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Iowa Waste Reduction Center Newsletter, March 2026, University Of Northern Iowa. Iowa Waste Reduction Center.

Iowa Waste Reduction Center Newsletter

Contents:

--- Welcome, IWRC Advisory Committee
--- Growing Iowa’s Composting Movement to National Leadership
--- Diane Albertson Memorial Scholarship
--- P2 Green Brewery Webinar Registration is Now Open
--- Potential Hazards of Lithium-Ion Batteries in Landfills

The Modern Sisyphus Blows Leaves: Attitudes And Impacts Regarding Leaf Litter Removal On A Suburban Campus, A. C. Nony, M. R. Mcclung, M. L. Reid

Journal of the Arkansas Academy of Science

Leaf litter habitat disruption in suburban spaces stems from the prevalence of “lawn culture,” that is, the accepted societal norm that land immediately surrounding humans or meant for human recreational use should be clean, tidy, controlled, and aesthetically pleasing, above other considerations. When leaf litter on residential and recreational lawns is cleared, it removes vital habitat for organisms of several taxa, especially species that rely on litter as a moisture and temperature-controlled substrate for overwintering. We administered an opinion survey to students, staff, and faculty at Hendrix College, a highly residential and suburban campus in Conway, Arkansas, to assess views …

Time-Dependent Amplification Of Growth Rates In A Plankton-Oxygen Model, Rapha Coutin

Master's Theses

Near-bottom hypoxia occurs when dissolved oxygen levels drop to a level that is harmful to marine biology, creating biological dead zones along the ocean floor. Recent years have seen a dramatic increase in the percentage of coastal, near-bottom, hypoxic water, with the average in 2021 nearly double that of the average from 2009 to 2018 and about twenty-eight times the average from 1950 to 1980. Recent literature has linked this increase in oceanic hypoxia to the increase in upwelling-favorable winds caused by climate change. Upwelling brings low-oxygen, nutrient-rich water up to the surface, leading to plankton blooms and mass consumption …

Video Generation Techniques For Novel View Synthesis With Flow-Matching Transformers, Xiuyuan Qiu

Master's Theses

Novel view synthesis (NVS) aims to generate images of a scene from unseen camera viewpoints. Recent work, such as Stable Virtual Camera, shows that large-scale image diffusion models like Stable Diffusion can be adapted for pose-conditioned view synthesis by incorporating video-generation techniques with camera conditioning. In this thesis, we introduce MVFlow, a new NVS model that extends this approach to a different image generation architecture: a flow-matching diffusion transformer, specifically FLUX.1, which has demonstrated strong performance in image synthesis. We evaluate MVFlow under varying input view counts and pose distance settings. Our results show that this architectural transfer is feasible; …

A Critical Review Of Statistical, Signal Processing And Machine Learning Methods For Continuous And High-Frequency Water Quality Data Improvement, A.T. Badrudeen, D. Sahoo, Calvin Sawyer, Jeremy Pike, R.D. Haramel

Publications

In the digital water world, high-frequency water quality monitoring from sensors is crucial for capturing rapid changes, especially during storm events or discharge fluctuations, in which important signals can occur at sub-hourly intervals. These signals are represented in a time series and can sometimes be irregular, noisy, and prone to missing values or errors due to buried conditions, sediment interference, and signal loss. The fine resolution of reporting also increases the risk of sensor errors and data loss, necessitating effective correction methods to ensure the accuracy and usability of the data. This literature review investigates the current state of time …

Optimizing And Fortifying Ai Software Through The Lens Of Artifact Synthesis, Jieke Shi

Dissertations and Theses Collection (Open Access)

Artificial Intelligence (AI) has transformed the software landscape, ushering in a new era of intelligent systems that increasingly shape our daily lives. This transformation is evident in various domains, including Software Engineering (SE), where Large Language Models (LLMs) support many development tools, and control systems, where self-driving cars and autonomous drones rely on deep learning models for real-time decision-making. These AI systems are collectively referred to as AI software, with the former categorized as AI4SE software (AI for Software Engineering) and the latter as AI4Control software (AI for Control). As AI software becomes central to modern computing infrastructure, its reliability …

