Systems Science Commons^™

Degree Assortativity And Estimation Of Degree Distribution In A Contact Network Of People Who Inject Drugs, Peter Geissert

Dissertations and Theses

This dissertation analyzes data from a respondent driven sampling (RDS) study of people who inject drugs in Portland, OR.  The objective was to assess assumptions for the use of weighted estimators to estimate a population physical contact (syringe sharing) network degree distribution.

Visualization of degree distribution for social and physical networks, Kolmogorov-Smirnov tests of difference, and kernel tests of equivalence were used for comparison of distributions. Spearman's rho and GAM non-parametric regression models were used to test for association and assess non-monotonic relationship. The validity of Markov chain assumptions for RDS was assessed using a transition matrix, Markov chain graphs, …

Spatial Reconstructability Analysis: Information-Theoretic Modeling Of Categorical Raster Data, David Percy

Dissertations and Theses

This research focuses on the application of Reconstructability Analysis (RA), an information-theoretic machine learning (ML) methodology, to categorical spatial data in Geographic Information Systems (GIS). RA was developed in the systems community with applications including classification, prediction, pattern recognition, and decision analysis. RA is implemented in OCCAM (Organizational Complexity Computation and Modeling), a software suite developed at Portland State University that analyzes discrete data in tabular form, with observations in rows and variables in columns. While RA has been successfully applied to a variety of domains, no systematic application to georeferenced raster data arranged in regular grids has been previously …

Structured And Interpretable Representations For Ensemble And Dynamical Systems, Yuan-Hung Kuan

McKelvey School of Engineering Graduate Student Theses & Dissertations

Precise and robust manipulation of large populations of structurally identical dynamical systems with heterogeneous dynamics, known as ensemble systems, is a central challenge in many emerging applications. These systems are often difficult to analyze and control because of the inherent nonlinearities, high dimensionality, heterogeneity, and incomplete knowledge of the underlying dynamics. To address these challenges, this thesis develops structured and interpretable representations for the analysis, control, and learning of nonlinear ensemble systems. Its central objective is to transform complex heterogeneous dynamics into representations that preserve essential structural and dynamical properties while enabling tractable computation and effective control design. First, the …

Functionality Risk Assessment And Risk-Based Management For Critical Infrastructure Systems, Anteneh Zewdu Deriba

Dissertations and Theses

Critical infrastructure systems such as transportation networks serve as the lifelines of modern society, supporting economic activity and ensuring access to critical services. Their disruption under extreme events can produce consequences extending far beyond physical damage to individual assets. In transportation networks for instance, failures of critical assets such as bridges and tunnels can reduce accessibility of communities, limit freight capabilities for commerce, and increase travel distance and travel time of road users. These consequences associated with the system functionality loss are tied not only to the physical vulnerability and failure of individual assets, but also to the system configuration …

Graph-Based Machine Learning For Multivariate Time Series Prediction In Scientific Domains: Streamflow Forecasting And Solar Flare Prediction, Kishore Ragul Alagarsamy

All Graduate Reports and Creative Projects, Fall 2023 to Present

Machine learning methods applied to multivariate time series data have emerged as powerful tools across a range of scientific domains. This report examines two distinct application areas in which such methods yield actionable predictive insights: hydrological streamflow forecasting and solar flare prediction in space weather.

In the domain of streamflow forecasting, a Two-Graph Spatio-Temporal Graph Neural Network (Two-Graph STGNN) was developed to predict river discharge across a 20-station network in the Upper Colorado River Basin. The architecture separates hydrological and meteorological feature streams into two complementary graph representations and fuses them through a learned attention mechanism. Systematic evaluation across 23 …

Structure Meets Context: A Simulation Study Of Centralized And Decentralized Decision-Making In Complex Environments, Murat Balci

Engineering Management & Systems Engineering Theses & Dissertations

The question of when to centralize or decentralize decision-making authority is a foundational problem in organizational theory and management science. Yet existing research often favors decentralized structures, frequently treating them as universally superior, and provides limited guidance on when centralization may be more effective. Leaning toward decentralization without thoroughly exploring options leaves organizational designers without clear direction under varying environmental conditions. This study addresses that gap by developing and applying a simulation-based framework to evaluate the context-dependent effectiveness of centralized and decentralized decision-making structures.

