Digital Commons Network^™

Secret Key Generation Based On The Physical Layer Characteristics For Iot Networks, Abdullah Dakhlallah Alshamdayn

Doctoral Dissertations

The rapid expansion of low-resource devices, coupled with advances in telecommunications, has significantly increased the number of connected devices and enabled the development of affordable, energy-efficient, portable, and high-performance sensors for diverse applications. However, this convenience comes with security and privacy concerns related to the reliability of hardware, software, and communication infrastructure. The extensive interconnectivity of limited-resource devices and the transmission of large data volumes pose significant security challenges in wireless networks. The future wireless technologies, such as 5G, will enable the transfer of critical data, including personal, financial, military, and industrial information, necessitating secure communication in wireless networks. Generally, …

Towards Robust Autonomous Systems: Handling Multi-Modal Uncertainties In Gps-Denied Environments, Vivya Kalidindi

Doctoral Dissertations

This dissertation focuses on designing a robust and uncertainty-aware framework for autonomous systems operating in GPS-denied environments, such as indoor infrastructures, underground tunnels, and lunar surfaces. The proposed framework addresses the challenges posed by multi-modal uncertainties, including sensor noise, distributional shifts under adverse conditions, and conflicting decision-making preferences. These challenges compromise the reliability and adaptability of autonomous platforms. To overcome these challenges, the proposed framework adopts a layered architecture that integrates advanced methodologies across the sensing, perception, and decision-making layers. At the sensing layer, an Edge-Kalman Filter combined with a density ratio-based update mechanism is employed to reduce aleatoric uncertainty …

Inverse Design For Generating Initial Conditions In Scientific Simulations, Leslie Horace, Christin Whitton, Vanessa Job, William Jones, Nathan A. Debardeleben

Computing Sciences

We propose a conditional normalizing flow (CNF) surrogate model to solve generative, many-to-one inverse problems in scientific simulations governed by partial differential equations (PDEs) with time-evolving interactions between heterogeneous materials. We present two case studies: electrostatic potential and heat diffusion, which serve as proxy simulations for generating diverse sets of initial conditions that can reproduce an observed output state (transient or steady). Finally, we provide a comprehensive overview of the synthetic datasets, the model specification, each stage of the experimental workflow, evaluation of training performance, and uncertainty quantification for the generated samples.

Connected-Component Labeling Using Hls For High-Energy Particle Physics Instruments, Nick Song, Marion Sudvarg, Roger Chamberlain

Computer Science Faculty Research & Creative Works

Many instruments used in high-energy particle physics observations, e.g., gamma-ray telescopes, use FPGAs for front-end signal processing of raw sensor data. The use of high-level synthesis (HLS) to express the signal processing algorithms has the potential to significantly reduce development time for new instruments of this type. We describe our experience with one of the computational stages in the signal processing pipeline, island detection, exploring its implementation across multiple configurations: 1D versus 2D islands, and 4-way versus 8-way connected-component labeling (CCL) in the 2D configuration. We report resource usage and performance for both configurations of 2D island detection, including the …

Modular Architecture For High-Performance And Low Overhead Data Transfers, Rasman Mubtasim Swargo, Engin Arslan, Md Arifuzzaman

Computer Science Faculty Research & Creative Works

High-performance applications necessitate rapid and dependable transfer of massive datasets across geographically dispersed locations. Traditional file transfer tools often suffer from resource underutilization and instability due to fixed configurations or monolithic optimization methods. We propose AutoMDT, a novel Modular Data Transfer Architecture, to address these issues by employing a deep reinforcement learning agent to simultaneously optimize concurrency levels for read, network, and write operations. This solution incorporates a lightweight network-system simulator, enabling offline training of a Proximal Policy Optimization (PPO) agent in approximately 45 minutes on average, thereby overcoming the impracticality of lengthy online training in production networks. AutoMDT's modular …

Modeling And Optimizing Real-Time Telescope Interaction For Multi-Wavelength Observation Of Gamma-Ray Bursts, Ye Htet, Marion Sudvarg, Honghao Yang, Jeremy Buhler, Roger Chamberlain, James Buckley

