Systems Science Commons^™

Spatial Markov Equilibrium Models For Taxi Services: Driver Decision, Search Friction, And Locational Pricing, Yanchao Liu

Industrial and Systems Engineering Faculty Research Publications

This paper develops a modeling framework for stochastic multi-agent systems and applies it to equilibrium and pricing analysis in urban taxi markets. Travel demand is represented as a trip network and embedded in a Markov chain that captures both locational and in transit taxi states, with transition dynamics reflecting trip durations, search frictions, spatial competition, and drivers’ perceptions of long-term value. The framework features a parametric Markov chain with endogenous transition probabilities and a behavioral model in which agents’ decisions depend on anticipated long-term rewards. We establish equilibrium existence and examine two locational pricing schemes that align individual incentives with …

The Self-Aware Room: A Framework For Operational Self-Awareness In Evolvable Blended Environments, David B. Smith

Publications and Research

The Self-Aware Room (SAR) is a room-scale research environment developed within the larger Balanced Blended Space and Blended Reality Performance System research trajectory. Rather than treating the room as a conventional “smart” environment composed of fixed automation technologies, SAR approaches it as an evolvable blended environment made from physical, virtual, conceptual, sensory, computational, and performative relationships. Its defining feature is not any particular sensor, model, or output device, but the set of transformations through which physical activity becomes structured observation, bounded representation, interpreted state, governed decision, and mediated response.

This paper develops the conceptual and methodological foundations of SAR as …

Can Generative Ai Make Farming Decisions? Current Status And Future Pathways: A Case Study In Row Crop Production With Chatgpt, Nipuna Chamara, Yufeng Ge, Joe Luck, Yu Pan, Saleh Taghvaeian, Cory Walters, Christopher Proctor, Daran Rudnick, Daren Redfearn

Department of Agricultural and Biological Systems Engineering: Faculty Publications

The agricultural decision-making process is experience-based, knowledge-dependent, time-sensitive, complex, and driven by historical data. Planting, fertilization, irrigation, and chemigation are key categories in farm decision-making, and currently there is no one-shot decision-support tool that covers all these activities. Generative Artificial Intelligence (AI) models are more advanced than traditional machine learning and deep learning models. These models have been trained on vast amounts of data from the internet, allowing them to accept unstructured data in various forms and generate human-like text, solutions to problems, and scenario predictions. Given this capability, we became interested in exploring the potential of generative AI in …

Meeting The Moment With Ai-Employer Informed Education, Brent Terwilliger, John Faraca

Publications

As artificial intelligence transforms aviation, aerospace, and autonomy-related sectors, higher education must adapt to meet evolving workforce demands. This session shares emerging findings from a nationwide study led by Embry-Riddle Aeronautical University, focused on employer perceptions of AI adoption, responsible use, and workforce preparedness in domains including uncrewed systems, space systems, robotics, and advanced air mobility. Based on a structured survey and follow-up interviews, the presentation explores how organizations are using AI tools, from generative platforms to enterprise systems, and defining effective and inappropriate use in operational contexts. Participants will gain insight into critical concerns (e.g., data privacy, compliance, security, …

Data Compression In Additive Manufacturing: Recent Progress And Opportunities, Dongmin Ethan Kang, Wenmeng Tian

Publications

A crucial aspect of quality control for Additive Manufacturing (AM) processes is the acquisition of diverse data from the entire lifecycle of the product. AM data has grown significantly in terms of diversity and volumes, resulting in diverse data formats of increasing volumes, including time series, images, and point clouds. Large quantities of these data are essential for effective in-situ process monitoring and ex-situ non-destructive evaluation. However, this will result in large manufacturing and inspection datasets that are difficult to manage for users, which will delay the broader adoption of AM for mission critical applications. This motivates the urgent need …

Mission Engineering Competencies Workshop Report: Roles, Responsibilities, And Skills, James D. Moreland, Jr. (Editor), Thomas Irwin, Robert-Allen Baker

Center for Mission Engineering (CME) Publications

This report summarizes the findings of a two-day Old Dominion University Mission Engineering Workshop involving the U.S. Navy, U.S. Marine Corps, U.S. Air Force, National Aeronautics and Space Administration, the University of New South Wales, and Old Dominion University, focused on defining the roles, competencies, and skills essential to Mission Engineering. It presents a structured competency framework and offers recommendations to guide workforce development, training, and cross-agency collaboration in support of complex, mission-driven systems.

