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The Role of Multiple Stakeholders in Pesticide Safety Policy Compliance: A Mixed-Methods Case Study Comparison in Idaho, Oregon, and Washington
No full textFarming communities across the United States (U.S.) face significant challenges with occupational safety and health (OSH) policy noncompliance, resulting in injury, sickness, and even death. Various stakeholders, including federal and state governments, nonprofit organizations, advocacy groups, and farm employees, play a role in enforcing and promoting OSH policy compliance. This dissertation investigates whether compliance with pesticide safety policies exists, identifies factors influencing compliance or noncompliance, and explores why farm management and Latinx farmworkers located in Idaho, Oregon, and Washington, choose to adhere to—or disregard—public rules and private recommendations about pesticide safety. The findings reveal varying levels of pesticide safety compliance among farms. Collaborative efforts including farm management communication with or receiving communication from OSHA, the Department of Agriculture, farm worker advocacy groups, and other farm management enhance pesticide safety compliance. However, participants emphasized the importance of proactive outreach and transparency over the frequency of communication. While pesticide safety trainings were consistently provided, many farmworkers did not fully benefit from them because they often did not understand the content, due to difficult wording. This study employs the Social Construction Framework and Narrative Policy Framework to guide research design, data collection, and analysis. A mixed-methods, case-study approach was used to collect data from surveys with farm management, focus groups with Latinx farmworkers, and interviews with policymakers and farmworker advocacy groups across Idaho, Oregon, and Washington. Insights from this research illuminate how perceptions of social constructs and the effectiveness of collaborative enforcement strategies influence pesticide safety compliance behavior. These findings inform policy recommendations to improve pesticide safety practices, ultimately mitigating adverse outcomes and enhancing OSH compliance across the U.S
Leveraging Machine Learning and Deep Learning Techniques for Voter Registration Fraud Detection
No full textThe primary objective of this research is to develop an advanced framework for detecting voter registration anomalies, with a specific focus on fraud detection, using the Idaho Voter Registration Election Dataset. The data set contains both anonymized real voter data and synthetically generated fraudulent instances, allowing for a comprehensive examination of potential vulnerabilities in voter registration systems. The real data was obtained from the Idaho Secretary of State\u27s office. The initial part of the research involved data analysis and identification of misinformation and potential disinformation using statistical analysis and approximate string matching algorithms. Subsequently, we have created the aforementioned anonymized dataset that preserves relationships between attributes in the form of similarity graphs between the major attributes.
By leveraging advanced machine learning techniques—such as spectral graph theory, positional embeddings, and deep learning models—the thesis aims to detect and classify fraudulent voter records while preserving the privacy of individuals. Moreover, our research\u27s approach is designed with flexibility, making it generalizable to voter registration systems in other states.
This research introduces the use of similarity graphs for key voter registration attributes, capturing complex interrelationships and patterns within the data. The construction of comprehensive graphs from these attributes, containing over 2 million nodes and 22 million edges, allows for the application of powerful algorithms to identify irregularities that may indicate fraudulent activity.
Furthermore, by testing both traditional machine learning models and modern approaches like Graph Neural Networks and Deep Learning, this work seeks to demonstrate the effectiveness of combining these techniques in voter fraud detection. The results of this study will provide election administrators with new tools to identify anomalies and improve the accuracy of voter registration data, thus contributing to the broader goal of strengthening election security and public trust
The Mermaid Game and Other Stories
No full textWhile the short stories in The Mermaid Game range greatly in style and form, two thematic cores run through each of them: transformation and memory. Some transformations are figurative, such as the abrupt role reversal in Old Texas Wives’ Tale. Most are more literal, as seen in the title story. Through the heady weight of memory, each story explores a metamorphosis—physical or existential, accepted or rejected—not just as external transformation but as internal reckoning
2025-2026 Boise State University Undergraduate Catalog
No full textThis catalog is primarily for and directed at students. However, it serves many audiences, such as high school counselors, academic advisors, and the public. In this catalog you will find an overview of Boise State University and information on admission, registration, grades, tuition and fees, financial aid, housing, student services, and other important policies and procedures. However, most of this catalog is devoted to describing the various programs and courses offered at Boise State
Leveraging Weekly Snow Cover Time Series for Improved Glacier Monitoring and Modeling
Get PDFSeasonal snow and ice melt strongly influence glacier mass balance, yet sparse sub-annual observations limit our understanding of seasonal dynamics. Here we construct and analyze weekly snow cover time series for 200 glaciers across western North America from 2013 to 2023 using an automated image processing pipeline. Snow cover varied widely across the region: snow minima timing varied with latitude — from August from 62 to 64N to October from 48 to 50N—and accumulation area ratios ranged from near-zero to 0.92 (median of 0.52). A comparison of snowlines from observations and the PyGEM glacier mass balance model revealed seasonally evolving but spatially consistent biases in modeled snowlines: observed snowlines rose earlier, but at a slower rate throughout the melt season, than modeled snowlines. Beyond capturing glacier state, snowline observations efficiently provide sub-seasonal mass balance constraints and empirically represent unresolved processes like snow redistribution, refining model gradients and improving projections
Toward AI Water Sustainability: Indigenous Knowledge, LiDAR, and Art
No full textArtificial Intelligence (AI) data centers consume water to remain operational. It is important to study the effects it has on the environment. Inspired by Shoshone-Bannock beliefs in “Dammen baa” translated as “our water” and as a life blood of all living things, our study looks at Indigenous water conservation efforts to guide water usage and draw attention to excessive AI water consumption globally. Through a local exploration of Idaho-Snake River Basin with Geographic Information Systems (GIS) maps of Idaho-Snake River Basin that already exist in LiDAR mapping databases online, we will create artworks using those aerial scans to explore themes of Indigenous water conservation, and AI water consumption.
