Digital Commons Network^™

Structural And Magnetic Characterization Of A Magnetite Nanoparticle-Based Aggregation Assay And Its Application To In Vitro Cytokine Monitoring, Gabrielle Rose Moss

Dartmouth College Ph.D Dissertations

Magnetic particle spectroscopy (MPS) based aggregation assays rely on target induced changes in functionalized magnetic nanoparticle (fMNP) hydrodynamic diameter and/or changes in fMNP aggregation level to enable target detection. fMNP target binding can be modeled via Brownian and Néel relaxation. We are engineering an MPS-based aggregation assay to sense tumor necrosis factor (TNF)-⍺ in a 3D melanoma tissue culture to aid in the development of immunotherapies for cancer treatment. TNF-⍺ is an inflammatory cytokine, whose production can be linked to cell death.

The first part of this thesis investigates the effects of ligand density, off-target proteins, and salts on fMNP …

Predicting Safety And Mobility Parameters Based On Comprehensive Analytics Of Connected Vehicle Data, Lei Han

Graduate Studies Theses and Dissertations 2026

Traffic safety and mobility remain critical challenges for modern transportation systems. The emergence of connected vehicle (CV) data offers unprecedented opportunities to capture microscopic, non-aggregated driving dynamics to support precise and finer-grained traffic safety and mobility research. This dissertation develops a comprehensive CV data–driven analytical framework to advance traffic safety and mobility research across multiple roadway contexts, including intersections, segments, freeways, and urban arterial networks. For traffic safety, this research extracts both longitudinal and lateral risky driving behaviors from CV trajectories and integrates them with macro-level roadway, traffic, and visual environment features. A spatial machine learning framework is proposed for …

A Dataset-Centric Analysis Of Evaluation Protocols In Face Morph Attack Detection, Sheerko R. Hmasalah, Abdulbasit K. Al-Talabani, Aras T. Asaad

Polytechnic Journal

Face morph attack detection (MAD) is often evaluated using datasets collected from diverse sources, created for different purposes, and acquired under varying conditions. This heterogeneity raises questions about how evaluation protocols and dataset characteristics influence reported performance. This work adopts a dataset-centric perspective to analyze the effects of evaluation protocol and dataset composition, including visual quality and morph-to-genuine imbalance, on MAD performance. Experiments are conducted on eight datasets and four morphing techniques using a fixed pipeline evaluated across six backbones, including Vision Transformer (ViT) and five CNN architectures, and ten evaluation scenarios covering homogeneous and heterogeneous cross-morph generalization. Performance is …

Construction-Phase Leed Compliance In Egypt: Development Of A Rule-Based Expert System For Contractor Decision Support, Mohamed R. Ahmed, Atef A. Ragab, Sameh Y. Mahfouz

Mansoura Engineering Journal

This study develops a rule-based expert system implemented in Python to support contractors in achieving LEED BD+C: New Construction v4 certification during the construction phase in Egypt. The research identifies and validates construction-phase obstacles affecting LEED compliance through a structured questionnaire targeting building contractors in Egypt. The questionnaire process was conducted in two stages: a pilot survey was used to screen and refine the initially identified obstacles, while the main survey was used to validate the practical relevance of the retained obstacles within the Egyptian construction context. In parallel, the study analyzes construction-phase prerequisites and credits relevant to contractors through …

Bi-Level Optimization Of Peer-To-Peer Trading In A Decentralized Energy Market, Marshal Miezah

Graduate Theses, Dissertations, and Problem Reports (ETD)

Distributed power generation based on rooftop photovoltaic (PV) systems integrated with battery storage emerges as a promising pathway for reducing greenhouse gas emissions and im- proving flexibility in modern power systems. This study develops a bi-level optimization model to examine how prosumers maximize profit through peer-to-peer (P2P) energy trading with consumers and the grid, and how consumers minimize cost by leveraging P2P trading. The bi-level problem is reformulated as a single-level mixed-integer programming (MIP) model using Karush- Kuhn-Tucker (KKT) conditions to improve tractability and preserve market-clearing behavior. For the model validation and case study development, prosumer and consumer data are …

Photocatalytic Degradation Of Cephalexin, Amoxicillin, And Phenol Red Using A Green-Synthesized Folic Acid–Zno–Bi2o3 Nanoporous System, M. Sarani, Haghighi, P. Tamaddon, A. Najafidoust, P. Faghani-Eskandarkolaei, N. Sattarahmady