Computer Science Faculty Research & Creative Works

Multi-wavelength observation of gamma-ray bursts (GRBs) requires real-time interaction among multiple telescopes. A gamma-ray telescope detects and localizes a GRB in the sky and must then communicate with an optical telescope to direct the latter toward the GRB as quickly as possible. We previously developed software for ADAPT, a suborbital gamma-ray telescope, to localize GRBs in real time, on a timescale shorter than that of the GRB itself. This work therefore studies progressive localization, in which ADAPT computes a series of increasingly accurate location estimates during a GRB to enable a partner instrument to more rapidly find it. We describe …

Quantum Readiness In Cybersecurity Education: A Framework For Preparing The Next Generation In The Post-Quantum Era, George Antoniou

Faculty and Staff Publications & Presentations

The rapid advancement of quantum computing represents both a revolutionary opportunity and an existential threat to contemporary cybersecurity infrastructure. While quantum computers promise unprecedented computational capabilities, they simultaneously pose a critical risk to current cryptographic protocols that protect sensitive data, financial systems, and national security frameworks. Post-quantum cryptography (PQC) standards, recently formalized by NIST in 2024, provide a roadmap for quantum-resistant encryption. However, a significant gap exists between technological advancement and educational preparedness, with most cybersecurity curricula failing to adequately prepare students for the quantum era. This paper addresses the urgent need for comprehensive quantum readiness in cybersecurity education across …

Playa Sediment Bioaccessibility Presents Previously Unknown Risks To Population Health In A Water-Stressed Region Undergoing Lake Desiccation: Salton Sea, Usa, Jordan Jaeger

Theses

Globally, drylands are expanding, and endorheic basins are in rapid decline. Saline lake desiccation exposes lakebed playa likely to become dust, posing potential health risks to surrounding communities. Studies investigating health risks from playa dust exposure often focus on health effects associated with the presence of airborne particulates, but fewer studies investigate health risks associated with dust source chemistry. This study examines dust source particle composition in a desiccating inland sea in a water-stressed agricultural region of California. Specifically, chemical bioaccessibility of bound trace elements uncovers risks to children, adults, and agricultural worker health. Arsenic exposure via sediment ingestion poses …

Source Apportionment And Potential Health Risks Of Trace Metals In A Contaminated Urban River In New York/New Jersey Harbor System, Md Shahnul Islam, Sana Mirza, Huan Feng, Tapos Kumar Chakraborty, Yu Qian, Shinjae Yoo

Department of Earth and Environmental Studies Faculty Scholarship and Creative Works

The Lower Passaic River (LPR), located within the New York/New Jersey Harbor Estuarine System, has experienced long-term industrial activities, resulting in elevated concentrations of trace metals in sediment and water. This study aims to assess the bioaccumulation behavior, potential human health risks, and sources of copper (Cu), lead (Pb), and mercury (Hg) in the LPR. Trace metal concentrations were measured in water, sediment, and seven edible aquatic species. Data were analyzed using statistical approaches, and evaluated by bioaccumulation factors (BAFs) and human health risk assessments based on U.S. Environmental Protection Agency (USEPA) guidelines. Results showed that Hg exhibited the highest …

Re: Approval Letter For The Butte Priority Soils Operable Unit (Bpsou) Draft Final Insufficiently Reclaimed Sites Remedial Action Work Plan (Rawp): Bres No. 104 – (Colorado Dump) North Slope (Dated October 17, 2025), Molly Roby

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Final Butte Mine Waste Repository Geotechnical Investigation Work Plan, Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

A Scalable Cybersecurity Model For Academic Makerspaces, William Faircloth

Cybersecurity Undergraduate Research Showcase

Academic makerspaces have become integral hubs of innovation on university campuses, providing students with access to industrial-grade operational technology (OT) such as 3D printers and CNC machines. However, the security posture for these spaces has overwhelmingly focused on physical safety, creating a significant cybersecurity gap. This oversight leaves networked OT vulnerable to cyberattacks, which threaten student intellectual property, expensive equipment, and the integrity of the broader institutional network. This research addresses this critical vulnerability by developing and implementing a secure and scalable cybersecurity model at the Old Dominion University Computer Science Makerspace, founded on two core principles: robust network segmentation …

