Digital Commons Network^™

Key Issues For Speed Safety Cameras: Results From A Semi-Structured Interview Of Agencies In The United States, Sophia Semensky, Miguel Figliozzi

Civil and Environmental Engineering Faculty Publications and Presentations

Speed safety cameras (SSCs) are a recognized countermeasure for targeting speeding-related crashes, injuries, and fatalities. SSCs have been introduced in various cities and are steadily expanding in use both in the U.S. and internationally. The extensive literature review of domestic and international documents and research papers reveals that the U.S. has been comparatively slow to adopt SSCs, with some other countries implementing these systems more extensively and earlier. Many local agencies are eager to learn SSC best practices and implementation lessons within the U.S. context. In this research, key issues related to SSC implementation are explored through semi-structured interviews with …

On Time-Series Analysis By Structured Matrix Decompositions With Applications To Signal Direction-Of-Arrival Estimation, Georgios Ierotheos Orfanidis

Electronic Theses and Dissertations 2020 - Present

Modern autonomous systems operating in highly dynamic, non-stationary environments require reliable inference from short, potentially corrupted time-series measurements, where conventional statistical methods relying on large-sample support and stationarity assumptions become fundamentally inapplicable. This dissertation develops a unified, model-free theoretical framework for time-series analysis, with a particular application to signal Direction-of-Arrival (DoA) estimation, grounded in structured matrix decompositions under both the L2-norm and L1-norm formulations.

We first approach the problem from a conventional viewpoint by carrying out standard matrix analysis directly on Hankel-structured representations of time-series data. In this context, we demonstrate that L1-norm decompositions of Hankel matrices offer strong resistance …

Critical Arteries: Prioritizing Economic Vitality And Emergency Access In Local Networks, Jeremy Hunter

Local Transportation Asset Management Conference

Standard Asset Management Plans (AMPs) often rely heavily on Pavement Condition Index, Paser ratings, and Bridge Condition Ratings to dictate repair schedules. However, a strictly condition-based approach may neglect the economic and safety criticality of specific routes. This presentation proposes a multi-variable evaluation framework for counties, cities, and towns. I will explore methods for overlaying industrial data—specifically for heavy-haul sectors like logging, masonry, and agriculture—to identify and fortify the "economic lifelines" that sustain local revenue. Furthermore, I will address the necessity of resiliency planning within the AMP. By integrating hydraulic data and flood-prone area mapping, agencies can ensure that routes …

Passenger Autonomy In Airport Systems: Integrating Accessibility, Spatial Design, And Technology To Improve Operational Efficiency, John Michael Schmitz

Honors Projects

Airports are among the most complex operational environments in modern transportation, requiring the coordination of passenger movement, security processes, and infrastructure under significant time constraints. Although there have been many advancements in aviation, most passengers continue to experience high levels of stress and confusion while trying to navigate airport terminals. This paper argues that operational efficiency in airport systems is achieved by increasing  passenger autonomy through the utilization of accessible design, spatial legibility, and new emerging technologies. This paper takes note of some foundational theories that drove urban design and cognitive psychology, including Lynch’s concept of imageability, Passini’s theory of …

Open Multi-Agent Systems: The Free-Range-Zoo Framework And Moasei Competition, Ceferino J. Patino Iv

School of Computing: Dissertations, Theses, and Student Research

The field of multi-agent reinforcement learning (MARL) has made significant strides in addressing sequential decision-making problems under uncertainty. However, traditional MARL frameworks assume closed-world settings with fixed agent sets, static task distributions, and unchanging environment dynamics. This thesis presents two complementary contributions that advance the state of open-world multi-agent systems research: (1) the free-range-zoo framework, an open-source environment suite for MARL in open environments featuring dynamic agent populations, evolving task sets, and changing operational frames; and (2) the MOASEI Competition, an international benchmarking event that leverages free-range-zoo to evaluate how artificial agents handle openness in complex, partially observable domains. The …

Integrated Approaches For Valorization Of Chicken Feather Waste: From Extraction To Application Of Keratin-Based Bioproducts, Muhammad Ehtasham Akram