Life Cycle Assessment Of Co-Located Floating Solar And Offshore Wind, Danela Sadikaj, Meghann Smith

Department of Earth and Environmental Studies Faculty Scholarship and Creative Works

Is combining floating solar and offshore wind platforms feasible for increasing energy capacity while displacing the environmental impacts that each of these systems has independently? In recent years offshore wind has become a very popular option to decarbonize the energy sector without overtaking valuable land. While floating solar is not a new technology, using solar in a marine environment is still in an experimental phase. This research proposes a Life Cycle Assessment (LCA) model of co-located solar technology within the proposed offshore wind farms near New Jersey’s coastline. While both technologies themselves are zero-emission while operating, they each require a …

Reducing Emissions And Increasing Resource Efficiency In Offshore Wind Turbines: Insight From Life Cycle Analysis, Jean Claire Pineda, Meghann Smith

Department of Earth and Environmental Studies Faculty Scholarship and Creative Works

Globally, offshore wind (OSW) has been identified as a key technology for decarbonization. While OSW’s development timeline remains uncertain in the United States, New Jersey has set the ambitious goal of 11 GW by 2040. OSW produces zero carbon emissions; however, it relies heavily on resource-intensive materials like steel, rare earth elements, and copper, and manufacturing those materials have numerous environmental impacts, including carbon emissions. Supply chain disruptions—from COVID-19, rising material costs, and geopolitical challenges—add further complexity. Previous research modelled the environmental impacts of Atlantic Shores OSW South Project with an established domestic supply chain, producing 0.013kg CO2 per kWh. …

Comparative Evaluation Of Linear Regression, Cross Validation And Regularization Approaches In Multivariate Data Analysis, Ransford Owusu, Felix Yeboah, Francis Effah Boateng

Data Science and Data Mining

This study evaluates linear regression and its enhanced variants incorporating cross-validation and regularization techniques for high-dimensional, multivariate datasets. We address challenges such as multicollinearity and overfitting. Methods including Ridge, LASSO, and Elastic Net are compared against ordinary least squares regression. Empirical analysis using an automobile dataset for fuel efficiency prediction shows that while OLS regression captures basic relationships, its limitations are mitigated through regularization and cross-validation, resulting in improved model interpretability. The findings provide a comprehensive framework for predictive modeling in complex data environments and offer insights into statistical methodology and practical applications in the automobile industry.

Future Of Bse Days 2025: Growing A Regenerative Bse, Derek M. Heeren, Santosh Pitla, Jennifer R. Keshwani, Mark Stone

Department of Agricultural and Biological Systems Engineering: Presentations and White Papers

The Future of BSE Days 2025: Growing a Regenerative BSE brought together over 150 faculty, staff, students, and partners to envision the next quarter-century of the Department of Biological Systems Engineering. The event emphasized regeneration—not only of resources and ecosystems, but also of ideas, learning models, and relationships. Across seven major sessions—three Spark Talks and four Pillar Workshops—participants explored how BSE can thrive amid technological disruption, demographic change, and societal transformation.

Key Outcomes

Redefining Impact: This session challenged participants to evolve from counting outputs to valuing relationships, collaboration, and community well-being.

Adaptive Learning Models: This discussion introduced design studios, micro-credentials, …

Thermal Management For Optimal Performance Of Polymer Electrolyte Membrane Unitized Regenerative Fuel Cells, Mythy Tran, Ayodeji Demuren

Mechanical & Aerospace Engineering Faculty Publications

Hydrogen is an excellent carrier for energy storage and can be produced from various green and renewable sources. However, the cost of producing hydrogen and converting it to useful energy is much higher than fossil fuel and traditional energy generation and storage systems. Unitized regenerative fuel cells (URFC) maximize utilization of high-cost cells and their components, thus, lowering system capital cost. Improving the URFC efficiency is an effective way to lower its operating cost. This study evaluates utilization of waste heat during operation and recovery strategy to improve system efficiency of Proton Exchange Membrane (PEM) URFC. A COMSOL Multiphysics 3-D …

Faids: Artificial Intelligence Developmental Systems Framework For Predicting And Preventing Cyberattacks In Supply Chain Networks, Lordt Becklines