Drawing on contingency theory and decision science, the study conceptualizes environmental complexity along two dimensions: dynamism …

Drones In Wildfire Airspace Management: Developing A Scalable Framework For Mid-Sized Cities, Victoria Denise Tanner Williamson

Engineering Management & Systems Engineering Theses & Dissertations

Wildfires are becoming increasingly frequent and severe, creating growing risks for mid-sized communities with limited emergency response resources. The January 2025 Los Angeles wildfires—which burned more than 57,000 acres and destroyed over 18,000 structures—underscore the urgent need to strengthen wildfire response strategies through the integration of unmanned aerial systems (UAS) (Hardy, 2025).

This study develops a scalable framework for drone-assisted wildfire response in resource-constrained environments. Using the city of Waynesboro, Virginia, as a case study, the research examines how mid-sized jurisdictions can integrate unmanned aerial vehicles (UAVs) to improve preparedness, response, and recovery. A three-round, mixed-methods Delphi study engages emergency …

Physics-Guided Deep Learning For Predictive Modeling Of Spatiotemporal Dynamical Systems, Niharika Deshpande

Engineering Management & Systems Engineering Theses & Dissertations

Many physical and networked systems evolve under continuously changing spatial and temporal conditions. Transportation networks respond to fluctuating demand, atmospheric fields reorganize as storms intensify, and coastal response depends on localized forcing pathways. Modeling such systems requires learning formulations that adapt to evolving structure, operate on irregular geometries, and provide interpretable measures of predictive uncertainty. This dissertation develops a physics-guided spatiotemporal learning framework designed for structured dynamical systems whose governing interactions are neither static nor Euclidean. The central premise is that spatial relationships in these systems are dynamic and geometry-dependent. To represent this behavior, system states are modeled on time-varying …

Improving Urban Search And Rescue Team Coordination Through Adaptive Context Awareness, Daniel Reyes Duran

Doctoral Dissertations and Master's Theses

Modern multi-agent Urban Search and Rescue (USAR) operations heavily rely on mobile geospatial Common Operating Pictures (COPs) to maintain team coordination and Situational Awareness (SA). However, the proliferation of high-frequency sensor telemetry at the tactical edge has introduced a data saturation paradox challenge: while information theoretically drives informed decision-making, unmanaged data surges induce increased operator cognitive overload and alert fatigue on mobile End-User Devices (EUDs), while downstream data-broadcasting models inherently strain edge processing and viewport environments.

To resolve these constraints, this dissertation presents a context-aware Value of Information (VoI) data-management framework integrated directly with a custom, event-driven Android Team Awareness …

Digital Lean Transformation Through Mes–Scada Intergration: Reducing Transactional And Cognitive Waste In High-Volume Electronics Manufacturing, Kamal Alalul

Graduate Dissertations and Theses

This research addresses inefficiencies in Manufacturing Execution System (MES)-driven workflows, where excessive user interactions and fragmented access to work instructions introduce transactional and cognitive waste. To address these limitations, an integrated architecture was developed using Ignition as a SCADA-based operator interface connected to the MES through API-based communication. The system replaces direct MES interaction with a unified interface that embeds work instructions and automates transaction execution within the production workflow. The approach was implemented and evaluated through an industrial case study on a 15 station electronics manufacturing line. Results show a 51% reduction in total cycle time, decreasing from 2,996 …

A Systems Engineering-Based Method For Enabling Digital Transformation In A Binational Small Healthcare Enterprise, Manuel Adrian Alvarez Ramirez

Open Access Theses & Dissertations

The year 2020, often described as "the great accelerator" for emerging technologies, marked a period of significant transformation in lifestyles, work patterns, and business strategies. For instance, online meetings have become more convenient than in-person interactions, even with colleagues in neighboring offices. For small businesses, rapid regulatory and technological changes required swift adaptation, including the acquisition of new technologies, tools, and software to ensure survival. This transition also necessitated adopting indirect engagement, revising communication protocols, and digitally transforming information. Small and Medium-sized Enterprises (SMEs) in essential service sectors face also a fundamental paradox: their dependence on the skills, relationships, and …

Plasmoid Vortex System Retrofit A Sustainability And Efficiency Study On Internal Combustion Engines, Walker Hall