Does Genetic Testing Influence Outcomes: Early Experience In Melanoma Case Series, Ryan Reyes, George Skenteris, Stella Coker Watson Self Ph.D., Ms, Christine Marie-Gilligan Schammel, Steven D. Trocha

Faculty Publications

Background:

The treatment and prognosis of melanoma have historically been based on histologic stage and Breslow depth; however, due to the increase in surveillance, melanomas are being identified at an earlier stage and lower Breslow depth. Advances in genetic testing, such as Decision Dx®, mean that melanoma diagnostic decisions and prognosis can now be directed by genetics. The purpose of this project was to assess the influence of a Decision Dx® high-grade (Class 2A/B) classification on the treatment of melanoma patients at a regional medical center, particularly those not deemed high risk by conventional classification methods, including Breslow depth and …

A Knowledge Transfer-Based Membrane Evolutionary Algorithm For Solving Large-Scale Sorted Waste Collection Problem With Timeliness, Wenxue Zhang, Boquan Gao, Aldy Gunawan, Yunyun Niu, Jianhua Xiao

Research Collection School Of Computing and Information Systems

The sorted collection of municipal solid waste has emerged as an effective waste management strategy due to varying timeliness requirements across different waste types, giving rise to the critical research challenge of timeliness-based waste collection. While existing algorithms primarily focus on small-scale versions of this problem, solving large-scale timeliness-based waste collection problems remains particularly challenging. To tackle this issue, this paper proposes a knowledge transfer-based membrane evolutionary algorithm. Specifically, the original problem and simplified problem are constructed in different membranes respectively, and the knowledge transfer learning mechanism is incorporated into the membrane evolutionary algorithm, enabling effective information exchange between the …

Codeultrafeedback: An Llm-As-A-Judge Dataset For Aligning Large Language Models To Coding Preferences, Martin Weyssow, Aton Kamanda, Xin Zhou, Houari Sahraoui

Research Collection School Of Computing and Information Systems

Evaluating the alignment of large language models (LLMs) with user-defined coding preferences is a challenging endeavor that requires a deep assessment of LLMs' outputs. Existing methods and benchmarks rely primarily on automated metrics and static analysis tools, which often fail to capture the nuances of user instructions and LLM outputs. To address this gap, we introduce the LLM-as-a-Judge evaluation framework and present CodeUltraFeedback, a comprehensive dataset for assessing and improving LLM alignment with coding preferences. CodeUltraFeedback consists of 10,000 coding instructions, each annotated with four responses generated from a diverse pool of 14 LLMs. These responses are annotated using GPT-3.5 …

Invert Your Prompt: Editing-Aware Diffusion Inversion, Yangyang Xu, Wenqi Shao, Yong Du, Haiming Zhu, Yang Zhou, Jiayuan Xie, Ping Luo, Shengfeng He

Research Collection School Of Computing and Information Systems

Recent advancements in text-guided diffusion models have enabled powerful image manipulation capabilities. However, balancing reconstruction fidelity and editability for real images remains a significant challenge. In this work, we introduce Editing Inversion (EditInv), a novel framework that inverts and edits real images for specific editing tasks by optimizing specific prompt embeddings within the extended  space. By leveraging distinct embeddings across different U-Net layers and time steps, EditInv seamlessly integrates inversion and editing through reciprocal optimization, ensuring both high fidelity and precise editability. This hierarchical editing mechanism classifies tasks into structure, appearance, and global edits, optimizing only those embeddings that are …

Learning To Search For Vehicle Routing With Multiple Time Windows, Kuan Xu, Zhiguang Cao, Chenlong Zheng, Lindong Liu

Research Collection School Of Computing and Information Systems

In this study, we propose a reinforcement learning-based adaptive variable neighborhood search (RL-AVNS) method designed for effectively solving the Vehicle Routing Problem with Multiple Time Windows (VRPMTW). Unlike traditional adaptive approaches that rely solely on historical operator performance, our method integrates a reinforcement learning framework to dynamically select neighborhood operators based on real-time solution states and learned experience. We introduce a fitness metric that quantifies customers’ temporal flexibility to improve the shaking phase, and employ a transformer-based neural policy network to intelligently guide operator selection during the local search. Extensive computational experiments are conducted on realistic scenarios derived from the …

A Unified Methodological Framework For Generating Digital Twins Of Multi Class Uncrewed Systems (Uxs), Sai Raghava Pathuri

Shelby Hall Graduate Research Forum Presentations

No abstract provided.

