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Agronomics
No full textAgronomics is a braided collection of short stories that weaves the lives of the people who populate a small town in Northern Indiana Each character struggles with their own definition of rural identity in a place where their place in this society is continuously in question. Beyond entertainment, these stories were written with the intent to provoke thought into what it mean to live in a rural place, how people affect their environment, and what people do as they struggle to maintain and change their identity
Leveraging Natural Variation in Yeast to Understand Susceptibility Differences in Parkinson’s Disease
Get PDFParkinson’s Disease (PD) is a neurodegenerative disorder that causes countless suffering around the world. Both forms, familial and sporadic, have been associated with the misfolding and cytotoxicity of α-synuclein, an otherwise non-pathogenic protein suspected to have various roles in the normal maintenance of the central nervous system. Model organisms have emerged as great vessels to uncover the cellular and molecular mechanisms that shape α-syn biology, and great strides have been made to understand it. Yet, it is still unclear why some individuals are affected by α-syn toxicity, while others not so much. Since it’s extremely complicated to study and probe natural variation in human populations, we turn to the friendly yeast Saccharomyces cerevisiae to understand this phenomenon. We identify wild populations of yeast with increased resistance or susceptibility to α-syn cytotoxicity, and we leverage this natural variation to further understand α-syn resistance. By employing transcriptomic and genetic mapping via whole genome sequencing approaches, we are able to narrow down candidate genes for resistance. Moreover, some of these candidate genes have also been functionally validated as mediators of α-syn resistance in yeast and opened new avenues of research in other model organisms. Finally, we employ genetic engineering and genome modification techniques to create a new panel of strains that can be used in the future for further dissecting α-syn resistance or other similar phenotypes for these strains. Our results highlight the importance of using natural variation to dissect complex traits, as we are able to pinpoint genes that are only causal for a specific genetic background, underlining the real complexity behind any resistance phenotype. Beyond our findings relevant to the PD-research field, we showcase how similar methods could be used to leverage natural variation in model organisms for other human diseases or complex traits
A Robust RF Fingerprinting Approach Using Physics-Informed Neural Networks
Get PDFRadio frequency (RF) fingerprints, caused by unique imperfections in communication hardware, offer a promising solution for zero-trust security. However, existing RF fingerprinting techniques, which aim to extract these signatures from transmitters to uniquely identify devices, often struggle with robustness in the face of temporal and spatial variations in real-world, time-varying wireless environments. For example, a neural network trained on RF signals collected on Day 1 can experience a significant performance drop when tested with data from Day 2.
To address this challenge, we propose a novel, robust RF fingerprinting method based on Physics-Informed Neural Networks (PINNs). Rather than training the model solely on the received signal, a complex mixture of RF fingerprints, time-varying channel conditions, and random channel noise, we incorporate invariant radio physics, specifically the estimated Carrier Frequency Offset (CFO), to guide the model’s learning. In addition, we input a distilled, frequency-domain equalized signal to mitigate the effects of dynamic wireless propagation conditions. Extensive experiments using USRPs were conducted to collect real-world RF data, and the proposed PINN model was benchmarked against state-of-the-art approaches. While all three methods achieved high classification accuracy (\u3e98%) when training and testing on the same day, the cross-day performance of baseline models dropped to approximately 35%. In contrast, the proposed PINN model maintained an accuracy of 97.54% across days. These results demonstrate that embedding radio physics into the learning process significantly enhances model robustness, offering a more resilient and practical path for deploying RF fingerprinting in real-world security systems
The Role of Mental and Physical Health on Fear of Crime
Get PDFFear of crime is a significant social issue that affects quality of life, often leading to behavioral changes and increased anxiety. While prior research has explored the link between fear of crime and health, notable gaps remain – particularly regarding how distinct dimensions of health influence fear of crime. This study addresses these gaps by examining the effects of perceived health and depressive symptoms on fear of crime. Data were drawn from the Arkansas Crime, Public Safety, and Health Survey (ACPSHS), an online, opt-in survey of approximately 2,300 non- institutionalized adults in Arkansas. Ordinary least squares regression models were used to assess the direct effects of self-reported health and depressive symptoms on fear of crime, controlling for key sociodemographic variables. Results show that poorer perceived health and higher levels of depressive symptoms are both significantly associated with greater fear of crime. Racial disparities were also evident, with Black Arkansans reporting significantly higher fear levels than White respondents. Gender and age differences emerged as well: women expressed greater fear than men, and fear of crime declined with age. Notably, educational attainment had no statistically significant effect, while individuals with a history of arrest were less likely to report fear. These findings contribute to a more nuanced understanding of the complex relationship between health and perceptions of public safety and carry important implications for both public health strategies and crime prevention policies
Insights From Automated Mineralogic Analysis of Modern Sand
Get PDFMethods for determining and categorizing the modal compositions of sand and sandstone have long been a subject of debate in the field of sedimentary geology. Point counting is the most commonly used technique for determining modal compositions from petrographic slides, which are then categorized based on relative proportions of quartz, feldspar, and rock (or lithic) fragments. However, this approach fails to adequately preserve relevant textural data, such as grain size and sorting, which play an important role in diagenesis and in influencing reservoir quality. Additionally, the categorical nature of point counting results in a lack of specificity and loss of valuable data regarding intra-grain mineral proportions, which could be important for provenance analysis.