Doctoral Dissertations and Master's Theses

The thesis addresses the persistent inefficiency and environmental degradation caused by internal combustion engines in modern vehicles, a major issue as the automotive industry faces increasing pressure to reduce fuel consumption and greenhouse gas emissions. Internal combustion engines, which power most cars today, convert only about 20-30% of fuel energy into useful work, with the remainder lost as heat and exhaust waste, including carbon monoxide (CO), carbon dioxide (CO₂), hydrocarbons (HC), and nitrogen oxides (NOx). This inefficiency contributes to global carbon emissions, with transportation accounting for approximately 29% of U.S. greenhouse gases in 2021 [1]. As regulatory standards tighten (e.g., …

An Integrated Bayesian Network-Based Zero Trust Model To Quantify Cyber Risk In Small-Medium Businesses, Ahmed Abdelmagid

Engineering Management & Systems Engineering Theses & Dissertations

Small-medium businesses (SMBs) play a pivotal role in the worldwide economy as they constitute the most considerable portion of businesses in developed countries like the UK and the US. As such, SMBs are likely targets of cybercrimes by malicious agents because of their vulnerable IT systems. The digital infrastructure of SMBs is more likely to be hit by cyberattacks than large businesses due to many factors that facilitate hackers’ missions. These factors include a limited financial budget devoted to cybersecurity, a lack of knowledge, an underrating of how dangerous cyber threats are, and a shortage of IT expertise. The enormous …

Identification Of Thruster Faults In Underwater Vehicles By Using Custom Encodings In Spiking Neural Networks, Donovan Gegg

LSU Master's Theses

Autonomous Underwater Vehicles (AUVs) are untethered robotic platforms used for tasks such as seafloor mapping, infrastructure inspection, and environmental monitoring. Recent technological advances have produced smaller, more affordable platforms, broadening access to research teams and small companies alike. This miniaturization comes at the cost of them handling drawbacks associated with a more compact machine such as reduced battery capacity as well as limited processing and sensing capabilities. These constraints make small-sized marine vehicle’s reliability critical as they can cause malfunctions, making the loss of a vehicle more likely. Actuator faults are particularly consequential as unintended and unstable control in an …

A Capability Maturity Model For Artificial Intelligence Integration In Supply Chain Management, Lordt Becklines

Dissertations

Artificial Intelligence (AI) is transforming Supply Chain Management (SCM), yet many organizations struggle to assess their readiness for AI adoption and to understand how AI capabilities develop across maturity stages. This dissertation addresses this gap by developing a Capability Maturity Model (CMM) for AI integration in SCM, grounded in Organizational Information Processing Theory (OIPT), the Resource-Based View, and related capability frameworks. The model provides a structured approach for evaluating an organization's information-processing requirements, resource configurations, and alignment needed for effective AI-enabled supply chain operations.

Using a design science research approach, the AI-SCM CMM and its associated assessment instrument were derived …

Feasibility Of Corn Stover For Biogas Production In Northwestern Illinois: A Case Study Of Jo Daviess And Carroll Counties, Anthony Amotoe-Bondzie

Masters Theses

The growing demand for renewable energy and sustainable waste management has intensified interest in agricultural residues such as bioenergy feedstocks. This study evaluated the feasibility of converting corn stover into biogas through anaerobic digestion (AD) in Jo Daviess and Carroll Counties, Illinois, with consideration of the Savanna Industrial Park as a potential centralized processing hub. Although corn stover represents one of the largest biomass resources in the United States, this research identifies a critical gap between theoretical availability and practically recoverable feedstock. Using a mixed-methods framework, the study integrates biomass quantification, methane yield modeling, economic analysis, and policy assessment. Results …

Probing The Mechanisms Of Reinforcement Learning: Reinforcement Learning, Ventral Striatal Astrocytes, And The Dynamic Coordination Of Information Seeking With Learning, Fatih Sogukpinar

McKelvey School of Engineering Graduate Student Theses & Dissertations

While reinforcement learning has been a vital component in artificial intelligence and machine learning, there exist many open questions about its implementations and how to improve them, in both minds and machines. Among these are i) the contribution of non-neuronal cell types to reinforcement learning, and ii) information-seeking behavior during reinforcement learning. In this thesis, we studied these main topics pertaining to reinforcement learning. In the first chapter, we examined the role of astrocytes in reinforcement learning, and in the second, we investigated human information seeking during reinforcement learning. Neurons in the human and animal brain have been known to …

Modeling The Mojave: A Multiphase, Mixed Methods Phenomenological Investigation Of Novice Understanding Of A Complex Ecological System, Nicole Juliana Thomas