Detecting Bitstream-Level Fpga Trojans With An Snn, Kylie Arnett

Shelby Hall Graduate Research Forum Presentations

Limited research has been conducted on SNNs for FPGA Trojan detection. FPGA design is often handled by manufacturers outside the U.S. FPGA manufactures outsource production to third-party foundries. This multi-step process introduces security vulnerabilities and increases risk of Hardware Trojan insertion.

Key Questions: To what extent can an FPGA be manipulated at the bitstream level to enable or disable encryption algorithms?

Can SNNs accurately detect the presence of Trojans within an FPGA?

A Scalable Iteration Of The Horizon Simulation Framework Using Multithreading Techniques, Jason E. Beals

Master's Theses

The Horizon Simulation Framework (HSF) occupies a unique space in the modern aerospace modeling landscape, enabling flexible, modular modeling of mission-level agent behavior through an object-oriented, hierarchical design. HSF's hallmark breadth-first search scheduling algorithm explores a "multiverse" of possible mission execution pathways, enabling exhaustive evaluation of schedule combinations against user-defined heuristics.

As aerospace systems become increasingly complex, HSF faces critical challenges in establishing verifiable, deterministic behavior. The framework's core scheduling algorithm had not undergone systematic validation, leaving questions about temporal consistency, state management correctness, and reproducibility across different program executions. Furthermore, the exponential growth of schedule combinations creates computational bottlenecks …

Molecular Lung Imaging Following Exposure To Radiation Predicts Long-Term Survival In Rats, Anne V. Clough, Kathrina Mpala, Taheri Pardis, Laura Norwood Toro, Andreas M. Beyer, Tracy Gasperetti, Ming Zhao, Sarah Kerns, Heather A. Himburg, Said H. Audi

Mathematical and Statistical Science Faculty Research and Publications

Delayed effects of acute radiation exposure (DEARE), including radiation pneumonitis (lung-DEARE), develop weeks to months after radiation exposure. Pathway-targeted biomarkers that capture early oxidative stress and cell death could improve risk stratification and provide objective measures of mitigator efficacy. The objective was to test whether molecular lung imaging predicts long-term survival and mitigator response after irradiation. Rats received 13.5 Gy leg-out partial-body irradiation with a subset treated with the radiation-injury mitigator lisinopril. Rats underwent lung imaging at weeks 2 and 4 post-irradiation with ^99mTc-duramycin (cell death) and ^99mTc-HMPAO (oxidative stress). Plasma mitochondrial damage-associated molecular patterns (mtDAMPs) were also …

Identification Of Microplastics And Additives From The Lake Erie Watershed And The Cuyahoga River Via Maldi-Ms And Dart-Ms, Chrys Wesdemiotis, Calum Bochenek, Luciana Rivera Molina, Robert Brand

University Research

Micro(nano)plastics have received increased attention as environmental contaminants due to their harmful effects on ecosystems and human health. Conventional extraction methods for microplastic analysis are often lengthy and complicated, resulting in the loss of important chemical components such as additives. In this study, we provide a complementary approach, utilizing matrix-assisted laser desorption/ionization (MALDI) and direct analysis in real time (DART) mass spectrometry (MS) techniques, with minimal extraction and sample preparation to preserve both polymer and additive information. MALDI-MS was applied to aqueous samples collected along the shores of Lake Erie and the Cuyahoga River, successfully identifying the base polymer(s) in …

The Application, Construction, And Validation Of Hidden Markov Model Profiles For Carbonic Anhydrase Enzymes, Samuel F. Kaplan

Master's Theses

Carbonic anhydrases (CAs) catalyze the reversible hydration of CO2 and have evolved independently at least eight times, resulting in structurally distinct enzyme families (α, β, γ, δ, ζ, η, θ, ι). Traditional sequence alignment methods struggle to classify these convergently evolved proteins because their sequential similarity does not reliably indicate functional or evolutionary relationships. Many CA sequences in public databases are annotated generically without family assignments, and prior computational approaches have focused predominantly on the three well characterized families (α, β, γ), leaving the five recently discovered classes without robust classification tools. Family level assignment is often a prerequisite for …

