emerging technologies. This research highlights the importance of hands-on, interactive learning in AI and CS education, offering practical guidance for curriculum design and teacher preparation based on the proven benefits of these approaches
Integrated experimental, numerical, and machine-learning framework for the analysis of spray dynamics in diesel and Gasoline Direct-Injection (GDI) engines
No full textThe regulation of diesel and gasoline direct injection (GDI) during cold starts has become subject to stricter numerical limits by both the Environmental Protection Agency (EPA) in the US and the European Commission (EC). For diesel engines, the EPA’s Clean Trucks Plan (Model Year 2027+) sets a certification NOₓ limit of 0.035 g/bhp-hr and an in-use fleet average limit of 0.050 g/bhp-hr, along with a PM limit of 0.005 g/bhp-hr, representing an 82.5% reduction in NOₓ compared to the former 0.2 g/bhp-hr standard. For gasoline engines, US standards regulate cold-start pollutants such as hydrocarbons (HC) ranging from 0.03 to 0.08 g/mi and nitrogen oxides (NOx) from 0.06 to 0.7 g/mi [1]. In contrast, the European Union adopts a stricter approach through Euro standards. The upcoming Euro VII regulation (effective May 29, 2028) harmonizes steady-state, transient, and real-driving emissions (RDE) for heavy-duty diesel engines by setting unified limits of 0.200 g/kWh for both NOₓ and PM. For light-duty gasoline vehicles, Euro 6d specifies cold-start limits for HC and NOx at 30 mg/km [2]. Gasoline vehicles exhibit significantly higher Total Hydrocarbons (THC) and NOx emissions at −7°C compared to 23°C, with THC increasing 6.5-fold and NOx increasing 1.7-fold under cold-start conditions [3]. To meet increasingly stringent emission regulations for diesel combustion systems and cold-start GDI engines, which produce hydrocarbon (HC) and nitrogen oxides (NOx) emissions, extensive experimental, computational fluid dynamics (CFD), and simulation studies have been conducted to analyze the effects of injector operating conditions and fuel characteristics on spray behavior and spray topology. These studies offer a deeper understanding of how injection pressure and fuel temperature affect spray behavior and provide insights into atomization, air–fuel mixing, spray penetration, width, and 3D topology. To complement this analysis, CFD simulations using the Large Eddy Simulation (LES) model and KH-RT breakup model were employed to numerically replicate spray dynamics and validate results. Additionally, a machine-learning model was developed to predict spray characteristics and 3D spray morphology using high-speed imaging, extinction imaging, schlieren photography, and 3D tomographic reconstruction from LVF data captured at 0°, 11.25°, and 22.5°, trained on injection pressure, fuel temperature, and PLV
Toward a unified network flow framework: from conservation principles to fluid dynamics models
No full textNetwork flows govern a wide range of critical systems, from tangible infrastructures like transportation and power grids to replicable processes such as information spread and epidemics. While tangible flows obey conservation laws and physical constraints, replicable flows, like rumors or viruses, can grow, decay, or vanish unpredictably. Despite their increasing interaction in real-world settings, these flow types are typically modeled in isolation, using disconnected mathematical frameworks. This dissertation presents a unified modeling approach that bridges the gap between conserved and replicable flows by embedding principles from fluid dynamics into graph-based propagation models. I introduce physically informed extensions to classical models, such as a Navier-Stokes-inspired SIR model and a fluid-dynamic reinterpretation of the Independent Cascade model. These formulations incorporate external forces, momentum, and dissipation to bring conservation awareness to probabilistic spreading processes. To support interpretability and efficient control of network flows, I also develop scalable tools: a Sobol-based feature attribution method for influence maximization, a Bayesian optimization framework for source localization and influence blocking maximization, and a directional tensor embedding system for multilayer propagation alignment. Several of these contributions are validated on real and simulated systems, including livestock epidemic data (VSV), social influence networks, and a co-simulation platform integrating EV traffic with power grid dynamics. Together, these efforts lay the foundation for a graph-based fluid dynamics framework. It unifies stochastic, physical, and multilayer propagation into a cohesive, extensible theory. This work opens new avenues for modeling hybrid flows, improving intervention strategies, and discovering governing equations from data using tools like symbolic regression and Koopman theory
