Digital Commons Network^™

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 …

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 …

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 …

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 …

Detecting Sensor Data Manipulation, Ricky Green, Michael Black

Shelby Hall Graduate Research Forum Posters

The integration of Information Technology (IT) and Operational Technology (OT) have made OT devices vulnerable to threats that have been successfully exploited with devastating results. Many modern techniques for hardening and securing enterprise IT systems are either incompatible with OT components in an Industrial Control System (ICS), reduce the efficiency of processes, or are prohibitively expensive to implement. Research in the area of ICS security focuses on a top-down approach, such as intrusion prevention by securing the perimeter of the network at layers 3 – 5 of the Purdue model by hardening IT systems. This approach is useful in Enterprise …

Trophic Structure And Mercury Bioaccumulation In Walleye And Yellow Perch In The Upper And Lower Red Lake Basins, Marissa Pribyl

Biology Graduate Theses

Mercury is a persistent global contaminant that biomagnifies through aquatic food webs, with dietary and environmental factors serving as the primary drivers of accumulation in fishes. Trophic structure and methylmercury dynamics in Walleye (ogaa; Sander vitreus) and Yellow Perch (asaawens; Perca flavescens) were investigated in the Upper and Lower Red Lake basins in Red Lake, Minnesota, during 2024-2025. Diets of Walleye and Yellow Perch were assessed through stomach dissections, and tissue samples from both species were analyzed for total mercury concentrations. Additional analyses included shiners (gigoozens; Notropis spp., Hudsonius spp.) along with a variety of freshwater fish and …

Driver Behavior Analyzer 2.0: A Modular Framework For Interpretable Driver Safety Analysis From Obd-Ii And Gps Telemetry, Sangwhan Cha, Venkata Sundar Kamesh Durvasula

Harrisburg University Other Works

Driver behavior analysis plays a central role in advancing road safety and enabling data-driven driver feedback. Although commercial telematics platforms offer sophisticated analytics, they are frequently expensive, proprietary, and optimized for enterprise-scale use. At the same time, low-cost On-Board Diagnostics II (OBD-II) adapters make telemetry collection widely accessible, but they typically do not provide higher-level behavioral interpretation.

In this paper, we present Driver Behavior Analyzer 2.0 (DBA 2.0), an offline-first, modular analytics framework that converts OBD-II and GPS telemetry into interpretable safety insights. DBA 2.0 supports ingestion of telemetry logs in CSV and JSON formats, data normalization, rule-based detection of …

Stard-Net: Spatiotemporal Attention For Robust Detection Of Tiny Airborne Objects From Moving Drones, Hasibur Rahman, Sanjay Kumar Madria

Computer Science Faculty Research & Creative Works

The rapid adoption of drones across various domains, alongside advancements in computer vision, has driven growing interest in vision-based airborne object detection from moving aerial platforms. However, this task remains challenging due to the small scale of objects, camouflage within cluttered backgrounds, and occlusions. To address these challenges, we introduce an end-to-end detection framework that integrates a Drone Receptive Field Block (DRFB) to extract multiscale and geometrically diverse features, specifically designed to enhance the detection of small and camouflaged airborne objects. To model motion patterns over time while preserving spatial structure, particularly for detecting camouflaged, cluttered and occluded objects with …

Using Large Language Models To Analyze Political Texts Through Natural Language Understanding, Kenneth Benoit, Scott De Marchi, Conor Laver, Michael Laver, Jinshuai Ma

Research Collection School of Social Sciences

Large language models (LLMs) offer scalable alternatives to human experts when analyzing political texts for meaning, using natural language understanding (NLU). Qualitative NLU methods relying on human experts are severely limited by cost and scalability. Statistical text-as-data methods are scalable but rely on strong and often unrealistic assumptions. We propose a systematic, scalable, and replicable method that can extend existing qualitative and quantitative approaches by using LLMs to interpret texts meaningfully rather than as mere data. Our ensemble means of LLM-generated estimates of party positions on six key issue dimensions correlate highly with equivalent mean ratings by country specialists. When …

Argument-Based Consistency In Toxicity Explanations Of Llms, Ramaravind K. Mothilal, Joanna Roy, Syed Ishtiaque Ahmed, Shion Guha