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

This dissertation explores the optimization of chicken feathers keratin hydrolysate (CFKH) production using alkaline hydrolysis (AH) and sub-critical water hydrolysis (SCWH), focusing on acquiring high keratin yield in the hydrolysate. Then, CFKH along with other locally sourced materials, was used to develop bioplastics with regenerative agriculture properties (BioWRAP), with the ultimate objective of applying it as a soil mulch to suppress weeds in agricultural crops. This work also addresses challenges in process design and industrial scale-up of BioWRAP. Chapter 1 is a literature review that introduces terms like biobased, biodegradable, and bioplastics, materials and methods used to make bioplastics, and …

A Machine Learning Framework For Ddos Attack Detection In Sdn-Enabled Mobile Wireless Networks, Ishita Sharma, Satyam Agarwai, Shashi Shekhar Jha, Sumit Chakravarty

Faculty Articles

Attacks on network components and devices pose a significant threat to service continuity, necessitating robust detection mechanisms. This paper presents a Distributed Denial of Service (DDoS) attack detection framework tailored for heterogeneous mobile wireless networks within a Software-Defined Networking architecture. A two-tier model is proposed: localized attack detection at access points (APs) using a Multi-Layer Perceptron (MLP) classifier, and centralized detection under mobility at the controller using a Long Short-Term Memory (LSTM) model. The system incorporates novel traffic features such as flow count, speed of source IP, source and destination IP address entropy, proportion of bidirectional flows, and handover frequency, …

Sdn Controller For Distributed Quantum Computing, Firas Selmi

Masters Theses

Quantum networks promise transformative capabilities for computation [1], but current hardware remains limited; state-of-the-art quantum processors still operate with only a few hundred qubits [2], far below the scale required for practical applications. This limitation motivates the use of Distributed Quantum Computing (DQC), where computation is performed across multiple interconnected nodes. However, efficient DQC requires global network awareness and orchestration, a role analogous to Software-Defined Networking (SDN) in classical systems. In this work, we investigate the impact of SDN-inspired control logic on quantum networks by executing a scaled distributed implementation of Shor’s algorithm to factor N = 15 over a …

2026 - The Thirtieth Annual Symposium Of Student Scholars

Symposium of Student Scholars Program Books

The full program book from the 30th Annual Symposium of Student Scholars, held on April 22-24, 2026. Includes abstracts from the presentations and posters.

Joint Inversion For Heat Transfer Parameters Of Porous Media With Geothermal-Seepage Coupling Based On A Physics-Informed Neural Network, Zhang Xiao, Yang Duoxing, Cong Lin, Cheng Yimei, Zhang Lianzhong

Coal Geology & Exploration

Background The thermal diffusivity and vertical seepage velocity of porous media are critical to characterizing the mechanisms underlying subsurface heat transfer. These two parameters are typically determined through inversion using temperature data since they are difficult to measure directly. Fiber-optic distributed temperature sensing (FO-DTS) can yield high-resolution, continuous temperature profiles, providing data for parameter identification. However, conventional numerical inversion suffers from limitations such as mesh dependency and sensitivity to initial values when used to process continuous monitoring data. Methods This study proposed a joint geothermal parameter inversion method that integrates FO-DTS data with a physics-informed neural network (PINN). Specifically, the …

Srcis: Human-Robot Collaboration In Urban Search And Rescue Operations, Younghoon Cho, Yavanni Ensley, Jaylin Ollivierre

Electrical Engineering and Computer Science Student Publications

Search and Rescue Coordinated Intelligence System (SRCIS) is a human-agent system designed to:

• Improve the effectiveness of real-time search and rescue operations.
• Integrate human judgement with robotic autonomy and capabilities
• Enable coordinated action and adaptive response in dynamic environments.

