Systems Engineering Commons^™

How’S It Growing? Tools For Observing Snow And Sea Ice In A Changing Arctic Ocean, Ian Alexander Raphael

Dartmouth College Ph.D Dissertations

September Arctic sea ice extent has diminished by roughly 50% in the 45 years since satellite observations began. The Arctic Ocean may experience ice-free summers within the next decade, with implications for habitat, resource extraction, geopolitics, and local and global climate change. To predict how Arctic sea ice will change in the future, we need to understand its behavior in the present. In situ sea ice mass balance measurements (snow accumulation, ice growth, snow and ice surface melt, and bottom melt) are essential for studying the processes driving rapid changes in the ice pack, and for validating remote sensing measurements …

Rotating Scatter Mask System Optimization Study For Determining Optimal Image Recreation, Seth L. Grover

Theses and Dissertations

The Rotating Scatter Mask (RSM) system is a radiation imaging technology currently limited by the mask design and governing identification algorithm parameters. To optimize the RSM design, Dakota—an optimization software—was integrated with a ray tracing code that simulates particle interactions with the RSM detector, and with the Locally Competitive Algorithm (LCA), which reconstructs the source image based on the ray tracing code’s Detector Response Matrix (DRM). Since the original ray tracing code was developed in MATLAB, it was translated into Python to improve compatibility with both Dakota and LCA. The Python version of the ray tracing code was then integrated …

Machine Learning And Optimization For Intelligent Decision-Making, Elson Cibaku

Dissertations

This dissertation presents a series of innovative machine learning and optimization model designs that address complex operational challenges across logistics and power systems. By integrating advanced neural architectures with robust optimization techniques, the work delivers scalable solutions designed to improve efficiency, reliability, and decision-making in dynamic and real-world environments. The first study introduces a two-stage approach to effective vaccine distribution. This framework tackles the capacitated vehicle routing problem by combining adaptive clustering techniques with reinforcement learning and a simulated annealing pickup policy. Through extensive computational experiments, the approach demonstrates substantial improvements in routing efficiency, reducing both computational time and logistical …

Air And Missile Defense Threat Scenario Variation To Reduce Pretest Sensitization, Video Games As A Case Study, Julie Renee Szekerczes

All-Inclusive List of Electronic Theses and Dissertations

This study uses fixed and variable video game types to measure pretest sensitization as a proxy for repeated and varied threat test scenarios in system performance testing of air and missile defense systems. The pretest sensitization phenomenon exists when repeated exposure to a test condition influences the participant's response. Research shows air and missile defense development correlates with video games, resulting in similar interfaces and computer operating environments. Department of Defense acquisition test and evaluation results must reflect system performance without prior knowledge of the threat scenarios confounding the results. System performance results inform acquisition decisions, such as further funding …

Compartmental Disaggregation: Bridging Simulation And Sampling Methods For Synthetic Population Data Generation, Dylan Mack

McKelvey School of Engineering Graduate Student Theses & Dissertations

As agent-based models (ABMs) grow increasingly widespread in public health, their associated challenges have become all the more significant. Lauded for their ability to capture population heterogeneity, nonlinear dynamics, and emergent behaviors, disease ABMs are also computationally expensive and often require detailed inputs that describe each agent at the individual-level, known as synthetic population data. Current approaches for synthetic population data generation generally fall into one of two categories: sampling or simulation. These methods are both feasible only under restricted conditions and suffer from challenges surrounding data availability and computing power. This thesis proposes compartmental disaggregation, an intermediate method for …

Tradespace Exploration With Statistical Modeling Techniques And Immersive Visual Representation In Virtual Environments, Nikhil Raj

All Theses

Statistical modeling techniques combined with virtual reality (VR) visualization offer powerful new approaches to tradespace exploration in engineering design. The research presented addresses the challenge of analyzing and communicating insights from complex multidimensional datasets, particularly for autonomous ground vehicle systems.

Beginning with a review of statistical methods—including Principal Component Analysis (PCA), Analysis of Variance (ANOVA), and correlation analysis—the study examines their applications in tradespace exploration. Building on this foundation, three distinct visualization pathways connecting MATLAB data to virtual environments are developed and evaluated: VRML representation, STL conversion, and Blender integration. Each approach is assessed for its ability to maintain data …

A Causal Model Of Performance Shaping Factors For Human Reliability Analysis In Manufacturing., Prameet Ranjan Jha

Electronic Theses and Dissertations

Human reliability analysis is a critical component of probabilistic risk assessment, aimed at predicting and mitigating human errors in complex systems. This dissertation develops a novel approach to human reliability analysis in manufacturing by integrating structural equation modeling and Bayesian networks to improve the estimation of human error probabilities. Traditional human reliability assessment methods, such as the Standardized Plant Analysis Risk-Human Reliability Analysis (SPAR-H) and the Technique for Human Error Rate Prediction (THERP), provide structured techniques for estimating human error probabilities. However, these methods often fail to capture the complex interdependencies among performance shaping factors (PSFs), limiting their applicability in …