The Tescan Integrated Mineral Analyzer (TIMA) can address some of the deficiencies associated with traditional point counting by providing discrete mineral maps of individual grains within a sample. TIMA is a fully automated analysis, during which a scanning microscope utilizes backscattered electron (BDE) and energy dispersive X-ray (EDX) technology to determine the precise elemental makeup of a sample. This information may then be used to infer mineralogy at micron resolutions.
This study explored the insights gained from TIMA data for determining the grain size and modal composition of seven modern sand samples with a diverse suite of compositions and textures. The purpose of this work was to develop a quantitative approach to analyzing the composition and morphology of clastic sediment to provide more insight than point counting alone in hopes of providing value to future provenance studies. A combination of ImageJ and Python scripts was used to analyze the mineralogic composition and morphology of individual grains and the distribution and compositions of each grain size within a given sample. I applied nonmetric multidimensional scaling using two dissimilarity metrics to evaluate inter-grain mineral relationships within a sample. I then assessed the quality of the fit of these solutions and suggested several explanations for suboptimal model performance. Additionally, I performed principal component analysis as an alternative method to evaluate inter-grain mineral relationships and evaluated the effectiveness of that approach. I found that, though conventional metrics suggest a poor fit, meaningful relationships could be drawn from multidimensional scaling analysis. I also concluded that principal component analysis may be a better choice for visualizing and interpreting intra-grain mineral relationships. Finally, I found that morphological analysis is both possible and insightful with TIMA data. Ultimately, this work serves as a notable step toward leveraging TIMA data for provenance analysis of sand(stone) composition
Cartographic Representation and Analysis of Sand Dune Morphometry in Wadi Rum, Jordan
Get PDFSituated in southern Jordan, near the Saudi Arabia border, Wadi Rum boasts a dramatic landscape of towering sandstone inselbergs and vast dune fields. The UNESCO World Heritage site is known for its cultural, historical, and physical uniqueness. Due to the hyper-arid environment and sandstone weathering, the region provides a perfect outdoor laboratory for aeolian research.
While research on aeolian processes has grown in popularity in the past century, there is a lack of conventional cartographic and visualization techniques in the field of aeolian studies. This project corroborates findings from influential aeolian researchers such as R.A. Bagnold while also implementing traditional and innovative cartographic practices. With the aim of cartographically representing sand dune morphometry and particle size distribution, a set of 71 vials of sand grains were collected from the slip face of the Umm Ishrin sand ramp in Wadi Rum, Jordan. These vials were sorted by particle size and weighed to determine the distribution of various grain sizes along the collection transect. As a result, several maps were produced as a way of effectively visualizing particle size distribution across the Umm Ishrin dune.
