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Expanding access: A college access model for English learners
No full textAs one of the fastest-growing student populations in K -12 schools, English Learners (EL)s remain significantly underrepresented in postsecondary education. Barriers such as academic preparedness, financial constraints, and sociocultural factors hinder their access to postsecondary education. However, by developing targeted strategies to assist ELs in accessing and completing college, institutions can address these challenges and ensure a more diverse and sustainable student body in the face of declining enrollment. Effective models for helping EL students access college include family engagement, early college exposure, peer mentorship, and student-centered advising. By assisting ELs in accessing college, institutions can strengthen their enrollment efforts, promote inclusivity, and mitigate the effects of the demographic shift. Grounded in Social Capital Theory (Bourdieu, 1986; Coleman, 1988) and the Appreciative Advising Framework (Bloom et al., 2008, p.11; Hutson & He, 2011), this project aims to provide EL students and their families with the information, support, and resources available to help them access and persist in postsecondary education. Applying this theoretical approach, the project acknowledges that social connections and information networks shape EL students\u27 access to college. This initiative aims to foster meaningful relationships with EL students, families, K -12 educators, and college staff to enhance the college planning process through increased support and education for navigating college enrollment. Furthermore, the project draws on the Appreciative Advising Framework, emphasizing the importance of supportive, strengths-based relationships between students and advisors (Bloom et al., 2008). Together, these frameworks emphasize the importance of relationships and information networks in promoting student success. By employing EL students\u27 strengths and expanding their access to support, knowledge, and resources, this project will create a student-centered college access model that empowers EL student and their families to navigate the college onboarding process with confidence and support
Markov Chain Monte Carlo techniques– innovations in lifetime data analysis and missing value imputations
No full textThis dissertation develops scalable Bayesian solutions for two critical challenges in modern data analysis: modeling failure times and imputing missing biological data. First, we introduce an adaptive semi-parametric MCMC framework for Weibull lifetime modeling, addressing the lack of conjugate priors and multidimensional sufficient statistics. Using hierarchical modeling and the No-U-Turn Sampler (NUTS) in STAN, we evaluate 24 prior combinations across 72 simulated datasets. The method yields robust parameter estimates under increasing and decreasing hazard rates and proves effective in predicting prostate cancer patient survival. Second, we assess imputation strategies for high-throughput proteomics data, where missingness distorts signal integrity. We compare MCMC, MICE, QRILC, and Random Forest methods using MAE, NRMSE, and correlation analyses. Among them, MCMC best preserves data structure across varying missingness and dimensionality. Together, these contributions demonstrate the versatility and robustness of Bayesian modeling for structured and unstructured data environments, offering practical tools for inference under uncertainty
An evaluation of the Mississippi 4-H/FFA replacement beef heifer development contest
No full textThe Mississippi 4-H/FFA Replacement Beef Heifer Development Contest began in 2008 to provide youth with a contest to showcase their managerial skills relating to beef cattle. This study examined the impact of contest participation on development of life skills, technical skills, and choice of occupation. Youth and parents/guardians reported slight to moderate improvement in life skills. The same groups also agreed that technical skills were improved through contest participation. While most youth did not consider the contest to impact their choice of occupation, all indicated that they are currently involved in the beef cattle industry as adults. Overall, these results highlight the positive value of the Mississippi 4-H/FFA Replacement Beef Heifer Development Contest
A mathematical theory of epitaxial growth
No full textIn this dissertation we investigate several PDE models of Epitaxial growth. These models are fourth-order PDE’s featuring exponential nonlinearities as well the p-Laplacian and 1-Laplacian. The presence of the exponential nonlinearity is what provides the main mathematical difficulty. Theexponent in particular does not have enough estimates to guarantee any compactness. Because of this, one has to allow the inclusion of a singular portion to the exponent in the sense of the Lebesgue decomposition theorem. In thefirst chapter weinvestigate a related epitaxial growth, with transition rates of the Metropo lis variety and a linear exponent. The metropolis rates induce an extra term which includes the reciprocal of the exponential part. Weareabletotakeadvantageofthistoprovetheglobalexistence of solutions without any singular portions. In the second chapter we consider the original model with a linear exponent. Building off of the idea in chapter 1, we impose a smallness