Our art project brings attention to AI water sustainability through a Literature Review, the use of Decolonial Methods, and Research-based Art Methods. Our study finds AI centers can reduce water consumption through placement in cooler environments with space for water recycling while not being active during hotter times of the day, referred to as equity-aware geographical load balancing (eGLB). If AI is a necessary tool, methods must be found and put in place to mitigate its damaging effects. Current AI systems are fundamentally not built with long-term sustainability in mind. The Shoshone-Bannock tribes focus on this in their water usage as they think of current and future generations
Generating Feature-Space Functional Adversarial Malware Using LLMs
Get PDFThe application of machine learning (ML) in malware detection has significantly advanced the threat identification capabilities against zero-day malware. With the advent of ML malware detectors came adversarial malware specifically designed to evade such detectors. As a result, new research has focused on quickly and efficiently creating adversarial malware that successfully evades existing models in order to be used for adversarial training: an approach for building robust detection models. Previous work has largely focused on programmatically modifying portable executable (PE) headers and sections to generate adversarial samples. While proven effective for evasion, many samples are left non-functional, and there is still a growing need to generate adversarial malware samples that utilize assembly code obfuscations to mirror real-world samples. In this work, we propose a novel framework that employs large language models (LLMs) to obfuscate the assembly code of malware samples from the SOREL-20M dataset. We aim to verify the functionality of such obfuscations by utilizing symbolic execution and then test the effectiveness of the obfuscated samples against an existing ML detection model. Within the feature space, our framework offers an improved method for generating advanced, functional adversarial malware
Interactive Visualization Tools for Mapsy Software
No full textMapsy is a tool for identifying characteristic sites on surfaces in computational chemistry. It generates geometry-based descriptors on the contact space - i.e., the region of space close to the surface - and uses machine learning to cluster points within this space according to their chemical environment. Currently, Mapsy displays individual 2D cross-sections at defined coordinates of the contact space and the associated descriptors. In this project, we integrate the visualization module Vizzy into the Mapsy workflow, enabling the visualization of 3D data and facilitating the interactive selection of 2D cross-sections. Including Vizzy as a dependency in the Mapsy workflow supports direct imaging of the contact spaces and descriptors generated by Mapsy without the use of external tools
The Stochastic Nature of Multicellular Organisms in Response to Gradients
No full textStochastic behavior is well documented in single-celled microbes, driving phenomena such as variable infection rates and antimicrobial resistance, but its influence in multicellular organisms is still poorly understood. Here, we investigate this question using the nutrient-foraging behavior of Medicago truncatula roots, a model chosen for its ease of cultivation and imaging. We hypothesized that the stochastic movement seen in single cells persists in multicellular organisms. Seedlings were grown in custom microfluidic devices that maximized nutrient heterogeneity, and embryonic roots were imaged with a 10× objective for roughly ten days. At each time point we measured three traits: root length, tip angle, and the distance from the tip to the nearest low-nutrient zone. Cross-correlations among all these traits showed no strong relationships, indicating the absence of deterministic guidance and pointing instead to stochastic growth. We are currently developing a mathematical model to formalize these observations and to elucidate the stochastic nature of nutrient-seeking root behavior
Exploring Masp1 and BMP4 Interactions During \u3cem\u3eXenopus\u3c/em\u3e Gastrulation
No full textMutations in the MASP1 gene can result in 3MC syndrome, a developmental disorder that presents with craniofacial abnormalities and cognitive impairment. Within its well-characterized role in the innate immune system, MASP1 acts as a serine protease that can cleave a multitude of substrates. However, MASP1 cleavage of substrates during embryonic development are unknown. Using Xenopus laevis as a model organism, we aim to understand how Masp1 functions during embryonic development. Previous research has shown that BMP4 signaling influences ectodermal specification, during gastrulation, contributing to neural and craniofacial tissue formation. Our data suggest that Masp1 modulates BMP4 signaling to ensure proper ectodermal specification. Here, we are performing in situ hybridization using BMP4 RNA probes to assess changes in BMP4 expression patterns in embryos in response to Masp1 overexpression and knockdown. In addition, we aim to identify Masp1-interacting proteins, particularly those involved in BMP4 signaling, using co-immunoprecipitation followed by mass spectrometry. As a first step, we are performing western blot analysis to identify antibodies that recognize Masp1 and characterize Masp1 protein levels in gastrulating embryos. Through these experiments we will identify molecular mechanisms underlying the Masp1/BMP4 interaction and advance our understanding of how MASP1 mutation leads to 3MC Syndrome patient phenotypes