Research & Publications

Cyber threats and attacks disrupt and damages supply chain networks (SCNs), which are complex and interlinked. Current methods to predict and prevent cyberattacks are inadequate and ineffective. This research proposes an AI developmental systems framework (FAIDS) to protect SCNs from cyberattacks. The framework has four components: (1) an AI threat intelligence system; (2) an AI risk assessment system; (3) an AI decision support system; and (4) an AI learning and adaptation system. The framework is tested on a simulated retail SCN. The results show that the framework can predict and prevent cyberattacks and improve the network's resilience and security. The …

Five Pivotal Practices For Leaders To Cultivate Empathy Within Virtual Scrum Teams, Dahm Mongkol Hongchai, Charles M. Weber

Engineering and Technology Management Faculty Publications and Presentations

In the digital and global economy, business leaders need more than just business and technology competencies; they also require empathy. However, there is a notable scarcity of research on empathy in Scrum for product development. This study aims to uncover critical insights into how leaders can foster empathy within virtual Scrum teams. Accordingly, this paper proposes five pivotal themes leaders can employ: Scrum fundamentals, meditation, self- and other-analysis, healthy interactions, and informal connections. These vital strategies offer actionable methods for leaders seeking to enhance empathy within their virtual Scrum teams.

Dynamic Metabolic Flux Analysis Amidst Data Variability: Sphingolipid Biosynthesis Case Study In Arabidopsis Thaliana Cell Cultures, Abraham Boluwatife Osinuga

Department of Chemical and Biomolecular Engineering: Dissertations, Theses, and Student Research

Sphingolipids are pivotal for plant development and stress responses. Growing interest has been directed towards fully comprehending the regulatory mechanisms of the sphingolipid pathway. In this study, we explore its de novo biosynthesis and homeostasis in Arabidopsis thaliana cell cultures, shedding light on fundamental metabolic mechanisms. Employing ¹⁵N isotope labeling and quantitative dynamic modeling approach, we obtained data with notable variations and developed a regularized and constraint-based Dynamic Metabolic Flux Analysis (r-DMFA) framework to predict metabolic shifts due to enzymatic changes. Our analysis revealed key enzymes such as sphingoid-base hydroxylase (SBH) and long-chain-base kinase …

Technical Data Package For Sysmlv2 Vignettes, Allen W. Dukes

Student Publications

This Technical Data Package (TDP) thoroughly compiles instructions, concepts, and solutions for eight SysMLv2 vignettes. Each vignette illustrates methods to solve complex system modeling challenges. The initial concept for each vignette can drive the use and evaluation of a custom SysMLv2 modeling tool as shown in [1]. They can support a baseline set of tasks to compare usability with other SysMLv2 modeling software tools.

The document starts with three pairs of vignettes for modeling a hypothetical Unmanned Aerial Vehicle (UAV) operated with a Virtual Reality (VR) headset derived from AFIT WKSP 696. The final pair of vignettes focus on two …

Experimental Data For: "Effects Of Network Connectivity And Functional Diversity Distribution On Human Collective Ideation", Yiding Cao, Yingjun Dong, Minjun Kim, Neil G. Maclaren, Sriniwas Pandey, Shelley D. Dionne, Francis J. Yammarino, Hiroki Sayama

Systems Science and Industrial Engineering Faculty Scholarship

This is the dataset collected from our online human-subject experiments described in the following manuscript:

Yiding Cao, Yingjun Dong, Minjun Kim, Neil G. MacLaren, Sriniwas Pandey, Shelley D. Dionne, Francis J. Yammarino, and Hiroki Sayama:
"Effects of Network Connectivity and Functional Diversity Distribution on Human Collective Ideation"
https://arxiv.org/abs/2307.04284

The Hazard Prediction Problem, Mary E. Helander, Brendan Smith, Sylvia Charchut, Erika Swiatowy, Calvin Nau, Gregory Cavaretta, Timothy Schuler, Adam Schunk, Héctor Ortiz-Peña

Social Science - All Scholarship

This work formulates the hazard prediction problem while addressing the research question: Can machine learning create a model to automatically recognize patterns that correspond to hazard state conditions during a mission-critical operation? Supervised learning models were trained and tested on data observed from mission simulators, which allowed for safe observation of dynamic system states and undesirable casualty events. The prediction task was formulated as a binary classification problem, producing the probability of being in a hazard state at time t and providing situational awareness of a possible imminent loss. Several modeling architectures were investigated: neural networks, logistic regression, a support …

Development Of A Machine Learning System For Irrigation Decision Support With Disparate Data Streams, Eric Wilkening

