2024-2025 Annual Security And Fire Report – Weatherford Campus, Southwestern Oklahoma State University

ANNUAL SECURITY & FIRE REPORTS

This is the 2024-2025 Annual Security and Fire Report of the Weatherford Campus by Southwestern Oklahoma State University.

2024-2025 Annual Security And Fire Report – Sayre Campus, Southwestern Oklahoma State University

ANNUAL SECURITY & FIRE REPORTS

This is the 2024-2025 Annual Security and Fire Report of the Sayre Campus by Southwestern Oklahoma State University.

Development And Deployment Of In Situ Fiber Optic Distributed Temperature Sensing Systems And Data Processing Workflows For Oceanographic Observatories In Coastal And Deep-Sea Environments, Johann Sebastian Becker

Open Access Dissertations

Over the last two decades, fiber optic distributed temperature sensing (DTS) has emerged as a transformative technology capable of providing unprecedented spatiotemporal resolution observations of temperature in a broad range of environmental sensing, industrial, and civil engineering monitoring applications. DTS uses a single interrogator unit to measure temperature continuously along an optical fiber, effectively turning the fiber into the equivalent of an array of temperature probes that can be interrogated synoptically. Modern commercially-available DTS systems are capable of continuous sensing with sampling frequencies up 5 sec, spatial resolution of 0.25 m, and temperature precision up to 0.01 °C) observations, on …

Democratizing Deep-Sea Technology Development Using Rapid Prototyping Techniques, Breanna Motsenbocker

Open Access Dissertations

Deep-sea research and exploration have consistently been inaccessible to a broader oceanographic community due to the lack of affordable technology and resources that are required for this type of work. Traditionally, to investigate the deep sea, large research vessels are required to operate the expensive and massive equipment that can withstand the intense pressure and harsh environment beyond 200 meters of water. Not only does this lessen the capacity for exploring the deep sea but it also introduces an extreme inequity for who can participate and lead these efforts. With a growing demand for the global Blue Economy and a …

Development And Characterization Of Polysaccharide-Based Controlled Drug Delivery Platforms, Amanda Pepler

Open Access Dissertations

Drug delivery research focuses on overcoming challenges in patient treatment. Most therapeutics are delivered systemically through oral or intravenous routes. However, these administration methods are often inefficient due to therapeutic hydrophobicity, poor bioavailability, and liver metabolic clearance, necessitating frequent dosing. When treatment plans require frequent dosing, patients experience significant fluctuations in therapeutic concentration in the bloodstream, which can increase adverse side effects. These factors contribute to low patient compliance. Therefore, the core focus of drug delivery research is to develop new administration routes for therapeutics. Drug delivery systems (DDS) that can target therapeutics to a localized region offer advantages over …

Environmentally Friendly Chelation For Enhanced Algal Biomass Deashing, Agyare Asante, George Daramola, Ryan W. Davis, Sandeep Kumar

Civil & Environmental Engineering Faculty Publications

High ash content in algal biomass limits its suitability for biofuel production by reducing combustion efficiency and increasing fouling. This study presents a green deashing strategy using nitrilotriacetic acid (NTA) and deionized (DI) water to purify Scenedesmus algae, which was selected for its high ash removal potential. The optimized sequential treatment (DI, NTA chelation, and DI+NTA treatment at 90–130 °C) achieved up to 83.07% ash removal, reducing ash content from 15.2% to 3.8%. Elevated temperatures enhanced the removal of calcium, magnesium, and potassium, while heavy metals like lead and copper were reduced below detection limits. CHN analysis confirmed minimal …

Flower Cluster Matching Utilizing The Unscented Transform For Robotic Pollination, Andy Chu

Graduate Theses, Dissertations, and Problem Reports (ETD)

The use of automated systems for agriculture is integral to keeping the food supply secure. Both industry and academia are exploring and applying methods to increase the yield of plants in environments ranging from outdoor fields to greenhouses. Specifically, many automated systems use continuous monitoring of plants to track plant health and yield. The use of computer vision is necessary when it comes to precision operations that use robotics. Today, robots are trained to weed, harvest, and pollinate. To accomplish these tasks autonomously, a lot of data is needed, which is where spatial-temporal observations of the plants are being recorded …