Deconstructing Tycoon 2fa: A Static Analysis Approach To Threat Intelligence And Automated Defense, Daniel A. Austin Jr

Cybersecurity Undergraduate Research Showcase

It's gotten much easier to be a cybercriminal. We're seeing a boom in "Phishing-as-a-Service" (PaaS) platforms, which sell advanced phishing attacks as a ready-to-use product. This means almost anyone can now get the tools to launch sophisticated attacks, even if they don't have a lot of technical skill.

This research dives into one of the most prominent threats, the Tycoon 2FA phishing kit. This kit is dangerous because it's designed to bypass Multi-Factor Authentication (MFA) using what is known as an Adversary-in-the-Middle (AiTM) attack.

This paper covers how I built and tested a set of Python-based tools to perform "static …

Re: Approval Letter For The Butte Priority Soils Operable Unit (Bpsou) Unreclaimed Sites Ur-46 Temporary Best Management Practices Work Plan (Dated October 17, 2025), Molly Roby

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

2025 Field Evaluation Of Reclaimed Sites Summary Report, Butte-Silver Bow Department Of Reclamation And Environmental Services

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Chronosort: Revealing Hidden Dynamics In Alphafold3 Structure Predictions, Matthew J. Argyle, William P. Heaps, Corbyn Kubalek, Spencer Gardiner, Bradley C. Bundy, Dennis Della Corte

Faculty Publications

Protein function emerges from dynamic conformational changes, yet structure prediction methods provide only static snapshots. While AlphaFold3 (AF3) predicts protein structures, the potential for extracting dynamic information from its ensemble predictions has remained underexplored. Here, we demonstrate that AF3 structural ensembles contain substantial dynamic information that correlates remarkably well with molecular dynamics simulations (MD). We developed ChronoSort, a novel algorithm that organizes static structure predictions into temporally coherent trajectories by minimizing structural differences between neighboring frames. Through systematic analysis of four diverse protein targets, we show that root-mean-square fluctuations derived from AF3 ensembles can correlate strongly with those from MD …

Synthesis And Characterization Of Highly Stable Fisetin Encapsulated In Lecithin-Chitosan-Savie Nanoparticles, Hart Monyatovsky, Travis Anderson, Jay Nadeau

Physics Faculty Publications and Presentations

Senolytic drugs, of the class of polyphenolic flavonoids, have attracted attention for a wide variety of medical applications for which they may be delivered topically, orally, and/or intravenously. However, due to their hydrophobicity and sensitivity to light and oxygen, they have been proven difficult to package and deliver. Here we report a synthesis method for encapsulated fisetin using lecithin-chitosan ionic gelation with a recently reported surfactant that is an alternative to polyethylene glycol (PEG)-containing compounds. Called “Savie” for a contraction of its components, sarcosine-Vitamin E, this surfactant allows for production of sub-100 nm nanoparticles that are bench stable for at …

Spatially Mapped Statewide Estimated Potential Evapotranspiration Using An Efficient Surface Interpolation Method: A Case Study Of South Carolina, Sudhanshu S. Panda, Devendra M. Amatya, Ka Kit Liu, Augustine Muwamba, Timothy J. Callahan

Journal of South Carolina Water Resources

Potential evapotranspiration (PET) exhibits substantial spatial and temporal variability across large landscapes, necessitating site-specific estimation for accurate environmental and water resource assessments. However, obtaining PET or ET data for specific locations across an entire state remains challenging due to the limited number of weather stations and associated environmental datasets. This study aimed to develop an automated geospatial modeling framework to map PET distribution across South Carolina, USA, using PET estimated by the temperature-based Hargreaves–Samani (H–S) method with daily weather data from 59 NOAA stations. Because the accuracy of spatial interpolation depends on both the target variable and the desired spatial …