Department of Agricultural and Biological Systems Engineering: Dissertations, Theses, and Student Research

In recent years, advancements in irrigation technologies have led to increased efficiency in irrigation applications, encompassing the adoption of practices that utilize data-driven irrigation scheduling and leveraging variable rate irrigation (VRI). These technological improvements have the potential to reduce water withdrawals and diversions from both groundwater and surface water sources. However, it is vital to recognize that improved application efficiency does not necessarily equate to increased water availability for future or downstream use. This is particularly crucial in the context of consumptive water use, which refers to water consumed and not returned to the local or sub-regional watershed, representing a …

Analysis And Requirement Generation For Defense Intelligence Search: Addressing Data Overload Through Human–Ai Agent System Design For Ambient Awareness, Mark C. Duncan, Michael E. Miller, Brett J. Borghetti

Faculty Publications

This research addresses the data overload faced by intelligence searchers in government and defense agencies. The study leverages methods from the Cognitive Systems Engineering (CSE) literature to generate insights into the intelligence search work domain. These insights are applied to a supporting concept and requirements for designing and evaluating a human-AI agent team specifically for intelligence search tasks. Domain analysis reveals the dynamic nature of the ‘value structure’, a term that describes the evolving set of criteria governing the intelligence search process. Additionally, domain insight provides details for search aggregation and conceptual spaces from which the value structure could be …

Development Of User Interface And Testing Harness, Jacob Amezquita, William Albertini

College of Engineering Summer Undergraduate Research Program

No abstract provided.

Utilizing The System Engineering Trade Study Analysis Method To Analyze Patient Aeromedical Evacuation, Sara Shaghaghi, Jeremy M. Slagley, Michael E. Miller, Gaiven Varshney

Faculty Publications

The US Air Force has gone through many aeromedical patient isolation transport system designs. The first designs were developed in response to the Ebola outbreak in 2014 and, more recently, the COVID-19 pandemic. The trade study analysis part of the system engineering design method was used to analyze the historic and current aeromedical patient contamination control transport systems. A trade study is a process that evaluates alternatives based upon various “-ilities”, such as reconfigurability, flexibility, durability, cost, and more, and performs a systematic analysis to aid designers in producing a ‘good’ design alternative given the large set of possible solutions. …

Systems Thinking Activities Used In K-12 For Up To Two Decades, Diana Fisher, Systems Thinking Association

Complex Systems Faculty Publications and Presentations

Infusing systems thinking activities in pre-college education (grades K-12) means updating precollege education so it includes a study of many systemic behavior patterns that are ubiquitous in the real world. Systems thinking tools include those using both paper and pencil and the computer and enhance learning in the classroom making it more student-centered, more active, and allowing students to analyze problems that have been heretofore beyond the scope of K-12 classrooms. Students in primary school have used behavior over time graphs to demonstrate dynamics described in story books, like the Lorax, and created stock-flow diagrams to describe what was needed …

Modeling Wealth Distribution In A Society, Dylan Berns, Peihsien Sun, Adrian V. Gheorghe

Engineering Management & Systems Engineering Faculty Publications

The interconnectedness of social mood, changing dynamics, income inequality, and wealth distribution underscores the complexity of understanding and addressing these issues. This complexity inspires researchers to develop models and conduct further research to gain insights into the mechanisms driving income inequality and wealth distribution. By studying these phenomena more comprehensively, one can aim to develop strategies and policies that promote a more equitable distribution of wealth and opportunities, thereby fostering social stability and economic prosperity. In the present paper, there was build a model on wealth distribution and income inequality to help people understand the complexities of wealth inequality and …

Computational Decision Support For Socio-Technical Awareness Of Land-Use Planning Under Complexity—A Dam Resilience Planning Case Study, Andreas Tolk, Jennifer A. Richkus, F. Leron Shults, Wesley J. Wildman

VMASC Publications

Land-use planning for modern societies requires technical competence as well as social competence. We therefore propose an integrative solution enabling better land-use planning and management through better-informed decision-making. We adapt a method developed for cross-disciplinary team building to identify the stakeholders and their various objectives and value systems. We use these results to populate artificial societies embedded into a dynamic data analytics framework as a tool to identify, explore, and visualize the challenges resulting from the different objectives and value systems in land-use planning and management. To prove the feasibility of the proposed solution, we present two use cases from …

Machine-Assisted Discovery Of Integrable Symplectic Mappings, T. Zolkin, Y. Kharkov, S. Nagaitsev