Enhancing Pillar Stability Assessment In Underground Limestone Mines Using Lamodel: Integrating Empirical Strength Equations For Improved Ground Control, Alim Elibol

Graduate Theses, Dissertations, and Problem Reports (ETD)

Underground stone mining accounts for approximately 21% of all underground mining operations in the United States, with more than 2,000 workers employed in this sector as of 2019. Ground control failures, particularly those involving massive pillar collapses and roof falls, pose significant safety hazards, contributing to 40% of fatalities and 15% of lost workdays since 2006. Several massive pillar collapses have occurred in active limestone mines from 2015 to 2021, and a fatal incident was reported in January 2022 in a mine operating within the Loyalhanna formation. Given these risks, accurate stability assessment tools are essential for improving mine safety. …

A Multiscale Ai Framework For Forest And Agriculture Health Monitoring: Drone-Based Object Recognition And Segmentation For Automated Ecological Assessment, Sruthi Keerthi Valicharla

Graduate Theses, Dissertations, and Problem Reports (ETD)

Forest and agricultural ecosystems are increasingly at risk due to invasive species, pests, and diseases, necessitating scalable, automated, and intelligent monitoring solutions. Traditional field based forest and agriculture health assessments are limited by cost, time, and spatial coverage. This dissertation presents a multiscale deep learning framework that automates forest and agriculture health monitoring using drone imagery and computer vision techniques. The system operates across three spatial levels: forest level, tree level, and leaf level, combining object detection, segmentation, and classification models to support large scale ecological assessment.

At the forest level, high-altitude drone imagery is processed using object detection and …

Microwave-Assisted Reduction Of Critical Metal Oxides From E-Waste Mixture, Kurundu Shavinka Jayasekera

Graduate Theses, Dissertations, and Problem Reports (ETD)

The efficient recovery of critical metals such as tantalum (Ta), manganese (Mn), gallium (Ga), and indium (In) from electronic waste (e-waste) is essential for resource sustainability and environmental protection. This research uses microwave-assisted treatment to investigate the carbothermal reduction of these four specific metals from their oxides in a simulated e-waste mixture, with carbon black serving as both a microwave coupling and reduction agent. Microwave processing is used specifically because it offers a rapid and energy-efficient alternative to conventional thermal methods, with the benefits of intrinsic heating and selective heating of materials. The first experiments centered on understanding the effects …

Customer Segmentation And Fuel Economy Prediction Using Telemetry Data From Heavy-Duty Trucks, Batishahe Selimi

Graduate Theses, Dissertations, and Problem Reports (ETD)

Heavy-duty trucks constitute only a modest fraction of on-road vehicles, yet their intensive duty cycles and high fuel demands yield a disproportionately large share of transportation fuel use and greenhouse gas emissions. Addressing this imbalance requires data-driven tools that capture the realities of fleet operation and translate complex telemetry into actionable insight.

This dissertation introduces a unified machine-learning framework that operates exclusively on high resolution time-series data collected from fifty-nine diesel trucks deployed across Southern California. It begins by constructing a multi-modal feature space that blends statistical summaries of key engine signals, static vehicle descriptors, and Mel-Frequency Cepstral Coefficients, thereby …

Autonomous Vehicle Supported Mobility Services For Rural Areas, Alvira Ahmed Tasnim

Graduate Theses, Dissertations, and Problem Reports (ETD)

Autonomous Vehicles (AVs) are emerging as a promising mobility solution to address growing transportation demands, particularly unmet traffic demand in rural areas where conventional transit systems often fall short. Rural areas face unique mobility challenges due to dispersed populations, limited infrastructure, and low/no transit coverage. Existing transit services tend to concentrate around dense urban cores, leaving low-demand areas outside underserved. This spatial undercoverage leads to significant mobility gaps for rural residents. This study examines the potential of AV-based ride-sharing services to address these mobility needs by providing flexible, demand-responsive mobility services where fixed-route transit services are either economically or operationally …