Trustworthy Federated Learning Framework For Secure, Efficient, And Quality-Aware Distributed Ai, Asadullah Tariq

Thesis/ Dissertation Defenses

Federated Learning (FL) emerged as a significant advancement in the field of Artificial Intelligence (AI), enabling collaborative model training across distributed devices while maintaining data privacy. As the importance of FL and its application in various areas increased, addressing trustworthiness issues in its various aspects became crucial. In FL process, not all client data may be relevant to the learning objective and incorporating updates from irrelevant data can harm the model's performance. The selection of training samples significantly impacts model performance, as datasets with errors, skewed distributions, or low diversity can lead to inaccurate and unstable models. To address these …

Direct Arylation Of 1,10-Phenanthroline Using An Organomagnesium Reagent, Nathanael A. Hirscher, Julian B. O. Van Esselstine, Bryan C. Yapo, Nadia Asif Masood, Erika L. Vandooijeweert

Department of Chemistry and Biochemistry Faculty Scholarship and Creative Works

Substituted derivatives of 1,10-phenanthroline are important ligands in inorganic chemistry. Such ligands are often synthesized by nucleophilic addition of aryl- or alkyllithium reagents to the carbon positions adjacent to the phenanthroline nitrogens. The typical reaction sequence (organolithium addition, protonolysis, oxidation, and rearomatization) has been widely used and seldom improved. We sought to replace lithium-based reagents with safer reagents based on magnesium (i.e., Grignards). The effective use of an arylmagnesium reagent in the production of a phenanthroline ligand derivative is now disclosed. Additionally, our newly reported method avoids the use of a heterogeneous oxidant (MnO2) which the traditional method employed in …

Fall 2024 Computer Programming And Engineering Self-Efficacy Survey Data, Mary Benjamin

Michigan Tech Research Data

This dataset was collected as part of a research study examining the impact of automated code critiquers on students’ programming and engineering self-efficacy in first-year engineering courses. The study involved pre- and post-surveys administered to students enrolled in ENG1101: Introduction to Engineering during Fall 2024 at Michigan Technological University. The research aims to understand how exposure to automated feedback tools, such as WebTA, influences confidence, persistence, and perceived competencies.

2025 November 13 - Tennessee Weekly Drought Summary, Tennessee Climate Office, East Tennessee State University

Tennessee Climate Office Weekly Drought Summaries

No abstract provided.

Contextual Bandits For Non-Stationary Multi-Agent Environments Using Assumptions-Alignment Tracking, Ethan Pedersen

Theses and Dissertations

A common challenge in AI is that of an agent possessing N distinct behaviors, each of which works effectively in certain tasks and circumstances, to choose from at any particular time. A popular approach to learn optimal selections in such contexts is found in bandit algorithms. However, traditional bandits often (1) assume the environment is stationary, (2) focus on asymptotic performance, and (3) do not incorporate available external information about the environment. While contextual bandits that do consider external information are designed to combat one of these challenges, the others remain problematic in realistic domains. This is especially true of …

Cv: Mathias Plass (Cybersecurity), Mathias Plass

ECaMS Department Faculty Curricula Vitae

No abstract provided.

Reducing Data Requirements In Polymer Science: Deep Neural Networks For Predicting Surface Tension Of Copolymer Compatibilizers, Md Mushfiqul Islam

USF Tampa Graduate Theses and Dissertations

In polymer chemistry, compatibilization involves adding a substance often a block or graft copolymerto stabilize polymer blends that would otherwise not mix well, leading to rough structures and weak me- chanical properties. Compatibilizers improve miscibility and reduce interfacial tension, which is critical for applications such as mixed-waste polymer recycling. Sequence-controlled polymers offer unique potential by combining the tunable chemistry of synthetic polymers with the precise, function-driven design of biological macromolecules, but unlike proteins, they lack large, evolution-shaped datasets to guide discovery. This research develops a deep learning framework to predict the surface tension of sequence-controlled copolymer compatibilizers across varying concentrations. …