Designing Research with Care: A Critical Race Nepantlera Methodological Approach to Rural Latinx College Access
No full textResearch has widely documented the lack of educational resources and opportunities available to rural communities. Cognizant of the structural and spatial barriers facing rural communities, this study intentionally employed a critical race nepantlera methodology (CRNM) to mitigate the adverse effects of living in geographically isolated, systemically underinvested rural areas. Rather than perpetuating a false notion of objectivity in research, the research team actively supported rural Latinx students in accessing higher education through a CRNM research design, which prioritizes the well-being of research collaborators over the study. In enacting a CRNM approach, the research team demonstrated care while increasing students’ access to higher education by (a) logistically supporting college application completion, (b) emotionally validating college concerns and pursuits, and (c) building bridges to institutional resources. Implications for rural-serving schools and rural educational researchers are discussed based on deep values of care and relationality in rural communities
Residual and Sequential Herbicide Treatments in Dicamba-Resistant Soybean
No full textDicamba-resistant soybean was developed and commercialized by Monsanto in 2016, and in recent years, barnyardgrass has become more troublesome for growers who use residual herbicides with dicamba technology. Field studies were conducted from 2019 to 2021 in Stoneville, Mississippi, to evaluate barnyardgrass control after applications of glyphosate or glyphosate + dicamba, when mixed with residual herbicides, and when applied sequentially. In the first field study, glyphosate (1,120 g ae ha−1) and glyphosate + dicamba (560 g ae ha−1) were applied in combination with common residual herbicides. The second field study included an initial treatment with glyphosate (1,120 g ha−1), glyphosate + dicamba (560 g ha−1), and glyphosate + dicamba + S-metolachlor (1,064 g ai ha−1) followed by a sequential treatment of glyphosate or glyphosate + dicamba at 3 and 7 d after an initial herbicide treatment. Results indicated that glyphosate alone provided greater barnyardgrass control than glyphosate + dicamba. Additionally, at 28 d after treatment, pyroxasulfone, pyroxasulfone + fluthiacet, dimethenamid-P, and S-metolachlor did not affect postemergence control of barnyardgrass after glyphosate + dicamba treatments. Furthermore, sequential herbicide treatments of glyphosate or glyphosate + dicamba led to no difference in barnyardgrass control 28 d after the sequential treatment. These results indicate that options exist for adding residual herbicides to glyphosate + dicamba treatments and that sequential treatments of glyphosate or glyphosate + dicamba are important for optimizing barnyardgrass control
Characterization of Cowpea Genotypes for Traits Related to Early-Season Drought Tolerance
No full textCowpea (Vigna unguiculata (L.) Walp.) is a vital legume crop recognized for its nutritional value and adaptability to various growing conditions. However, exposure of cowpea to drought stress during the early growth stages can significantly restrict growth and yield potential. Therefore, identifying cowpea genotypes tolerant to drought during early growth and development is essential for maintaining yield potential. This study characterized 15 diverse cowpea genotypes for various physiological, pigment, and morphological traits that may contribute to drought tolerance. At the V2 stage, the cowpea genotypes were subjected to two moisture regimes: control (100% irrigation) and drought (50% irrigation) for 22 days to assess trait responses and their relationship to drought tolerance. Drought-stressed plants decreased stomatal conductance by 79%, negatively correlating with a 2.9 °C increase in canopy temperature. Under drought, the photochemical reflectance index (PRI) was strongly associated with the quantum yield of PSII and electron transport rate. Shoot biomass decreased by 51% and root biomass by 32% under drought. Leaf area and shoot weight were correlated with root traits such as total length, surface area, and weight. Among all genotypes, 280785-11 and UCR 1004 demonstrated superior rooting vigor under drought, emphasizing their efficiency in resource utilization. These findings highlight the relevance of utilizing drought-adaptive traits to improve early-season drought tolerance