Leveraging Physiological Signal Activity And Self-Report Data To Assess Students’ Trust In “My Friendly Mind” App And Its Impact On Their Mental Health Knowledge: A Mixed-Method Phase 1 Clinical Trial Focusing On Depression And Attention Deficit Hyperactivity Disorder From Human Factors Standpoint., Yeganeh Shahsavar

Graduate Theses, Dissertations, and Problem Reports (ETD)

Mental health issues have become a significant global public health concern, especially among younger generations. The growing number of mental health challenges, combined with limited access to quality care, makes the problem even worse. Studies reveal that over 70% of individuals worldwide in need of mental health services do not receive appropriate care. Digital health technologies have the potential to enhance mental health services by making them more accessible and affordable. Despite the increasing popularity of mental health mobile applications (mHealth), there remains a lack of robust evidence of their effectiveness and the level of user trust, particularly in areas …

Investigation Of Spalling Effects On The Brittle Behavior Of Rectangular Pillars In Underground Stone Mines, Yolver Yesid Jimenez Ibanez

Graduate Theses, Dissertations, and Problem Reports (ETD)

Recent catastrophic collapses in U.S. limestone mines have exposed the limitations of traditional empirical design formulas for brittle rock masses. These events, especially those involving slender pillars, underscore that traditional methods often fail to account for progressive, spalling-dominated failure, highlighting a crucial gap in our understanding. This research addresses this issue by conducting a comprehensive investigation into the mechanics of spalling in rectangular stone mine pillars, utilizing advanced three-dimensional numerical modeling with FLAC3D combined with LiDAR-based geometric modeling.

The stability of a pillar is governed by a complex interaction between the in-situ stress field, rock mass properties, and pillar geometry. …

Revitalizing The Urban Riverfront: Redesigning The West End Bridge In Pittsburgh,Pa For Sustainable Multi-Use Connectivity And Public Space Creation., Mina Kargari Rezapour

Graduate Theses, Dissertations, and Problem Reports (ETD)

Urban waterfronts are critical assets for cities, offering opportunities for ecological restoration, public engagement, and multi-modal transportation. However, many riverfronts remain underutilized due to infrastructure barriers and lack of accessibility.

Pittsburgh’s riverfront is dynamic asset that supports recreation, cultural expression, and community interaction. The Three River Heritage Trail Loop, a visionary project led by River Life over the past 20 years, seeks to connect diverse neighborhoods and natural assets along the city’s rivers. At the heart of this initiative is the West End Bridge, serving as the bookend and a pivotal gateway to complete the loop and critical link for …

Assessing And Predicting The Impact Of A Retrofitted Methane Mitigator On Non-Road Large Natural Gas Engines Using Artificial Neural Networks, Augustine Thomas Ojo

Graduate Theses, Dissertations, and Problem Reports (ETD)

Abstract

ASSESSING AND PREDICTING THE IMPACT OF A RETROFITTED METHANE MITIGATOR ON NON-ROAD LARGE NATURAL GAS ENGINES USING ARTIFICIAL NEURAL NETWORKS

Augustine Thomas Ojo

Methane emissions from the oil and gas sector present a pressing environmental and economic challenge, given methane’s high global warming potential and the considerable volumes of unutilized gas lost from industrial processes. This study investigates the impact of a retrofitted methane mitigation system, the Methane Mitigator (M²), on fuel consumption and engine-out emissions in non-road, four-stroke natural gas-powered, lean-burn engines and develops predictive Artificial Neural Network (ANN) models to estimate the resulting engine-out emissions and fuel …

Synthetic Data–Driven Early Prediction Framework For Acute Kidney Injury In Patients Receiving Vancomycin And Ceftazidime/Avibactam, Maryam Ramazani

Graduate Theses, Dissertations, and Problem Reports (ETD)

Background: The nephrotoxic risks of combining ceftazidime/avibactam (AVI) with vancomycin (VAN) remain underexplored, despite both agents independently being linked to acute kidney injury (AKI). This study assessed the risk of AKI associated with concurrent VAN and ceftazidime/avibactam (VAN-AVI) therapy and developed synthetic data models to enable early prediction of AKI.