Health Services and Informatics Research

The discourse around toxicity and LLMs in NLP largely revolves around detection tasks. This work shifts the focus to evaluating LLMs’ reasoning about toxicity—from their explanations that justify a stance—to enhance their trustworthiness in downstream tasks. Despite extensive research on explainability, it is not straightforward to adopt existing methods to evaluate free-form toxicity explanation due to their over-reliance on input text perturbations, among other challenges. To account for these, we propose a novel, theoretically-grounded multi-dimensional criterion, Argument-based Consistency (ArC), that measures the extent to which LLMs’ free-form toxicity explanations reflect an ideal and logical argumentation process. Based on uncertainty quantification, …

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 …

Securing Ai Models Against Backdoors, Jailbreaks, And Prompt Injections, Myat Min Nay

Dissertations and Theses Collection (Open Access)

Deep neural networks (DNNs) and large language models (LLMs) in safety-critical settings face a family of related threats: an adversary can corrupt training so a hidden trigger flips a model’s behavior (backdoor), craft inference-time inputs that override safety alignment (jailbreaks) or smuggle in task-overriding instructions (prompt injections), or steer the model’s stance on whole concepts (semantic inconsistencies). Current defenses address each of these in isolation. This dissertation argues they admit a unified treatment through a shared diagnostic design principle: a matched perturbation, probing, or operating condition exposes a method-specific diagnostic signal in the model’s internal computation or observable behavior, and …

Graph Convolution Neural Network And Deep Q-Network Optimization-Based Intrusion Detection With Explainability Analysis, Kelvin Mwiga, Mussa Dida, Leandros Maglaras, Ahmad Mohsin, Helge Janicke, Iqbal H. Sarker

Research outputs 2022 to 2026

As networks expand in size and complexity, coupled with an exponential increase in intrusions on network and IoT systems, this leads to traditional models failing to capture increasingly intricate correlations among network components accurately. Graph Convolution Networks (GCNs) have recently acquired prominence for their capacity to represent nodes, edges, or entire graphs by aggregating information from adjacent nodes. However, the correlations between nodes and their neighbours, as well as related edges, differ. Assigning higher weights to nodes and edges with high similarity improves model accuracy and expressiveness. In this paper, we propose the GCN-DQN model, which integrates GCN with a …

Software Engineering In The Age Of Coding Agents: Failure Modes And Rejection Patterns, Mahd Mohd Hindi

Theses

This thesis investigates the real-world behavior of LLM-driven coding agents that generate code changes and submit pull requests (PRs) to public software repositories. As these tools evolve from autocomplete-style assistants into more autonomous agents, their contributions increasingly interact with socio-technical review processes (human reviewers, bots, CI/CD gates, and project norms). The thesis focuses on understanding why agent-generated PRs are accepted or rejected and what these outcomes reveal about current agent limitations in practical development workflows.

The main objective of this thesis is to systematically characterize rejection patterns and failure modes of agent-generated pull requests in real repositories. Specifically, the thesis …

A Cryptographic Perspective On The Verifiability Of Quantum Advantage, Nai-Hui Chia, Honghao Fu, Fang Song, Penghui Yao

Computer Science Faculty Publications and Presentations

In recent years, achieving verifiable quantum advantage on a NISQ device has emerged as an important open problem in quantum information. The sampling-based quantum advantages are not known to have efficient verification methods. This article investigates the verification of quantum advantage from a cryptographic perspective. We establish a strong connection between the verifiability of quantum advantage and cryptographic and complexity primitives, including efficiently samplable, statistically far but computationally indistinguishable pairs of (mixed) quantum states (EFI), pseudorandom states (PRS), and variants of minimum circuit size problems (MCSP). Specifically, we prove that a) a sampling-based quantum advantage is either verifiable or can …

In What Style Shall I Confront Them? The Role Of Social Relationships In Social Correction Of Misinformation Among The Uk And Arab Social Media Users, Muaadh Noman, Mohamed B. Almourad, Ala Yankouskaya, Firoj Alam, Raian Ali

All Works

This study investigates how social factors influence the likelihood of employing direct or indirect communication styles when correcting misinformation on social media in two different cultural contexts, the United Kingdom (UK) and the Arab Gulf Cooperation Council (GCC) countries. We conducted an online survey, supported by vignettes, that involved 686 participants, 367 from the UK and 319 from the Arab GCC countries. Participants were presented with a misinformation scenario and asked about their likelihood of using direct or indirect communication styles to correct their acquaintances. The survey captured variations in gender similarity (same vs. different gender), social status (lower vs. …