While the resulting maps validated the findings of previous studies in regard to typical aeolian processes, the maps also served as a conventional technique that had not been seen in previous aeolian research. As found in recent research, changes in sand dune development and mobility are indicators of Earth’s changing climate. This project serves to prove that the utilization of traditional cartographic and visualization techniques will aid in future aeolian studies as well as climate change research
Big Blue
Get PDFBig Blue is a record keeping system of sorts- to show the story of my relationship with the tallgrass Big Bluestem as an Oglala Lakota tribal citizen of The Sioux Nation. It’s meant to question the idea of binaries, what is valued, how verticality is only powerful if supported horizontally, and the legacy of the remnants that remain. Big Bluestem is a reminder of our linked survival as humans with the land and the importance of knowing the individual names of the leafy and legged beings who exist equally on it
Leveraging Machine Learning Models for Enhanced Landslide Prediction in Western North Carolina
No full textLandslides pose significant hazards to human safety, infrastructure, and the environment, particularly in regions of high elevation that experience extended periods of heavy rainfall. This research focuses on preparing and evaluating landslide susceptibility maps (LSMs) for the Blue Ridge Mountains, a portion of the Appalachian Mountains in western North Carolina, utilizing three machine learning algorithms: Logistic Regression, Random Forest, and Gradient Boosting Regression. Sixteen landslide conditioning factors, reflecting topographic, geological, environmental, and anthropogenic influences, were identified for model input. The landslide inventory database, comprising 7,350 locations, was randomly divided into training (80%) and testing (20%) sets. The performance of each model was evaluated and compared using confusion matrices. The Random Forest algorithm had the highest performance in predicting landslide locations, with a strong emphasis on elevation, slope, and proximity to roads as the most influential factors. The Logistic Regression model provided useful insights into the linear relationships between the conditioning factors and landslide susceptibility, performing well in areas where the relationship between predictors and landslide occurrence is more straightforward. In contrast, the Gradient Boosting model, known for its ability to capture complex nonlinear relationships, identified similar critical factors as the Random Forest model but with a higher sensitivity to variations in slope and distance to drainages. The differences in model outcomes can be attributed to the inherent characteristics of the algorithms: Logistic Regression’s simplicity and linearity, Random Forest’s ability to handle complex interactions through ensemble learning, and Gradient Boosting’s strength in optimizing weak learners for more nuanced pattern recognition. Overall, the LSMs produced by these models suggest that 20% of the study area is highly susceptible to landslides. These LSMs can serve as a valuable tool for land use planning, disaster preparedness, and risk mitigation efforts at a large scale
#Girls Gone Viral: Girlification Trends and the Digital Postfeminist Sensibility
Get PDFThis thesis provides a critical analysis of viral girlification trends on TikTok to bolster understanding of the rhetoric of popular feminism in the emerging digital landscape. Grounded in feminist cultural theory, this thesis posits that women’s digital engagement influences their notions of self-actualization, their avenues for resisting sexist ideals, and ultimately, their political mobilization. Extending scholarship on postfeminism, I offer two unique but co-constructive features of the postfeminist sensibility: post-irony and the confession paradigm. The first chapter analyzes the “girl dinner” trend as a site of post-ironic discourse. Characterized by ambiguity and ambivalence, post-ironic discourse muddles the line between sincerity and irony to the extent that no distinctive feminist sentiment can be reliably discerned. The second chapter examines on the “girl math” trend with a focus on confession, discipline, and humiliation. I argue that the postfeminist sensibility has developed a confession paradigm that promises women empowerment through self-disclosure but ultimately results in feminine humiliation and further discipline. In an effort to cultivate digital visibility in an economy of popular feminism, these trends have adopted both post-irony and confession as central discursive tools. In doing so, the culture of the postfeminist sensibility has persistently evolved and with it, the ways in which women conceptualize feminist engagement
Adopting an International Human Rights Approach in the U.S. to Combat Sizeism and Related Racism and Sexism in Healthcare, Public Health Efforts, and Food Advertising Policy
Get PDFEvidence of size stigma in U.S. food and health industries is overwhelming. Many policies affecting consumer and patient health and care look to patient Body Mass Index (BMI), a ratio of patient weight to height that anthropologists describe has roots in eugenics, scientific racism, and sexism, and that even the American Medical Association describes as being used for racist exclusion and not encompassing of sex-based differences. Many healthcare policies, public health messages, and food advertising strategies boast goals of reducing BMI in hopes of improving health status, but, in addition to having abhorrent origins, BMI has been shown to be a poor indicator of health. International Convention on Elimination of All Forms of Racial Discrimination, the International Covenant of Economic, Social, and Cultural Rights, the Convention on the Elimination of All Forms of Discrimination Against Women, and the Convention on the Rights of the Child, recognize the rights to health and food. The U.S., having ratified or at least signed each of these treaties, should therefore work to prevent sizeism in U.S. health systems and food advertising, phenomena which clearly affects healthcare access and quality, the food landscape, and overall wellbeing for people in the U.S., particularly for fat people, and especially for fat people of color and fat women