Research outputs 2022 to 2026

The persistent presence of pharmaceutical pollutants like amoxicillin (AMX) and cephalexin (CPX), and industrial dyes such as phenol red (PR) in wastewater poses serious ecological and public health risks, including antibiotic resistance. It necessitates efficient and low-cost remediation technologies. Photocatalysts employing innovative and engineered nanomaterials such as bismuth oxide (Bi2O3) and zinc oxide (ZnO) nanostructures exhibit considerable potential for eliminating pollutants. This study focused on the photodegradation of AMX, CPX, and PR utilizing green-synthesized folic acid–ZnO–Bi2O3 nanoparticles (F-Zn-Bi NPs) under visible light irradiation. Characterization of the synthesized photocatalyst was conducted using various techniques of powder x-ray diffraction (PXRD), field emission …

Managing Green And Sustainable Construction Projects: A Computational Analysis Of Associated Risks And Challenges For Improved Performance, Bahaa Chammout, Islam H. El-Adaway

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Green and sustainable building practices have emerged as effective strategies to address the environmental challenges posed by the construction industry. This includes unsustainable resource utilization, excessive waste production, greenhouse gas emissions, and high energy consumption. However, the integration of green and sustainable initiatives in construction introduces distinct risks and challenges that affect project management and may lead to claims and disputes. To this end, and while previous research examined dispute causation across various aspects of the construction industry, there is limited investigation of the unique and complex nature of risks and challenges - and consequently conflicts, claims, and disputes - …

Managing And Accelerating The Circular Economy Transitions Within The Construction Value Chain Using Network Governance And Game Theory Systems Perspectives, Radwa Eissa, Islam H. El-Adaway

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

The fragmented nature of the construction industry has hindered its transition toward circular economy (CE) practices. While game theory (GT) has been successfully applied to support the strategic decisions of CE transitions in other sectors, CE-GT models in the construction domain remain extremely limited. This paper addresses two research questions: (1) how are CE network governance activities currently modeled using GT across domains? (2) How can such models be transferred and adapted to solve strategic interactions within the construction value chain in a manner that enables CE practices to emerge as equilibrium outcomes? These questions are driven by the need …

Solving High-Dimensional Differential Equations Using Recurrent And Residual Neural Network Architectures, Hind Khaled Kolaib

Knowledge Engineering and Data Science

High-dimensional Partial Differential Equations (PDEs) form the foundation of complex process modeling in various scientific and engineering applications, including finance, physics, and optimal control. However, classical numerical methods are adversely affected by the curse of dimensionality, making them inapplicable for large-scale problems. Recently, however, deep learning-based approaches have provided a new toolbox for these high-dimensional PDEs, including methods such as the Deep Backward Stochastic Differential Equation (Deep BSDE) method. Our approach draws on a more sophisticated deep learning backbone, using neural networks (in our case, a Residual Neural Network and a Long Short-Term Memory network (LSTM) integrated into the Deep …

Integrating Stakeholder Priorities Into Resilience Planning And Investment Decision-Making, Victoria Jeaneen Kraemer

Doctoral Dissertations

"Resilience is a system’s ability to bounce back from a disruption. However, resilience needs may vary across different stakeholders. To better integrate stakeholder input into resilience planning and investment decision-making, existing frameworks were combined to develop the Integrated Stakeholder-Centered Resilience Framework. This broad framework is tailorable across contexts and stakeholder types. Research results demonstrate the use of this framework to evaluate the effects of disruptions and represent stakeholder preferences for investments. In the first study, participants were randomly assigned to power outage scenarios, which did not affect their criteria priorities. An exploratory factor analysis revealed two main priorities, (1) Absorption …

Texels: A Programmable Textile Interface For Replicating Textures, Maya E. Eusebio

Honors Undergraduate Theses

Self-moving fabric interfaces have massive potential for applications in fields ranging from art to haptic feedback to deployable space structures. However, current systems of implementation face the impracticalities of bulkiness, burnout, and energy consumption on top of limiting designs that can only contract uniformly or create one pre-programmed shape. For this technology to bring the change that it promises, we must break the barriers of usability and sustainability to make it a practical choice. This thesis aims to develop a scalable model of fabric that designers, programmers, and anyone else can acquire or create with accessible materials, integrate into design …

Evaluation Of The Long-Term Sealability Performance Of Aramid Fiber Nitrile-Based Gaskets, Salome Ramirez