Speculative Automated Refactoring Of Imperative Deep Learning Programs To Graph Execution, Raffi T. Khatchadourian Ph.D., Tatiana Castro Vélez, Mehdi Bagherzadeh, Nan Jia, Anita Raja

Publications and Research

Efficiency is essential to support ever-growing datasets, especially for Deep Learning (DL) systems. DL frameworks have traditionally embraced deferred execution-style DL code---supporting symbolic, graph-based Deep Neural Network (DNN) computation. While scalable, such development is error-prone, non-intuitive, and difficult to debug. Consequently, more natural, imperative DL frameworks encouraging eager execution have emerged but at the expense of run-time performance. Though hybrid approaches aim for the "best of both worlds," using them effectively requires subtle considerations. Our key insight is that, while DL programs typically execute sequentially, hybridizing imperative DL code resembles parallelizing sequential code in traditional systems. Inspired by this, we …

Comparing Machine Learning, Deep Learning, And Reinforcement Learning Performance In Culex Pipiens Predictive Modeling, Wei Yin, Sanad H. Ragab, Michael G. Tyshenko, Teresa Patricia Feria-Arroyo, Tamer Oraby

School of Mathematical & Statistical Sciences Faculty Publications

Several machine learning (ML) and deep learning (DL) methods have been used to predict the presence of species in classification problems. Another set of methods, called reinforcement learning (RL), has been used in training agents to perform various tasks, but not in predicting species distribution. Culex pipiens (Diptera: Culicidae), commonly known as the common house mosquito, is a globally distributed species prevalent in temperate and subtropical regions. They serve as a primary vector for West Nile Virus (WNV), a mosquito-borne pathogen that affects humans and other animals. The study objective is to compare the performance of logistic regression, random forest …

Retaining The Herd: Habitat Restoration Efforts Increase Patch Retention Times In Mule Deer, Jack Rasmussen

Browse all Datasets

Across much of their range, and the State of Utah, mule deer (Odocoileus hemionus) populations have been declining. Declines in herd numbers can be attributed to alterations to historic habitats through the introduction of exotic annual grasses and the encroachment of pinyon-juniper forests on sagebrush ecosystems, changing resource availability. To combat habitat alterations, the Utah Department of Natural Resources has implemented various habitat restoration treatments through the Watershed Restoration Initiative , broadly to remove exotic species and decrease pinyon juniper woodland encroachment. We investigated how mule deer patch retention times differ on eight different habitat restoration treatment types during summer …

Thermodynamic Feasibility Of Nanoenergetic Systems Based On Metal Oxynitrides, Manoj Chhetri, Mkhitar A. Hobosyan, Maxim Zyskin, Karen S. Martirosyan

Physics & Astronomy Faculty Publications

A comprehensive computational study was conducted to identify and evaluate nano-energetic systems utilizing aluminum (Al) as the fuel and a range of metal oxynitrides as oxidizers. The primary objective was to determine combinations that yield highly exothermic reactions, specifically those that maximize gas generation, adiabatic combustion temperature, and energy release. These parameters are critical for optimizing the performance of energetic materials in applications such as controlled energy delivery, propulsion, and pyrotechnics. By systematically analyzing thermodynamic properties including the heat of formation, enthalpy of reaction, Gibbs free energy, and equilibrium reaction products across a range of Al/oxynitride stoichiometries, the study aimed …

Latent Classification Of Time-Dependent Transition Rates In Longitudinal Binary Outcome Data, Joonha Chang, Wenyaw Chan

School of Public Health Faculty Publications

Continuous-time Markov chain (CTMC) models and latent classification methods are commonly used to analyze longitudinal categorical outcomes in medical research. While CTMC models are popular for their simplicity and effectiveness, their assumption of constant transition rates presents limitations in capturing dynamic behaviors. To address this, non-homogeneous continuous-time Markov chains (NH-CTMCs) have been developed, incorporating time-varying transition rates to enhance model flexibility. In this study, we leverage closed-form transition probabilities for a fully ergodic two-state NH-CTMC model and propose a latent class clustering approach to identify heterogeneous transition rate patterns within the population. We emphasize the potential advantages of these models …