Physics Faculty Publications

We present a new automated method for finding integrable symplectic maps of the plane. These dynamical systems possess a hidden symmetry associated with an existence of conserved quantities, i.e., integrals of motion. The core idea of the algorithm is based on the knowledge that the evolution of an integrable system in the phase space is restricted to a lower-dimensional submanifold. Limiting ourselves to polygon invariants of motion, we analyze the shape of individual trajectories thus successfully distinguishing integrable motion from chaotic cases. For example, our method rediscovers some of the famous McMillan-Suris integrable mappings and ultradiscrete Painlevé equations. In total, …

Using Strategic Options Development And Analysis (Soda) To Understand The Simulation Accessibility Problem, Andrew J. Collins, Ying Thaviphoke, Antuela A. Tako

Engineering Management & Systems Engineering Faculty Publications

Simulation modelling is applied to a wide range of problems, including defense and healthcare. However, there is a concern within the simulation community that there is a limited use and implementation of simulation studies in practice. This suggests that despite its benefits, simulation may not be reaching its potential in making a real-world impact. The main reason for this could be that simulation tools are not widely accessible in industry. In this paper, we investigate the issues that affect simulation modelling accessibility through a workshop with simulation practitioners. We use Strategic Options Development and Analysis (SODA), a problem-structuring approach that …

Event-Triggered Optimal Adaptive Control Of Partially Unknown Linear Continuous-Time Systems With State Delay, Rohollah Moghadam, Vignesh Narayanan, Sarangapani Jagannathan

Publications

This paper proposes an event-triggered optimal adaptive output feedback control design approach by utilizing integral reinforcement learning (IRL) for linear time-invariant systems with state delay and uncertain internal dynamics. In the proposed approach, the general optimal control problem is formulated into the game-theoretic framework by treating the event-triggering threshold and the optimal control policy as players. A cost function is defined and a value functional, which includes the delayed system output, is considered. First, by using the value functional and applying stationarity conditions using the Hamiltonian function, the output game delay algebraic Riccati equation (OGDARE) and optimal control policy are …

Emergence Of A Norm From Resistance: Using Simulation To Explore The Macro Implications Of Social Identity Theory, Khadijeh Salimi, Jesse T. Richman, Regina Karp, George P. Richardson, David Anderson

Political Science & Geography Faculty Publications

We usually hope that social norms discourage injustice. However, we are all witnesses to harmful norms enforced by governments, such as xenophobia, which need to be contested and changed. Previous studies have concluded that it is possible to change a harmful norm through contestation by powerless actors if suitable structural conditions exist. However, these structural conditions have not been sufficiently studied and, as such, are the focus of this paper. Our paper begins with a review of well-established micro-level theories of social identity theory (SIT), recast as a set of 42 discrete theoretical statements. These statements are then re-expressed in …

System Dynamics Modeling For Traumatic Brain Injury: Mini-Review Of Applications, Erin S. Kenzie, Elle L. Parks, Nancy Carney, Wayne Wakeland

Complex Systems Faculty Publications and Presentations

Traumatic brain injury (TBI) is a highly complex phenomenon involving a cascade of disruptions across biomechanical, neurochemical, neurological, cognitive, emotional, and social systems. Researchers and clinicians urgently need a rigorous conceptualization of brain injury that encompasses nonlinear and mutually causal relations among the factors involved, as well as sources of individual variation in recovery trajectories. System dynamics, an approach from systems science, has been used for decades in fields such as management and ecology to model nonlinear feedback dynamics in complex systems. In this mini-review, we summarize some recent uses of this approach to better understand acute injury mechanisms, recovery …

Smart Manufacturing—Theories, Methods, And Applications, Zhuming Bi, Lida Xu, Puren Ouyang

Information Technology & Decision Sciences Faculty Publications

(First paragraph) Smart manufacturing (SM) distinguishes itself from other system paradigms by introducing ‘smartness’ as a measure to a manufacturing system; however, researchers in different domains have different expectations of system smartness from their own perspectives. In this Special Issue (SI), SM refers to a system paradigm where digital technologies are deployed to enhance system smartness by (1) empowering physical resources in production, (2) utilizing virtual and dynamic assets over the internet to expand system capabilities, (3) supporting data-driven decision making at all domains and levels of businesses, or (4) reconfiguring systems to adapt changes and uncertainties in dynamic environments. …