Honors Undergraduate Theses

The reliability of sealed, oil-filled containers is critical for maintaining the operation of high-voltage energy systems, in which the sensitive electronics need to be isolated from the cyclic conditions associated with outdoor environments. New materials bring the possibility of extending service lifetimes and maintenance intervals. Aramid fiber nitrile-based gaskets are an attractive alternative to conventional gasket choices due to their long-term stability in various environments and compatibility with porcelain, metals, and plastics. For newly designed porcelain-to-metal fittings, understanding the behavior of the candidate gasket material under realistic loading conditions is essential to ensure long-term seal integrity and equipment safety. Traditional …

Multi-Parameter Optimization And Adaptive Temperature Compensation For Fbg Strain Sensors In Wide-Temperature-Range Aerospace Applications, Ruling Zhou, Jiacheng Yu, Yutang Dai, Jianguan Tang, Minghong Yang, Farhan Mumtaz

Electrical and Computer Engineering Faculty Research & Creative Works

Accurate strain measurement in cryogenic fuel pipelines is crucial for ensuring the structural integrity and reliability of rocket engine systems operating under extreme thermal conditions. Fiber Bragg grating (FBG) sensors show significant potential for such applications; however, their inherent temperature-strain cross-sensitivity limits performance over wide temperature ranges. This research presents an enhanced compensation strategy combining multi-parameter optimization with temperature-zone-specific adaptation to improve the accuracy and stability of FBG-based strain sensing in harsh aerospace environments. Four special steel substrate materials including S03, S06, S07, and 1Cr18Ni9Ti, were evaluated using strain transfer theory and thermo-mechanical coupling simulations. Genetic algorithms optimized key design …

Safe Optimal Control Framework For Cooperative Manipulation Of Objects In Human–Robot Teams, Irfan Ganie, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This article introduces a distributed deep neural network (NN)-based adaptive control framework for cooperative object manipulation in human–robot teams with unknown agent dynamics by using three distinct multilayer NN observers (MNNOs). The first observer, termed the reference point estimator, enables each robotic agent to estimate the object's reference center using consensus-based learning, even without direct access to global reference trajectories. The second observer, referred to as the human force-to-trajectory estimator, uses human-applied forces to infer the intended position, velocity, and acceleration of the object, enabling real-time estimation of human intent. Together, these two observers allow distributed estimation of human-intended motion. …

Waveguide-Assisted Single-Mode Fiber Bragg Gratings In A Highly Multimode Coreless Fiber Via Femtosecond Laser Inscription For Extreme Temperature Quasi-Distributed Thermal Sensing, Farhan Mumtaz, Koustav Dey, Bohong Zhang, Jeffrey D. Smith, Ronald J. O'Malley, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

This research reports a potential quasi-distributed thermal mapping optical sensing system for extreme temperatures, leveraging femtosecond (fs) laser inscribed single-mode fiber Bragg gratings (FBGs) and a waveguide within coreless, highly multimode optical fiber, resulting in a single-mode structure. Unlike doped single-mode fibers, coreless fibers composed of silica rods prevent issues associated with dopant migration and ensure data accuracy. The strategic placement of point-by-point FBGs in a cascaded formation on the fs-laser inscribed waveguide facilitates localized multipoint sensing. The long-term stability of the proposed waveguide-assisted FBG system was assessed over 24 hours at elevated temperatures (1000°C), showing no hysteresis during heating …

Self-Calibrating Uav Navigation: Reinforcement Learning Approaches For Horizontal Trajectory Estimation, Shirin Nasr-Esfahani, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

Accurate unmanned aerial vehicle (UAV) trajectory estimation is essential for autonomous navigation, particularly in GPS-denied environments. Visualodometry and simultaneous localization and mapping (SLAM) approaches require precise camera intrinsic parameters, which are typically obtained through predefined or offline calibration. Instead, in this work, we propose a reinforcement learning (RL)-based self-calibration framework that estimates camera intrinsic parameters directly from monocular video sequences, without requiring prior knowledge of the camera, environment, or calibration targets. This intrinsic parameter estimation is then leveraged to achieve robust UAV trajectory estimation using only video data. We formulate the problem as a sequential decision-making task, where an RL …

Performance Analysis And Optimization Of Fructose Memristor-Based Neuromorphic Systems, Harshvardhan Uppaluru, Shakil Mahmud Jiban, Shah Zayed Riam, Feng Zhao, Jinhui Wang