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 …

An Electrochemiluminescence-Based Arsenic (Iii) Sensor Using Luminol On Screen-Printed Gold Electrodes, Harmesa Harmesa, Isnaini Rahmawati, Andrea Fiorani, Yasuaki Einaga, Eny Kusrini, A’An Johan Wahyudi, Asep Saefumillah, Tribidasari A Ivandini

Journal of Electrochemistry

Electrochemiluminescence (ECL) of luminol has been studied on a screen-printed gold electrode for a simple and sensitive detection of arsenic ions (As(III)). Cyclic voltammetry (CV) was applied as the proposed technique to study luminol’s electrochemical behavior and to evaluate the arsenic’s effect in the ECL system, while hydrogen peroxide (H₂O₂) served as a co-reactant to enhance luminol’s light emission under alkaline conditions. To achieve optimal electrode performance, key parameters including pH, scan rate, and the concentrations of H₂O₂ and luminol were carefully optimized. The presence of As(III) induced a quenching effect on the …

Cultural Science Night, Jiyoon Yoon

Mavs Open Press Open Educational Resources - Archive

This book is to learn water and weather by integrating with other cultures. This textbook is organized around two interconnected components. The first component provides cultural knowledge of China, India, Korea, and Mexico. The second component focuses on culturally responsive science instruction created for the Cultural Science Night event, an occasion in which teachers present hands-on, culturally responsive science activities in collaboration with students.

If you need an accessibility accommodation or have questions about the use of this text, please contact Mavs Open Press.

Re: Conditional Approval Letter For The Butte Priority Soils Operable Unit (Bpsou) Revised Butte Hill Revegetation Specifications (Dated August 15, 2025), Molly Roby

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Identical Vanishing Of Coefficients In The Series Expansion Of Eta Quotients, Modulo 4, 9 And 25, Tim Huber, James Mclaughlin, Dongxi Ye

School of Mathematical & Statistical Sciences Faculty Publications

Let A(q)=:∑∞n=0anqn and B(q)=:∑∞n=0bnqn be two eta quotients. In some previous papers, the present authors considered the problem of when

an=0⟺bn=0.

In the present paper we consider the “mod m” version of this problem, i.e. for which eta quotients A(q) and B(q) and for which integers m>1 do we have (non-trivially) that

an≡0(modm)⟺bn≡0(modm)?

(We say “non-trivially” as there are trivial situations where an≡bn(modm) for all n≥0). The m for which we found non-trivial (in the sense just mentioned) results were m=p2, p=2,3 and 5. For m=4 and m=9, we found results which …

2022 Bpsou Stormwater Evaluation And Maintenance Report Annual Stormwater Report For Bpsou Stormwater Structures, Butte-Silver Bow Department Of Reclamation And Environmental Services

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Bl(U)E Crab: Bluetooth Low Energy Connection Risk Assessment Benchmarking, Dylan Christopher Conklin

Dissertations and Theses

The usage of Bluetooth Low Energy (BLE)-based tracker devices for stalking has become a salient privacy concern. Detecting unwanted or suspicious trackers is challenging due to their cross-platform compatibility issues, inconsistent detection methods, and lack of an industry-wide standard for detecting malicious devices. BL(u)E CRAB, Bluetooth Low Energy Connection Risk Assessment Benchmarking, scans data and generates risk factors about nearby devices to classify them as suspicious or not. These risk factors include the number of encounters the user had with a device, the duration of time a device has been near the user, the distance a device has traveled …

Soil Re-Engineering In Western Australia, Part I: A Novel Approach For Rapid And Lasting Improvement Of Soil Physical And Chemical Properties, Gaus Azam, Chad Reynolds

Natural Resources Research Articles

Context

Soil acidity, high soil strength, and poor subsoil structure are major constraints to crop productivity in coarse-textured and texture-contrast soils of southern Australia. These interacting limitations restrict root growth, reduce access to subsoil water and nutrients, and constrain yield potential in water-limited environments. While deep tillage and surface liming have been used to address individual constraints, their benefits are often short-lived and insufficient to overcome multiple subsoil limitations simultaneously. Aims