Program And Proceedings, The Nebraska Academy Of Sciences 1880–2026: 146th Anniversary Year, One Hundred-Thirty-Sixth Annual Meeting

Nebraska Academy of Sciences: Programs and Proceedings

Program and abstracts of the proceedings for the Nebraska Academy of Sciences 136th annual meeting, 2025

Sessions

Aeronautics and Space Science

Anthropology

Biological and Medical Sciences

Earth Sciences

Ecology, Sustainability, and Environmental Science

Physics and Engineering

General Poster Session

2026 Maiben Lecture: Shifting Extremes: Understanding Nebraska’s Changing Climate and Preparing for What Lies Ahead, Deborah Bathke

2026 Friends of Science Awards: Julie Shaffer and Irina Filina

2026 C. Bertrand and Marian Othmer Schultz Colleaguate Schoalrship Award: Piper Ryschon and Bryce Reeson

In Memoriam: Paul Royster

Aircraft Violations Related To Temporary Flight Restrictions & Operations​ From 2017 To 2021, Mark Lipp, Tea Yankova, Hudson Schnier, Vivek Sharm, Brooke Wheeler

Aeronautics Student Publications

Temporary Flight Restrictions (TFRs) are temporary airspace limitations issued to protect sensitive operations, events, and public safety within the United States and Territories National Airspace System (NAS). Despite widespread dissemination of TFR information, violations continue to occur. This study examined the relationship between the monthly number of active TFRs, total aircraft operations, and TFR violations from 2017 to 2021. A quantitative, non-experimental correlational design was used to analyze publicly available Federal Aviation Administration datasets, including TFR records and incident reports from the Aviation Safety Reporting System (ASRS). Descriptive statistics and Pearson product–moment correlations were calculated using 59 monthly observations. Results …

Analysis Toolkit For Potential Lunar Landing Zones, Jacobo Matallana

Morehead State Theses and Dissertations

A thesis presented to the faculty of the College of Science and Engineering Morehead State University in partial fulfillment of the requirements for the Degree Master of Science by Jacobo Matallana on April 24, 2026.

Automatic Determination Of Mechanical Properties From Molecular Dynamic Stress-Strain Curves Using Regression Fringe Response, Josh Kemppainen, Tristan Muzzy, Travis Wavrunek, Gregory Odegard

Michigan Tech Publications

Molecular dynamics (MD) simulations have become a powerful tool for studying polymers, designing new materials, and composites. Mechanical behavior in MD simulations are typically evaluated through stress-strain curves. However, MD-generated stress-strain curves are often obscured by thermal noise from atomic vibrations. This noise complicates the reliable derivation of mechanical properties and hinders objective and reproducible analysis. The noise in the stress-strain curves has been removed by using a Butterworth low-pass filter, enabling clearer interpretation of stress-strain data. Building on this, a novel analysis framework the Regression Fringe Response (RFR) is introduced to systematically determine the Young's modulus, yield strength, and …

Breach Buddy: Compact Ugv, Zane Hicks, William Stanish, Kyle Gillenwater, Luis Garbanzos

Electrical Engineering and Computer Science Student Publications

This project addresses the challenges of obtaining real-time intelligence in hazardous environments, where personnel face significant risks and many existing systems face significant drawbacks.

Remotely Controlled Car Via Lte/Wi-Fi, Nicholas Shenk, Christian Prieto, Joseph Digafe, Donoven Nicolas

Electrical Engineering and Computer Science Student Publications

PROBLEM 

• Professionals must enter dangerous environments just to gather information 
• Existing robotic systems are expensive and complex
• Communication links are often unreliable or high-latency
• Limited real-time feedback reduces decision-making ability

Aria – Ai Robot Intelligence Assistant, Balakrishna Reddy Channa Reddy

Pacific Innovation and Entrepreneurship Summit (PIES)

ARIA (AI Robot Intelligence Assistant) is an autonomous AI-powered robot designed to improve human-robot interaction and autonomous navigation. The system integrates advanced capabilities including face recognition, voice commands, AI responses, obstacle avoidance, phone tracking via Bluetooth Low Energy (BLE), and autonomous navigation.

The architecture consists of multiple layers: sensors (camera, microphone, and ultrasonic), a Raspberry Pi serving as the AI brain, an Arduino for motor control, motors and wheels for movement, and phone connectivity via Bluetooth (BLE). This layered design enables real-time perception, decision-making, and physical interaction with the environment.