Demystifying Hardware Formal Verification For Undergraduate Education: A Risc-V Processor Case Study With Coursework Implementation, Riley A. Peters

Master's Theses

Hardware verification engineers apply formal methods to prove that a digital device always behaves according to its specification. This differs from traditional functional verification, in which engineers establish correctness by repeatedly sending test inputs to the device and comparing the outputs against a reference model. With the growing complexity of integrated circuits, the demand for digital verification engineers with formal methods experience has continued to increase. However, California Polytechnic State University: San Luis Obispo's current curriculum lacks dedicated material to prepare students for these roles.

This thesis seeks to address the lack of formal methods material through two efforts. First, …

Designing For Trust In Chat-Based Question Answering Systems: An Exchange-Based Retrieval Approach, Nathan Mccutchen

Master's Theses

Community chat platforms such as Discord and Slack support spontaneous, collaborative communication but make it difficult to retrieve previously discussed information. As conversations accumulate, valuable exchanges become buried, leading to repeated questions and sustained burden on experienced community members.

This work contributes a set of design requirements for question-answering systems operating over unstructured chat data, a Discord bot prototype implementing those requirements named Echo, and an empirical evaluation of how such a system affects user trust. Rather than encoding discrete question-answer pairs or generating synthetic responses with a language model, Echo indexes conversation topics for semantic retrieval and presents results …

Environmental Impact Of Preventative Fire-Retardant Applications On Soil Water Chemistry, Patrick C. Michelsen

Master's Theses

Between 1992 and 2012, 84% of all documented wildfires were started by humans. These fires often start around high-risk locations such as power lines and roadsides where fuel loads are near these potential human-caused ignition sources. Point source ignition sites that are surrounded by shrubs or grasslands are especially important because fires that occur within these biomes are responsible for far more destruction of property than forest fires. One strategy that is effective at mitigating the spread of fires from ignition sites that are within grassland or shrub biomes are preventative fire-retardant products (PFRPs). These PFRPs differ from traditional fire-retardant …

2026 March - Tennessee Climate Snapshot, Tennessee Climate Office, East Tennessee State University

Tennessee Climate Office Monthly Reports

No abstract provided.

2026 March - Tennessee Monthly Climate Report, Tennessee Climate Office, East Tennessee State University

Tennessee Climate Office Monthly Reports

Hi All,

It was a very warm and dry March across Tennessee, with temperatures 10°F+ above average for most parts of the state through the 1st, 2nd, and 4th weeks of March. Based on monthly mean temperatures, it was a top-5 warmest month on record for Memphis, Nashville, Oak Ridge, Knoxville, Chattanooga, and the Tri-Cities, and a top-5 driest month for Nashville. A strong cold front March 16-17 produced the first tornadoes of the year, in Middle Tennessee.   With warm and dry conditions, we've also seen areas classified by the US Drought Monitor as Abnormally Dry (D0) to Extreme Drought …

Empirical Comparisons Of Partial Dimension Reduction Algorithms In High-Dimensional Regression, Nathan Greenfield

Master's Theses

In high-dimensional regression problems, dimension reduction methods are often used to address the challenges of multicollinearity and estimation instability. Partial dimension reduction extends these ideas by applying dimension reduction to a subset of the predictors, while the remaining predictors are modeled without compression. This approach is particularly useful when it is important to retain variability and interpretability in certain predictors.

This thesis investigates the empirical performance of partial dimension reduction algorithms and introduces a novel algorithm, Iterative Partial Residual (IPR). Two algorithms are considered: a baseline algorithm, Marginal Residual (MR), and the proposed IPR method. Their predictive performance is evaluated …

Stock Market Price Prediction Using Big Data Models Comparison Analysis, Vibhor Pal

Shelby Hall Graduate Research Forum Posters

The stock market consists of complex financial datasets, and achieving stock price real time prediction needs an efficient big data framework for processing. This paper compares big data distributed data processing frameworks for forecasting stock prices using Graph Neural Networks (GNNs) - Apache Flink and Apache Spark. We analyze 70 publicly traded companies’ monthly data for the last 5 years from Yahoo Finance, ranked by Price-to-Earnings (P/E). In the companies’ datasets, there may be a connection or similarity between companies, and this can lead to similar stocks’ price behavior. These interfirm relationships are maintained by GNNs models, and their output …