This study aimed to evaluate the short- and long-term effects of soil profile re-engineering on soil physical, chemical, and hydrological properties, and to determine the persistence of these changes …

Sensi-Train: Context-Aware Sensitive Content Detection Using Transformers And Large Language Models, Nazeefa Muzammil

USF Tampa Graduate Theses and Dissertations

The detection of sensitive content has become a critical task in natural language processing due to the widespread use of online platforms and the social harm caused by it. Even though current methods have advanced significantly, many of them mainly rely on surface-level text and neglect to take into consideration contextual and cultural elements that are crucial for correctly assessing sensitivity of any content to a particular individual. Due to this, implicit offense, sarcasm, and culturally grounded expressions frequently cause problems for such systems. To enable culturally grounded sensitive content detection, this thesis presents Sensi-Train, a context-rich dataset and modeling …

Comments On The Draft Environmental Impact Statement On Post-2026 Operational Guidelines And Strategies For Lake Powell And Lake Mead, Anne Castle, John Fleck, Eric Kuhn, Jack Schmidt, Katherine Tara, Kathryn Sorensen

The Traveling Wilburys of the Colorado River

Thank you for the opportunity to submit comments on the Draft Environmental Impact Statement on Post-2026 Operational Guidelines and Strategies for Lake Powell and Lake Mead (DEIS). The undersigned colleagues with experience in Colorado River matters commend the Bureau of Reclamation and its consultants for the tremendous effort involved in preparing this environmental impact analysis. We are especially impressed by Reclamation’s effort to analyze policy alternatives within the context of the deep uncertainty about what future hydrology might be. Your work is novel in its effort to provide the public with a sense of what the performance, robustness, and vulnerability …

Comparative Computational Study Of Sulfur-Donor Additives For Stabilization Of Fapbi3 Perovskites, Ilnar Nurgaliev, Akbarxon Hamzayev, Murad Marasulov, Zavkiddin Julliev, Akhmad Oblakulov, Nigmat Ashurov

Turkish Journal of Chemistry

A multiscale computational investigation integrating density functional theory (DFT) and molecular dynamics (MD) simulations was conducted to elucidate the mechanisms through which sulfur-containing donor molecules stabilize the photoactive α-phase of formamidinium lead iodide (FAPbI3) perovskites. The binding energetics, charge-transfer behavior, and hydrogen-bonding interactions of thiourea (TU), thiosemicarbazide (TSC), thiocyanate (SCN–), and diethyldithiocarbamate (DTC) were systematically analyzed. DFT results revealed pronounced Pb–S coordination and multidentate hydrogenbonding, with binding energies following the trend SCN–>TSC>DTC>TU. Thermodynamic analysis demonstrated that these additives lower the Gibbs free energy difference, thereby stabilizing the black α-phase, with TSC, TU, and to a lesser extent …

Reconstructing Soil Conductivity Profiles From Electromagnetic Induction Data, Tyler Philip Casas

Theses and Dissertations

Construction of infrastructure in cold regions poses a significant challenge, particularly due to permafrost degradation, which can lead to significant structural damage and deterioration to facilities and transportation systems. In recent years, the issue of geophysical hazard detection has been instrumental in minimizing ground subsidence risks when con- structing infrastructure in cold regions. As a commonly used technology for geophysical applications, electromagnetic induction (EMI) can be used for (i) detecting ground subsurface layers and (ii) characterizing soil types via electrical conductivities. However, EMI technology tends to struggle with lower resolution at lower frequencies and shallow depth of penetration at higher …

Reliable Eigenspace Error Estimation Using Source Error Estimators, Jay Gopalakrishnan, Gabriel Pinochet-Soto

Mathematics and Statistics Faculty Publications and Presentations

We introduce a framework for repurposing error estimators for source problems to compute an estimator for the gap between eigenspaces and their discretizations. Of interest are eigenspaces of finite clusters of eigenvalues of unbounded nonselfadjoint linear operators with compact resolvent. Eigenspaces and eigenvalues of rational functions of such operators are studied as a first step. Under an assumption of convergence of resolvent approximations in the operator norm and an assumption on global reliability of source problem error estimators, we show that the gap in eigenspace approximations can be bounded by a globally reliable and computable error estimator. Also included are …