condition on the initial data to prove the global existence of a solution without a singular portion. The strategy involves deriving a quadratic inequality which depends on the initial data. We are then able to leverage this inequality to show that the exponent remains bounded for all time. In the third chapter we study the model with a nonlinear, p-Laplacian in the exponent. The nonlinear exponent renders many of our earlier estimates untenable. We develop some new estimates for �� in between one and two. With this we are then able to prove enough compactness to justify passing to the limit. With no size restriction on the initial data, we have a singular portion to the exponent. In the fourth chapter, we consider the model with gradient dependent mobility coefficients. Due to the coefficients, the previous tools are no longer enough to handle the model. For this reason we first linearize the exponential term. We also have included the 1-Laplacian in our analysis, which in our case is dominated by the p-Laplacian for all �� greater than one. We prove the global existence of solutions, without any singular portions
Neural architecture search-driven unsupervised domain adaptation for enhanced wood chip quality evaluation in forest industries
No full textReliable and efficient measurement of wood chip moisture content is crucial for forest-reliant industries, including biofuel production, pulp and paper manufacturing, and bio-refineries, as it directly influences product quality and energy output. Traditional methods like the oven-drying technique, despite their widespread use, are time-consuming and impractical for real-time applications, while modern alternatives such as NIR spectroscopy, electrical capacitance, and microwave analysis are often costly, less portable, and require specialized expertise. This dissertation addresses these limitations by leveraging deep learning and machine vision to develop a scalable, accurate, and portable method for moisture content measurement using RGB images of wood chips. A dataset of 1,600 images was collected and annotated based on ground truth data from oven-drying results. Using this dataset, two optimized neural networks, MoistNetLite and MoistNetMax, were developed through Neural Architecture Search (NAS) and hyperparameter optimization. MoistNetMax achieved a 91% accuracy, outperforming state-of-the-art models like ResNet152V2 by 9.6%, while MoistNetLite provided fast and efficient predictions suitable for deployment on portable devices such as smartphones. To enhance the robustness of predictions across domains, we integrated Kernel Fisher Discriminant Analysis. We developed a Bayesian Optimization strategy, ensuring a balance between feature transferability and discriminability, which led to superior performance across benchmark datasets. Furthermore, texture-based analysis using Haralick features was conducted to explore moisture content classification, validated by Local Interpretable Model-agnostic Explanations (LIME) to provide insights into model decision-making. Building on this, a framework called AdaptMoist was proposed by integrating five types of texture features with a domain-adversarial network and introducing a custom model selection strategy based on Adjusted Mutual Information (AMI). Finally, the work extends NAS to the domain adaptation setting. A novel NAS-driven Unsupervised Domain Adaptation (NAS-UDA) framework was introduced, incorporating a structured search space, a set of label-free proxy metrics, including a newly validated Hellinger distance, and an ensemble-based prediction strategy to improve robustness across domains. Collectively, this dissertation provides a unified, interpretable, and generalizable solution for wood chip moisture content prediction across varying operational conditions
Modeling groundwater flow and estimating agricultural withdrawals in the Mississippi Delta: Insights from GMS-MODFLOW and climate change projections
No full textGroundwater plays a critical role in sustaining agricultural productivity in the Mississippi Delta, where irrigation depends almost entirely on withdrawals from the alluvial aquifer. However, persistent over-pumping, particularly during the growing season, has led to significant groundwater level (GWL) declines, raising concerns about long-term sustainability. Historically, groundwater studies in the region have relied on biannual measurements, limiting insight into seasonal GWL fluctuations. This creates a research gap, particularly during peak irrigation periods when withdrawals are highest. More frequent data, on a daily or monthly scale, would enable earlier trend detection and more responsive groundwater management. Accurate pumping data is also limited due to voluntary reporting and the scarcity of installed flowmeters, making it difficult to quantify actual groundwater use. This study addresses these gaps by analyzing daily GWL data and introducing a novel method to estimate daily agricultural pumping across the Delta and at the counties, an essential step toward improving groundwater models. Another key gap lies in modeling groundwater using daily data, which allows for faster responses to level changes and better tracking of evolving trends compared to infrequent seasonal observations. Moreover, research into the long-term effects of climate and land use change on groundwater in the Delta remains in its early stages. This study integrates