UNLV Theses, Dissertations, Professional Papers, and Capstones

This research explores early college science majors’ conceptions of the Mojave Desert ecosystem as a complex system. A multi-level, explanatory sequential mixed methods design was employed to study college science majors’ understanding of the Mojave Desert ecosystem to better inform science pedagogy. The first level, the quantitative phase of the study, consisted of developing a mathematical representation of twelve ecosystem variables present within the Mojave Desert. The second level, the qualitative phase of the study, consisted of phenomenological semi-structured interviews with 23 early college science majors. The findings of this research suggest that the Mojave Desert operates as a complex, …

An Innovative Hdm Model For Assessing Renewable Heat Energy Resources In Utility Energy Systems, Mark Alan Ryan

Dissertations and Theses

Renewable Heat Energy projects exhibit numerous unique, complex, and often conflicting characteristics, including varying project sizes, diverse energy resources, stakeholder demands, and significant challenges in energy conversion applications. Traditionally, cash flow analysis and return on investment are the most widely recognized metrics for evaluating remote energy projects. However, numerous quantitative and qualitative factors influence the overall performance and success of Renewable Heat Energy System (RHES) projects. A comprehensive approach using advanced tools is essential for assessing the factors and criteria that significantly impact these projects.

This research investigates the key quantitative and qualitative factors that contribute to the successful implementation …

Mbse For Process Analytical Technology- Bwon Analysis Case Study, Arnaldo Garcia Cervantes

Open Access Theses & Dissertations

Volatile Organic Compound (VOC) emissions from industrial sources, particularly Benzene, present significant environmental and public health challenges. Regulatory frameworks, such as the U.S. Environmental Protection Agency’s Benzene Waste Operations NESHAP (BWON), mandate strict monitoring of control devices, specifically carbon adsorption canisters, to prevent emission breakthrough. However, current industry practices rely heavily on manual Method 21 testing, a labor-intensive process that creates lagging indicators and increases the risk of non-compliance events. This thesis proposes the design and development of an on-line, automated fugitive emissions monitoring system tailored for carbon canisters using Model-Based Systems Engineering (MBSE). Utilizing the Object-Oriented System Engineering Method …

Comparative Analysis Of Emergent Behaviors Of Three Drone Swarm System Models For Targeting Using Agent-Based Modeling And Simulation, Arsenio T. Gumahad Ii

Engineering Management & Systems Engineering Theses & Dissertations

This dissertation introduces a novel computational simulation framework for evaluating the emergent behaviors of three swarm drone models using Agent-Based Modeling and Simulation (ABMS). The three swarm models are a Leader-Follower swarm model based on Bruckstein's antline theory, a Flocking model based on a simplified Reynolds 'Boids’ model, and a Stigmergic model with pheromone-based coordination. The primary objective of the simulation is to evaluate the performance of these models in delivering a user-defined number of drones of each type to a target area of interest in four separate scenarios, resulting in 50,000 separate simulation trials. Each scenario was structured to …

Component Model Development Of Heat Exchangers, Expanders, And Control Valves For Autonomous Cryogenics Plant Cool-Down, William Harris Buhrig Iv

Mechanical & Aerospace Engineering Theses & Dissertations

The traditional method of cryogenic plant cool-down involves having continuous on-call staff to head into the office at any time to modify the existing multi-layered PID control systems if the on-call staff member detects a significant deviation from the cool-down plan. This thesis aims to outline an effective method for modeling the structure of systems with performance characteristics that deviate from design requirements and from ideal inlet-outlet correspondence, enabling the adjustment and modification of existing control structures across all Thomas Jefferson National Accelerator Facility (JLab) cryogenic refrigeration plants. Analytical Modeling and Gaussian Process Regression (GPR) are applied to model the …

Evaluating Large Language Models For Requirements Engineering And Verification: A Comparative Analysis Of Gpt-5 And Gemini 3 Pro, Guadalupe Nevarez

Open Access Theses & Dissertations

Large Language Models (LLMs) are increasingly viewed as viable tools for Systems Engineering, yet empirical data regarding their effectiveness in generating engineering-quality requirements remains limited. This thesis compares the performance of GPT-5 and Gemini 3 Pro in generating system functional requirements for the Miner Guardian Drone, a prototype UAV designed for minefield remediation. Both models were provided with identical prompts and tasked with producing 15 functional requirements. Two independent raters evaluated the outputs using a nine-dimensional quality rubric aligned with IEEE 29148 and INCOSE guidelines to ensure rigorous assessment. The difference between the models was analyzed using descriptive statistics and …