Future development directions include real-time learning, edge AI optimization, and …

Llm For Clinical Named Entity Recognition: A Study On Rag With Pubmed And Umls, Apoorv Tripathi

Electronic Theses and Dissertations 2020 - Present

The first step of biomedical NLP is recognizing clinical named entities, which consist of identifying and categorizing a variety of clinical entities such as diseases, symptoms, genetics, diagnostic tests, procedures, etc. from a body of unstructured clinical text. This study presents a PubMed and UMLS based Retrieval Augmented Generation framework which improves the performance of the Large Language Models to identify clinical entities by providing context. In particular, the framework consists of a two-stage pipeline, where candidate tokens are identified from initial LLM-based classification and refined with retrieved context from either PubMed or UMLS. The proposed framework is assessed across …

Improving Electrochemical Measurements And Informing Future Reprocessing Of Advanced Nuclear Fuels, J. Marvin Torrie

Theses and Dissertations

This research promotes the application of molten salts through developing analytical tools in electroanalytical studies and in the data analysis of vapor pressure data. The electrochemical community lacks standardized, stable, and repeatable reference electrode designs which threaten the reliability of measured thermophysical properties and repeatability of published studies. This work confirms the application of a saturated reference electrode design for molten chlorides systems that outperforms traditional Ag+/Ag reference electrodes. In the field of thermal physical property measurements, vapor pressure data for chloride salts are diffused throughout the literature, uncertainty quantification is absent or limited, and all previous studies rely on …

Uscis-Grounded Ai: Preventing Hallucinations In Immigration Legal Services, Hephzibah Igwe

ONU Student Research Colloquium

Artificial intelligence chatbots increasingly provide legal information to consumers, but AI "hallucinations" (confidently stated but incorrect responses) pose serious risks in immigration law. Incorrect information about USCIS forms, fees, processing times, or filing procedures can result in visa denials, deportation proceedings, or permanent bars to entry.

This research presents a novel "source-grounded AI" system that eliminates hallucinations in immigration legal information. Rather than relying solely on large language models (LLMs) trained on general internet data, the system uses USCIS.gov as the primary source of truth for all operational data including current forms, fees, processing times, filing addresses, and policy updates. …

A Trojan Attack On Tdma Synchronization In Energy-Harvesting Wireless Networks, Ethan Berei

ONU Student Research Colloquium

This paper investigates a Trojan attack targeting the time-division multiple access (TDMA) synchronization mechanism in single-hop energy-harvesting wireless networks. The attack compromises a single node, which subtly skews its transmission timing to operate outside its assigned time slot, causing localized transmission overlaps and triggering repeated network-wide resynchronization events. This behavior shortens the synchronization interval, significantly increases control-plane traffic, and leads to higher energy consumption and delay in energy-constrained networks. The attack is modeled within a finite state machine (FSM) framework and experimentally evaluated under varying energy-harvesting conditions. Experimental results show that the number of synchronization events can increase by up …

Analyzing Proposed Unmanned Aerial System Regulations And Their Impact On Manned Aircraft Operations, Kordell Irish

Honors Theses

The purpose of the thesis is to investigate the current state of the proposed Part 108 regulations and to find possible solutions to the problems faced by Part 108 implementation, with a focus on the interactions between manned and unmanned aircraft. The thesis covers upcoming changes to federal aviation regulations by the Federal Aviation Administration (FAA) regarding unmanned aircraft systems and their integration into the national airspace system. Currently, most unmanned aircraft systems (UAS) are regulated under Part 107 of the federal aviation regulation. A brief literature review will provide a background and timeline in the lead-up to the Part …

Integration Of Bayesian Networks And Neural Networks For High-Dimensional Data Analysis, Cooper Schmer

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

High-dimensional biomedical datasets, such as omics data, present significant challenges for predictive modeling due to noise, redundancy, and computational complexity. This thesis proposes a hybrid framework that integrates Bayesian Networks (BNs) and Artificial Neural Networks (NNs) to improve classification performance of such data sets while reducing input dimensionality. Central to this work is a novel feature selection method based on d-separation, a structural property of Bayesian networks that encodes conditional independence relationships.