observational data, modeling, and future projections to assess past, present, and potential future groundwater dynamics. It captures detailed spatiotemporal GWL fluctuations and estimates groundwater withdrawals at seasonal, monthly, and daily scales based on crop distribution and irrigation demand. Simulations using the GMS-MODFLOW platform demonstrate that GWLs are highly sensitive to recharge variability, pumping intensity, and aquifer properties. In addition, future projections under three climate scenarios (SSP126, SSP245, SSP585) suggest recharge rates may generally decline and cropping patterns may shift, likely intensifying groundwater stress, especially in the central Delta. However, these impacts will differ across time and space. These findings underscore the need for high-resolution data and adaptive, forward-looking water management strategies to ensure the sustainable use of this critical resource
Ex vivo evaluation of polyethylene cable compared to stainless steel cerclage wire in a canine fracture model
No full textA long oblique osteotomy model was created using paired canine cadaveric femurs. The osteotomies were stabilized with either three ultra-high molecular weight polyethylene (UHMWPE) cables (n=10) or three 18 gauge stainless steel loop cerclage wires (n=10). Cyclic testing was performed in four-point bending by applying increasing force at 2 Hz until construct failure, defined as ≥2mm of actuator displacement. Data analyzed included cycles to failure, load at failure, and dynamic stiffness. There was no statistically significant difference in any of the outcomes tested between constructs. Visible loosening was noted in all loop cerclage constructs. No visible loosening of the UHMWPE cable was noted. The results suggest that the UHMWPE cable’s resistance to failure was comparable to SSW in four-point bending
Basket Weavers at Work; Basketmakers
No full textThis postcard features a black and white image of a Black woman and a Black man weaving large baskets outside among a yard of tree limbs and weaving materials. The woman is standing with her hand resting on her completed basket while the man is seated behind the basket he is working on weaving. The back of the card is addressed to Dr. D. K. Cason in Nacogdoches, Texas. The card is postmarked West Point, Mississippi, June 21, 1909 and a green, one cent Benjamin Franklin postage stamp is placed in the upper right corner. An inscription on the left side reads Dear Papa, Minnie and Happy are coming today. I am having a nice time. Emilyhttps://scholarsjunction.msstate.edu/mss-lampton-images-us-african-americana/1033/thumbnail.jp
Living Act 31: Perspectives From Bayfield, Wisconsin
Get PDFIn this article, we discuss the teaching of Indigenous land sovereignty, history, and culture, commonly referred to as Act 31, in the School District of Bayfield in Bayfield, Wisconsin. Since the legislative mandate in 1991, the Wisconsin Department of Public Instruction has strongly recommended that Wisconsin students receive instruction related to Act 31 twice in elementary school and once in high school. However, because Act 31 is not strictly enforced, there is uneven implementation throughout the state. At the School District of Bayfield, teaching Act 31 is mainstreamed in the curriculum. Here, five teachers offer their vignette, or story, on infusing Act 31 into their instruction. This scholarship emerged from a collaboration between the School District of Bayfield and the University of Wisconsin-Platteville’s School of Education
Cyber security threat recognition and preparedness of undergraduate students
No full textCybersecurity awareness and preparedness are critical competencies for individuals across academic disciplines and professional sectors. However, undergraduate students often lack sufficient knowledge and skills to recognize and mitigate cybersecurity threats. This dissertation examines cybersecurity threat recognition and preparedness among undergraduate students through a three-phase research approach. Study 1 explores faculty perspectives on students\u27 cybersecurity awareness, identifying gaps in knowledge and preparedness across various fields of study. Study 2 investigates industry professionals\u27 perceptions of new hires’ cybersecurity readiness, assessing the alignment between academic training and industry expectations. Study 3 evaluates the effectiveness of an online intervention designed to enhance students\u27 cybersecurity awareness through video-based training. This research is grounded in Protection Motivation Theory (PMT), which examines how individuals assess threats and adopt protective behaviors. Findings indicate that students in technical disciplines exhibit greater cybersecurity competence, while those in non-technical fields demonstrate limited awareness of security best practices. Faculty members emphasize the need for foundational cybersecurity education across disciplines, and industry professionals highlight the importance of applied cybersecurity training in workforce preparation. The intervention study demonstrates that structured cybersecurity education significantly enhances students’ ability to recognize and respond to threats. The results underscore the need for interdisciplinary cybersecurity education, targeted training programs, and institutional policy reforms to ensure that all students, regardless of major, develop fundamental cybersecurity competencies