Methods: We conducted a retrospective analysis using electronic health record data from hospitalized adults between 2015 and 2022. The incidence of AKI was compared among patients receiving VAN-AVI or VAN in combination with piperacillin/tazobactam (VAN-TPZ) versus VAN monotherapy. AKI was defined as a composite of de novo and recurrent AKI …

Development Of A Model To Estimate Engine Crankcase Methane Vent Emission Rates, Juan Pablo Rincon

Graduate Theses, Dissertations, and Problem Reports (ETD)

The primary focus of this research is the modeling and validation of crankcase methane emissions stemming from natural gas combustion engines commonly deployed in the natural gas compression industry. The models employed rely on equations predicting the flow rate and composition of losses, typically generated within reciprocating piston-cylinder systems. Since crankcase emissions originate in a piston–cylinder arrangement, multiple variables were considered in this study, including displaced volume, pressure, temperature, flow, and viscosity (with corrections applied to account for the thermal and compositional effects on the mixture of fluids). The initial data for this research was collected during three measurement campaigns …

Watershed-Level Survey Of Antimicrobial Resistance From Onsite Wastewater Systems, Trey Franklin Clark

Graduate Theses, Dissertations, and Problem Reports (ETD)

The emergence of antimicrobial resistance (AMR) has quickly become one of the largest global health and development threats. Research related to environmental modes of transmission of AMR has accelerated in recent years, and the US Environmental Protection Agency is particularly interested in this emerging contaminant. Knowledge of how onsite (decentralized) wastewater treatment systems contribute to this issue is lacking, so this research serves to analyze and quantify the environmental dissemination of AMR and fecal contamination from these sources at a watershed level while using statistical analysis to assess relationships between physiochemical, spatial, and sampling parameters. This research surveyed four watersheds …

The Influence Of Social Achievement Goals On Grit In Aviation Education, Austin T. Walden

Journal of Aviation/Aerospace Education & Research

Aviation students have multiple ways of persisting and persevering in their aviation education, and they do not learn in a vacuum. Their social goals may help explain why some aviation students show more grit than others in an aviation education environment. The study examined the relationship and predictive ability of social achievement goals (i.e., social development goal, social demonstration-approach goal, and social demonstration-avoid goal) to predict grit in an aviation education environment. The constructs of social achievement goals and grit have yet to be examined together in motivation research. This paper provides a new predictive model for using the two …

Towards The Wearable Cardiorespiratory Sensors For Aerospace Applications, Chandan Sheikder

Journal of Aviation/Aerospace Education & Research

In safety-critical aviation operations, adaptive Human-Machine Interfaces (HMI) rely on accurate physiological monitoring to mitigate cognitive overload. While cardiorespiratory sensors are promising for real-time cognitive workload assessment, existing studies lack rigorous validation of consumer-grade devices in high-stress aviation contexts and fail to address measurement uncertainty propagation. This study evaluates the Zephyr BioHarness, a commercial wearable sensor, against clinical-grade equipment during arithmetic tasks simulating aviation cognitive demands. By integrating a neuro-fuzzy system with uncertainty propagation methods, we quantify the reliability of heart rate (HR) and breathing rate (BR) metrics for workload estimation. Results demonstrate moderate HR accuracy (RMSE: 4.85 bpm, CC: …

Empowering Precision Forecasting: Self-Supervised Lstm For Hourly Pressure And Temperature Prediction, Anand Shankar, Deepak K. Singh, Mantosh Kumar, Pankaj Kumar, Pradhan Parth Sarthi