The proposed approach introduces a count-based d-separation metric to quantify the relevance of variables to a target outcome, along with a thresholding scheme to balance feature selection robustness …

Space Degradable Pyrotechnic Fastener, Jonathan Baker

Honors Theses

Space debris has become a critical threat to orbital operations, with over 30,000 tracked objects currently circling Earth. Traditional separation charges used in satellite launches contribute significantly to this problem by generating unpredictable shrapnel that can remain in orbit for decades or centuries. This project proposes the design and development of a spacedegradable explosive bolt that exploits the harsh space environment to accelerate material breakdown after use.

The design utilizes advanced polymer materials selected for their susceptibility to degradation from UV radiation — an environmental factor abundant in Low Earth Orbit (LEO). Our prototype will be engineered to meet industry-standard …

Development Of A Grease Resistant Barrier For Food Industry Applications, Mira Marino

Honors Theses

The objective of this project is to develop a grease-resistant barrier coating for paper-based food packaging and to evaluate the economic feasibility of implementing this coating system at an industrial scale. As demand increases, this project focuses on identifying an optimal pigment and binder combination that enhances barrier performance while maintaining cost-effectiveness. The primary goal was to maximize grease and moisture resistance of coated paper while ensuring the process is economically viable and compatible with existing paper mill operations.

The final plant design consists of one continuous stirred tank reactor (CSTR) and three centrifugal pumps integrated into an existing paper …

Enhancing Lane Detection In Autonomous Vehicles Using Data Augmentation For Adverse Environmental Conditions, Rutvikkumar Dave

Electronic Theses and Dissertations 2020 - Present

To make sure that self-driving and connected automobile technologies are safe and work well, it’s really important that they can correctly identify lanes. But lane detection Algorithms typically have a hard time working well when the weather is bad, such when it rains, fogs, or goes too fast. The circumstances cause visual distortions that make existing computer vision systems less reliable, which makes it harder requires autonomous navigation systems to work well. This paper introduces a comprehensive lane detection system that integrates synthetic Weather-informed data augmentation combined with a Weather-aware Temporal Lane Detection Network (WTLDNet) to make it easier for …

Opportunistic Breast Cancer Detection With Photon-Counting Ct: A Quantitative Pilot Study Of Reconstruction-Dependent Image Quality, Graham Hannon

McKelvey School of Engineering Graduate Student Theses & Dissertations

Photon-counting computed tomography (PCCT) has emerged as a promising imaging modality with improved spatial resolution and contrast capabilities compared to conventional energy-integrating detector systems. These advantages present an opportunity for opportunistic detection of breast cancer in routine thoracic CT scans that include breast tissue. In this pilot study, we evaluated the feasibility of opportunistic breast cancer detection using clinical PCCT data through quantitative image analysis. Two patients with confirmed breast malignancies and corresponding thoracic PCCT scans were retrospectively analyzed. Dedicated reconstructions were performed with varying convolution kernels, field-of-view sizes, and virtual monoenergetic spectra. Image quality was assessed using signal-to-noise ratio …

Swosu Research And Scholarly Activity Fair 2026, Swosu Office Of Sponsored Programs

SWOSU Research and Scholarly Activity Fair Programs

On behalf of the University Research and Scholarly Activity Committee (URSAC) and the Office of Sponsored Programs (OSP) at Southwestern Oklahoma State University, we are pleased to welcome you to the Thirty-Fourth SWOSU Research and Scholarly Activity Fair.

Intelligent Competition Platform And Mode Driven By Cloud-Native Simulation, Long Qin, Hesong Huang, Lujia Yin, Chuan Ai, Qi Zhang, Xinmeng Li

Journal of System Simulation

Abstract: To solve the problems faced by the adversarial competition mode of agents, including difficult development and deployment, low resource utilization, poor reusability, and difficulty in accessing reinforcement learning algorithms, a new agent simulation training platform was designed. The software components of the competition platform were decoupled based on cloud-native technology; a high-performance simulation engine for the competition environment was proposed; a new method of an embedded reinforcement learning model for an intelligent control terminal was designed, with multiple online and offline policy-based reinforcement learning algorithms set. The experiment demonstrates that the development and deployment of the system is efficient, …