An Msbe–Driven Advanced Air Mobility Post–Disaster Response System, Olabode A. Olanipekun

Graduate Theses and Dissertations (2019 - present)

In this work, an overarching conceptual design model towards the realization of a proposed Advanced Air Mobility Post–Disaster Response System (AAMPDR system) was explored through the focal lenses of systems thinking (ST), socio–technical systems (STS) and model–based systems engineering (MBSE) paradigms. Initially aimed at providing intervention for casualties and aerial support to emergency rescue workers on the ground in the event of a hurricane disaster around the Gulf shore of the Mobile bay area, Mobile city, AL., the scope of this research subsequently expanded to include a global outlook. Thereafter, culminating in the development of a generalized AAMPDR system model …

Bridging The Gap: Care Team’S Perspectives On Technology And Ai Integration In Healthcare, Sarah Fernandes, Pranathi Boyina, Awatef Ergai Dr., Wellstar Health System, Mohammad Yousef Mousa Naser, Sylvia Bhattacharya Dr.

Symposium of Student Scholars

As healthcare systems increasingly integrate digital solutions, understanding the perspectives of frontline healthcare workers on technology adoption is critical. This study explores how Registered Nurses (RNs), Licensed Practical Nurses (LPNs), and Certified Nursing Assistants (CNAs), collectively referred to as the Care Team, interact with existing and emerging healthcare technologies, including artificial intelligence (AI). Given the growing reliance on digital tools for clinical and administrative tasks, this research examines the challenges and benefits perceived by healthcare professionals when incorporating AI-driven solutions into their workflows.

A cross-sectional research design was employed, involving 30 semi-structured interviews with Care Team members from an Intensive …

Carbon Accountability Scores: A Process-Oriented Approach For Carbon Offsetting Using Ai Agents, Joshit Mohanty, Vaishali Vaishali

Graduate Student Government Association Research Conference

Conventional carbon offset programs rely on quantified emissions to determine balancing requirements. While this approach offers a standardized means of measuring carbon output, it often provides industries with a loophole—allowing them to offset their emissions by purchasing equivalent credits for activities such as tree planting rather than tackling inefficiencies at the source. This research proposes a process-based framework called Carbon Accountability Scores (CA scores) to offer a proactive strategy for assessing and reducing carbon footprints. Instead of focusing on the mere balancing of emitted and sequestered carbon, CA scores integrate an organization’s operational processes into the calculation, thereby offering the …

Digital Thread: Bridging Macro–Micro Services In System-Of-Systems, Joshit Mohanty

Graduate Student Government Association Research Conference

Organizations and industries increasingly rely on distributed services in decentralized environments—ranging from large-scale, system-of-system architectures to fine-grained, agent-based microservices. While this distributed paradigm offers flexibility and innovation, it presents critical challenges such as interoperability gaps, inconsistent data formats, and a lack of holistic oversight. Traditional integration approaches, including ad-hoc middleware or enterprise service buses, tend to solve these issues reactively. As a result, technical debt accumulates, stakeholder misalignments persist, and scaling to new demands becomes complex.

This research proposes digital thread (DT) as the unifying framework to create an authoritative source of truth: a continuous flow of information across the …

Meta-Clustering For Specialized Language Models: Enhancing Contextual Adaptation And Mitigating Hallucinations In Diverse Healthcare Environments, Joshit Mohanty, Vaishali Vaishali, Sandeep Kumar Nayak, Sumit Lahiri

Graduate Student Government Association Research Conference

Large Language Models (LLMs) have significantly advanced conversational AI by enabling dialogic information-seeking and task execution across diverse domains. However, their extensive parameters and broad domain scope lead to “data hallucinations.” These shortcomings are particularly evident in dynamic and diverse environments like India’s healthcare sector, where myriad languages, regional practices, and cultural nuances demand specialized, localized expertise rather than one-size-fits-all generalist models. This paper introduces a meta-clustering framework that integrates Distilled Language Models (DLMs) and Small/Specialized Language Models (SLMs) with meta-learning principles to address these limitations. By drawing on evidence from works such as MedHalu and Med-HALT, the framework seeks …

Tamos: Task-Aware Multi-Agent Orchestrator System, Joshit Mohanty, Sandeep Kumar Nayak, Sumit Lahiri

Graduate Student Government Association Research Conference

Large language models (LLMs) are increasingly at the core of multi-agent systems (MAS). However, the high resource demand, error propagation, and lack of adaptive evaluation mechanisms pose significant challenges in deploying these agentic solutions at scale. To address these concerns, this research proposes a Task-Aware Multi-Agent Orchestrator System designed to refine the agentic framework, categorizing tasks autonomously, assigning specialized evaluation datasets, and balancing token usage against functional effectiveness. This approach underscores robust data management, including AsyncHow, Mosaic AI, and Synthetic Preference Optimization (PO) corpora. Each dataset targets specific dimensions of agent performance, such as dynamic task decomposition and tool integration …