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, …

From Model To Behavior: Methodological Challenges In Using Fuzzy Cognitive Maps To Represent Mental Models, Catherine Elizabeth Moore

Dissertations and Theses

This dissertation examines the use of Fuzzy cognitive maps (FCMs) as representations of mental models and how these representations connect to individual behavior. Fuzzy cognitive maps (FCMs) are semi-quantitative models that encode cause-and-effect structures as directed graphs. They are frequently used to represent individuals' knowledge structures/mental models and should in theory correlate to actions and decisions of individuals; however, this correlation has yet to be explored in a systematic manner. This dissertation represents the first step in experimental research on the connection between FCMs and individual behavior. It examines two theories of how humans use mental models, one based on …

An Envisioned And Efficient Design Of Next Generation Decentralized Iot Bot Detection Model, Ahmed Abdullah Almalki

Doctoral Dissertations

The Industrial Internet of Things (IIoT) and Internet of Medical Things (IoMT) are revolutionizing critical infrastructures, but their expansion has also introduced severe cybersecurity vulnerabilities. Traditional IoT Bot Detection Systems (IBDS) struggle to scale in environments characterized by high-dimensional, large-scale, and redundant network traffic. These challenges hinder the development of reliable cloud-based intrusion detection systems. The limitations of static and rulebased methods in detecting evolving IoT botnet attacks—such as those launched by Mirai and Gafgyt—underscore the need for intelligent, adaptive approaches. To address this, the present study proposes a machine learning and deep learning-driven IoT Botnet Detection Model, validated through …

Moment Ensemble Approaches For Estimation And Robust Quantum Control, Andre Luiz Paes De Lima

McKelvey School of Engineering Graduate Student Theses & Dissertations

Large-scale population dynamics governed by ensemble systems present significant challenges due to their inherently high dimensionality. Recent advances have demonstrated the effectiveness of moment-based methods (particularly those employing polynomial bases such as Legendre and Chebyshev polynomials) when integrated with optimization techniques for control design in ensemble systems. However, these methods have not been extensively explored for enhancing robustness against systematic noise, nor have they been widely applied to complex quantum systems involving multi-parameter configurations and entanglement phenomena. In this work, we extend the application of moment methods in ensemble systems through two projects. Initially by developing a Kalman filter analogue, …

A Control Theoretic Approach To The Stochastic Multi-Armed Bandit Problem With Applications In Hyperparameter Optimization, Jonathan Gornet

McKelvey School of Engineering Graduate Student Theses & Dissertations

Decision-making under uncertainty is a fundamental problem encountered frequently in many real-world applications. This challenge has been rigorously formulated as the Stochastic Multi-Armed Bandit (SMAB) problem, which consists of a learner interacting with an environment. For each interaction, the learner selects an action and then receives a reward from the environment based on the chosen action. The learner's objective is to maximize the accumulated reward over a set number of rounds. This thesis addresses the SMAB problem by leveraging the field of Control Theory and dynamical systems. We specifically focus on a SMAB environment where the rewards are the output …

Emergence Of Norms In The Presence Of Social Network Structure In A Stochastic Evolutionary Game Theory Model, Parker Foresman

Dissertations and Theses

In their 2000 paper, The Emergence of Classes in a Multi-Agent Bargaining Model, Axtell, Epstein, and Young introduced an agent-based bargaining model to study how social norms can emerge and persist from the decentralized, asynchronous interactions of individual agents in the presence of noise, even when these agents have no predefined preferences or advantages. This thesis extends their model by incorporating structured interaction networks, allowing us to investigate how the topology of agent interactions influences long-run dynamics and norm formation. Using a custom simulation package built in the Julia programming language, we replicate the original results under a complete …

Understanding The Elements Of Sterile Processing Workflow: Or, Sterilization And Personnel, Sayed Rezwanul Islam

All Dissertations

The Sterile Processing Department (SPD), also known as the Central Sterile Services Department (CSSD), is an essential part of hospitals and healthcare facilities and is responsible for ensuring the cleanliness, sterility, and proper functioning of medical instruments. Sterile processing departments (SPDs) are key drivers of productivity, effectiveness, safety, and infection control in hospitals. A well-designed SPD workflow can enhance patient safety, reduce operating room (OR) delays, and improve productivity. To understand the flow of sterile processing and interactions between the OR and SPD, process maps and task analyses were developed through direct observations of the basic SPD functions: decontamination, assembly, …