Journal of Aviation/Aerospace Education & Research

The most important parts of any flight are landing and takeoff, and an aircraft's takeoff configuration must balance the regulated takeoff weight, runway length, and weather conditions to ensure a safe departure and arrival. In addition to runway length, wind, temperature, pressure, and visibility determine the total allowed takeoff weight and the economic viability of any trip. Thus, any meteorological office involved in flight planning and operation at any airport must accurately assess these factors, known as takeoff data. This research paper suggests multivariate self-supervised LSTM-based models to accurately predict the temperature and pressure (MSLP) of the takeoff data. The …

Metal-Organic Thin Film Coated On Optical Fiber, Nahideh Salehifar

Doctoral Dissertations

This dissertation explores the development of metal-organic framework (MOF)-based optical fiber sensors for detecting volatile organic compounds (VOCs) at low concentrations (parts-per-billion to parts-per-million). In the first part of the study, theoretical calculations were performed using effective medium approximation (EMA) models, including Lorentz–Lorentz, Maxwell–Garnett, and Bruggeman equations, to predict the refractive index changes of MOFs upon gas adsorption. These models were applied to MOFs such as ZIF-7, ZIF-8, ZIF-90, MIL-101(Cr), and HKUST-1 to evaluate their potential for gas sensing.

In the second part of the dissertation, experimental work was conducted to validate the theoretical predictions. MOF-coated optical fibers were fabricated …

Reduced Set-Shifting Processing Speed In Male Rats Following Low Dose (10 Cgy) Proton Exposure, Hui Ho Vanessa Chang, Gyutae Kim, Kyu-Sung Kim, Richard A. Britten

Center for Integrative Neuroscience and Inflammatory Diseases (CINID) Faculty Publications

Space radiation (SR) exposure poses significant biomedical risks, including effects on the central nervous system (CNS). These risks are particularly relevant to cognitive function during long-duration space missions. One critical cognitive skill is decision-making, which requires attentional set-shifting (ATSET)—the ability to quickly assess problems, evaluate options, and select the best actions. Previous studies have shown that exposure to However, the impact of low LET (< 1 keV/μm) protons, which significantly contribute to the total radiation flux astronauts encounter within spacecraft, on ATSET performance is unknown. To address this gap, we evaluated the effects of cranial irradiation with 10 cGy of 100 MeV/n protons (LET = 0.732 keV/μm) on ATSET performance in male Sprague-Dawley rats. We also investigated whether concurrent exposure to variable gravity (hypergravity step-up, step down, purported to have the same effect as exposure to microgravity (another major spaceflight stressor) exacerbated SR-induced cognitive deficits. Our findings indicate that proton exposure alone significantly impaired ATSET performance, as evidenced by decreased processing speed while performing compound discrimination reversal and extra-dimensional shifting. Notably, no additive or synergistic effects were observed when hypergravity was combined with proton exposure. The impact that low-dose proton exposure has on CNS functionality, particularly in reducing processing speed during complex tasks, warrant further investigation. If similar cognitive deficits were to occur in astronauts exposed to galactic cosmic rays, mission success and safety could be significantly compromised.

Neuroinflammatory Signaling And Immune Cell Infiltration Differ In Brains Of Rats Exposed To Space Radiation And Social Isolation, Austin M. Adkins, Zachary N. M. Luyo, Alea F. Boden, Riley S. Heerbrandt, Richard A. Britten, Laurie L. Wellman, Larry D. Sanford

Center for Integrative Neuroscience and Inflammatory Diseases (CINID) Faculty Publications

Astronauts on the proposed Mars missions will be exposed to extended periods of social isolation (SI) and space radiation (SR). SI and SR-induced immune dysregulation can result in persistent neuroinflammation and neuronal damage which could negatively impact an astronaut’s health and ability to maintain adequate levels of performance. The synergistic effects of combined SI and SR on immune system functionality and the brain remain unknown. Determining how single and combined inflight stressors modulate the immune system is crucial for fully understanding pathways impacting astronaut health and performance. We used ground-based analogs of SI and SR in rodent models to investigate …