Graphtreemed: A Hybrid Graph-Tree Rag Architecture For Mission-Critical Medical Applications, Joshit Mohanty, Sandeep Kumar Nayak, Sumit Lahiri

Graduate Student Government Association Research Conference

Studies within engineering management indicate that decision-making is often based on the cognitive processing of grouped and pictographic information clusters entangled with high-level pattern recognition. Similarly, graph-based retrieval-augmented generation (RAG) architectures substantially improve diagnostic accuracy and interpretability, while tree-structured systems reduce critical misses through hierarchical reasoning. However, existing solutions often lack a unified framework that seamlessly integrates these two paradigms to address the multifaceted demands of mission-critical healthcare settings. This proposal introduces GraphTreeMed, a novel hybrid RAG architecture designed to harness the complementary strengths of graph-based and tree-based retrieval mechanisms, thereby advancing the safety and efficacy of clinical decision support …

Network Analysis Of Sociotechnical Systems, Siva Kadiresan

Engineering Management & Systems Engineering Theses & Dissertations

Sociotechnical networks have become integral frameworks in the accomplishment of organizational objectives by increasingly diverse, cross-functional, and technology-enabled modern teams. In cross-disciplinary teamwork, multiple mediating and moderating factors appear to lend context to the effects of team diversity faultlines on team outcomes. In the several decades of research into teamwork from a network perspective, there has been a paucity of empirical studies that examine how team transition processes and cross-disciplinary connections influence the impact of faultlines on team performance. This study investigates the association among perceived teamwork effectiveness, transition processes, cross-functional team connections, and faultlines in the context of sociotechnical …

A Theoretical Framework For Examination Of Context In Complex System Governance, Meggan M. Schoenberg

Engineering Management & Systems Engineering Theses & Dissertations

“A complex system’s identity and viability are directly related and affected by its context. It is important to identify, monitor, and manage (or mitigate risk) system contextual elements” (Keating C. B. et al., 2022, p. 209). Despite this importance, there is very limited research and literature on complex systems context. This research seeks to expand our understanding of complex system context and improve our ability to govern complex technology development programs effectively. The ability to analyze complex systems and their problems is necessary for this improvement.

A “clear understanding of the specific complex system context is fundamental to the process …

A Sysml V2 Implementation Of A Traceability And Verification Metamodel For “-Ilities”, Pacifique Munezero

Engineering Management & Systems Engineering Theses & Dissertations

Multidisciplinary knowledge and exchange of information are two of the most important aspects of complex system design within the Model-Based Systems Engineering (MBSE) domain. The next generation of systems modeling language, SysML v2, is being developed to improve the precision, expressiveness, interoperability, consistency, integration of the language concepts relative to SysML v1, and the implementation and maturation of MBSE approaches. This research focuses on using SysML v2 to model structured notations for -ilities, referred to as non-functional requirements (NFRs), to address how they can be treated with the same rigor as functional requirements and mapped directly and explicitly from the …

On The Provenance Of Software Systems: Automating Software Traceability With Knowledge Graph And Large Language Model Synergy, Tyler Procko

Doctoral Dissertations and Master's Theses

The present dissertation delineates a system that enables those engaged in software development to automatically generate and maintain project life cycle provenance. All projects are implemented and made manifest with the development of artifacts, e.g., papers, code files, etc. Tools exist to accelerate artifact creation, but little focus is paid to the processes that produce them. In terms of Ontology, or, from Ancient Greek, the study of being, the two most basic entities in reality are Continuant and Occurrent, or, roughly, “Artifact” and “Process”. This dissertation posits that for any created artifact, its process of creation, i.e., its life …

A Comparative Study Of Electronic And Paper Ballot Systems In Modern U.S. Elections, Gianna M. Wadowski

Open Access Master's Theses

In-person voting processes that rely on paper ballots have long dominated voting in the U.S. However, following the implementation of the Help America Vote Act in 2002, states rapidly adopted new voting technologies that dramatically changed the in-person voting experience. Since then, states have continued to adopt new voting technologies as new challenges and opportunities have emerged. Although scholarship has demonstrated that new voting technologies can offer benefits, reported improvements to the in-person voting experience are inconsistent. Despite changes in voting equipment and voting methods, voters continue to wait in long lines, affecting turnout and voter confidence. Using observational time …

Accuracy Of Time Phasing Missile And Munition Using The Continuous Distribution Function, Joseph Zobler

Theses and Dissertations

Accurate cost and schedule estimates are crucial for maintaining the U.S. military’s technological and operational superiority, ensuring efficient resource allocation and timely development of advanced defense systems. This research examines S-curve models for time-phasing non-recurring Research, Development, Test, and Evaluation (RDT&E) expenditures in missile and munition acquisition programs. This research evaluates the commonly used 60/40 rule, which assumes 60% of expenditures occur by 50% of the schedule, for its accuracy using Cost Assessment Data Enterprise (CADE) and Earned Value Management Central Repository (EVM-CR) data from 21 missile and munition development programs.