Evaluating Software-Based Hardware Abstraction As A Fault Injection Countermeasure, Tristan Clark, J. Todd Mcdonald

Shelby Hall Graduate Research Forum Posters

Despite how theoretically secure a system may be, it can be compromised if an adversary has physical access to the device. This can be done by injecting hardware corruptions directly into the physical system, which is called a fault injection (FI) attack. To combat this, there is a need for robust fault-tolerant countermeasures. One such countermeasure is obfuscating the program to introduce redundancy and complexity, particularly through software-based hardware abstraction (SBHA).

This research proposes using SBHA as a countermeasure to fault injection attacks. By transforming point-function password programs, the proposed countermeasure aims to increase the difficulty of conducting successful FI …

Explainable Deep Reinforcement Learning For Real-Time Network Intrusion Detection, Sebastian Bustamante

Shelby Hall Graduate Research Forum Posters

This research aims to enhance current Deep Reinforcement Learning (DRL)-based Intrusion Detection System (IDS) models by adding transparency using Explainable Artificial Intelligence (XAI). This study proposes a DRL-based IDS architecture that incorporates explainability to provide interpretable reasons for IDS decisions. A Deep Q-Network (DQN) agent will be trained in a simulated network using well-known datasets to learn traffic behavior. XAI methods will be applied to extract feature importance and allow users to understand why alerts were generated. The outcomes of this research will contribute to improving network security by providing more insight on how XAI could be adopted into modern …

Text Corpus Combined Method And Tools For Music Textual Analysis, Yuwei Lu

Shelby Hall Graduate Research Forum Posters

While there has been a great deal of research conducted on how to search images and video using text, there has been less focus on how to retrieve music using multiple facets such as mood and lyrical content. or its features This is due in part to the historical lack of available musical corpuses. A musical corpus is a specialized text corpus specifically designed to capture information about music. Recently, several musical corpora for music information retrieval have been built and made available. However, these corpora and the tools designed to exploit them are typically designed to facilitate only one …

Integrating Nonlinear Phase Space Analysis And Image-Based Representation For Network Intrusion Detection, Chakriya Suon

Shelby Hall Graduate Research Forum Posters

With the rise of cyber threats, cybersecurity continues to play a critical role in the ever-changing landscape of technology by protecting and defending against threat agents. Our research applies novel machine learning (ML)techniques to detect network intrusions effectively. Our primary focus is to extend prior research, which has used network flows that are processed by a nonlinear phase space algorithm (NLPSA). The NLSPA approach has proven extremely effective in detecting anomalous or malicious traffic patterns on representative data but requires extensive training time.

Our contribution integrates deep learning into the anomaly detection approach by creating image-based representations of the adjacency …

Evaluating The Effects Of Anti-Forensic Activities In Additive Manufacturing Devices, Daniel B. Miller

Shelby Hall Graduate Research Forum Posters

Additive Manufacturing (AM) is a newer famlily of production tecchologies that constructs objects by fusing layers of material into the desired shape. Methods for achieving this as described in Gibson et al. [1] are varied and include Fused Filament Deposition, Selective Laser Sintering, Stereolithography (SLA), and Powder Bed Fusion. Computers are integral to the processes being responsible for creating and decoding design instructions, collecting and processing sensor data, and, ultimately, directing the activity of the machines which implement the process. Additionally, the AM industry is rapidly expanding, worth an estimated $23 billion in 2023 and projected to reach $88 billion …

Cellebrite Reliability In Digital Forensics, Christina Huynh

Shelby Hall Graduate Research Forum Posters

Forensic tools like Cellebrite are commonly used in court to gather and interpret raw data for evidence. Cellebrite does not only collect data but creates and interprets the artifacts of data to create a scene of the process it has been through. With this, evidence can be influenced by software designs and not just the data on the mobile device. Courts and police use Cellebrite to gather evidence and reconstruct it to create an easily readable dataset. These tools lack reproducibility, transparency, integrity, and chain of evidence command. Cellebrite is often used in court and by police without further vetting …