Biochar For Soil Amendment: Applications, Benefits, And Environmental Impacts, Ujjwal Pokharel, Gururaj Neelgund, Ram L. Ray, Venkatesh Balan, Sandeep Kumar

Civil & Environmental Engineering Faculty Publications

The excessive use of chemical fertilizers results in environmental issues, including loss of soil fertility, eutrophication, increased soil acidity, alterations in soil characteristics, and disrupted plant–microbe symbiosis. Here, we synthesize recent studies available from up to 2025, focusing on engineered biochar and its application in addressing issues of soil nutrient imbalance, soil pollution from inorganic and organic pollutants, soil acidification, salinity, and greenhouse gas emissions from fields. Application of engineered biochar enhanced the removal of Cr (VI), Cd²⁺, Ni²⁺, Zn²⁺, Hg²⁺, and Eu³⁺ by 85%, 73%, 57.2%, 12.7%, 99.3%, and 99.2%, …

The Bioremediation Of Nutrients And Heavy Metals In Watersheds: The Role Of Floating Treatment Wetlands, Nirmal Kumar, Bhupinder Singh, Yuanze Chen, Abishek Kafle, Ram L. Ray, Sandeep Kumar, Xiaonan Shan, Venkatesh Balan

Civil & Environmental Engineering Faculty Publications

Floating treatment wetlands (FTWs) are engineered systems that utilize floating platforms planted with aquatic vegetation to treat polluted water such as stormwater, agricultural runoff, and wastewater. FTWs have emerged as promising and environmentally sustainable solutions for water purification. This review synthesizes the current knowledge on FTW design, plant selection, and performance evaluation. It highlights key factors influencing nutrient and heavy metal removal, including the hydraulic retention time, mat thickness, and types of plant species. Recent findings on the roles of root architecture, microbial interactions, and seasonal variability in treatment efficiency are also discussed. Additionally, the review explores advanced analytical methods …

Laboratory Investigation Of Shape And Initial Orientation Effects On Surf Zone Object Migration, Temitope E. Idowu, Jack A. Puleo

Civil & Environmental Engineering Faculty Publications

Discarded objects like munitions in marine environments pose public safety risks. The behavior of various density objects deployed at four cross-shore positions in the surf zone of a large-scale 120 m x 5 m x 5 m wave flume were observed under different forcing conditions. Net migration was predominantly directed offshore, with approximately 70 % offshore migration observed near the outer surf zone. Density, shape, and initial orientation were identified as important to object behavior, with density acting as the dominant driver in 67 % of the object pairing scenarios. The influence of shape and initial orientation on net migration …

Contextual Augmentation In Artificial Intelligence, Emmanuel Joshua Balogun

College of Graduate Studies: Theses & Dissertations

Contextual understanding is a significant challenge of Large Language Models (LLMs), which are typically trained on general-purpose datasets. Due to this, LLMs fail to capture nuanced or domain-specific information and may struggle to interpret user queries accurately. Consequently, prompt engineering can become complex in automating, and LLMs are prone to “hallucinating”—generating random or irrelevant texts—when they lack sufficient context. This undermines their ability to provide focused, accurate responses. Accordingly, this thesis seeks to enhance the contextual understanding capabilities of Artificial Intelligence systems to facilitate more precise and relevant answer generation. Study A looks into a new approach to combating misinformation …

Enhancing Ai-Driven Automation For Object Detection And Computer Vision, Nafeeul Alam Walee

College of Graduate Studies: Theses & Dissertations

In recent years, AI-driven automation has revolutionized the field of object detection and computer vision, enabling sophisticated and efficient solutions across various industries. This research explores the latest advances and techniques in improving AI-driven automation for object detection and computer vision applications. We examine state-of-the-art deep learning models and frameworks that have contributed to significant improvements in accuracy and speed and highlight the generative results. The focus is on exploring the real-time processing capabilities that have expanded the applicability of these technologies in real-world scenarios. Furthermore, we investigate image integration and video data to improve precision detection and contextual understanding. …