Electrical and Computer Engineering Faculty Research & Creative Works

Natural organic memristors have demonstrated promising synaptic behavior, positioning them as strong candidates for synaptic devices in neuromorphic systems. This paper presents the fabrication and evaluation of a neuromorphic system based on natural organic 16-level and 32-level fructose memristors. First, the manufacturing process of fructose memristors is described in detail. Second, the nonlinear property associated with fructose memristors is investigated, and an optimization method is applied to address the nonlinear effects. The performance of the fructose memristor-based neuromorphic system on MNIST with a multi-layer perceptron and on CIFAR-10 with VGG-8 is evaluated and reported under various conditions -with/without nonlinearity optimization …

Enhancing Measurement Accuracy: The Impact Of Missing Data On Parameter Estimation In Mass-Spring-Damper Systems, Michkath Omanda Bouraima, Steven Thompson, Maciej J. Zawodniok

Electrical and Computer Engineering Faculty Research & Creative Works

In sensor-driven dynamic systems, missing data can severely degrade parameter estimation accuracy. This article investigates the impact of missing data on phase estimation in a mass-spring-damper system using an information-theoretic framework based on the Cramér-Rao Lower Bound (CRLB). Closed-form CRLB expressions are derived for four scenarios: complete data, missing completely at random (MCAR) deletion, MCAR-based imputation, and missing at random (MAR) missingness via a selection-weighted formulation. These bounds are used as theoretical benchmarks to evaluate classical imputation methods (last observation carried forward (LOCF), linear interpolation) and advanced approaches (Kalman filtering, Rauch-Tung-Striebel (RTS) smoothing, Bayesian inference, and transformer-based imputation) through Monte …

Rf-Attennet: A Hybrid Attention-Enhanced Network For Mixed Signal Classification In Uav Swarm Detection, Prajoy Podder, Mohammad Atikur Rahman, Maciej Zawodniok, Sanjay Madria

Electrical and Computer Engineering Faculty Research & Creative Works

The continuous increase of UAVs, particularly in swarms, creates significant challenges for security and airspace regulation. Traditional RF fingerprinting methods struggle to detect and classify UAV swarms due to overlapping signals and interference. This study introduces RF-AttenNet, a hybrid deep learning model designed to classify mixed UAV signals by analyzing composite RF spectrograms. RF-AttenNet uses dual attention mechanisms, channel and spatial attention to focus on critical spectral features, enabling the model to effectively separate and identify overlapping UAV signals. We have developed custom composite UAV datasets that simulate real-world swarm interference, incorporating both single and mixed UAV classes. RF-AttenNet achieves …

Fixed-Time Consensus Tracking For Nonlinear Multi-Agent Systems Under Aperiodically Intermittent Control, Lei Xue, Tong Wu, Wenwen Jia, Donald C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

This article explores the problem of fixed-time consensus tracking (FT-CT) for nonlinear multi-agent systems utilizing the a periodically intermittent control (AIC) strategy. In contrast to existing control algorithms, the proposed algorithm utilizes the AIC strategy instead of the conventional continuous-time control strategy, effectively reducing the consumption of communication resources. Moreover, the problem of intermittent FT-CT is well handled by proposing the average control rate of the AIC strategy. Two theorems based on the cases of directed and undirected graphs are proposed, respectively. Finally, the validity of these results is confirmed through numerical simulations on a general nonlinear system and a …

Sapphire Optical Fiber Bragg Grating Sensors Based On Dispersive Microwave-Photonic Frequency-Time Domain Analysis, Ruimin Jie, Chen Zhu, Farhan Mumtaz, Koustav Dey, Bohong Zhang, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

Sapphire fiber Bragg gratings (SFBGs) have attracted growing interest for high temperature sensing in harsh environments, yet their interrogation typically relies on optical spectrum measurements, demanding a high-resolution optical spectrum analyzer (OSA) that is bulky, expensive, and constrained in acquisition speed. Moreover, the inherently multimode nature of sapphire fiber further complicates spectrum-based demodulation, thereby limiting the achievable sensing resolution. In this paper, we propose and experimentally demonstrate a microwave-photonic interrogation approach for SFBG sensors. Instead of measuring the optical reflection spectrum, the complex frequency response in the microwave domain of an SFBG is acquired using a vector network analyzer (VNA) …

Resolving Stiffness Trade-Offs In Simultaneous Pressure And Vibration Sensing Using A Corrugated-Tube Fiber-Optic Sensor, Yizheng Chen, Yan Tang, Jie Huang, Qi Zhang, Biyao Shi, Zewei Wu

Electrical and Computer Engineering Faculty Research & Creative Works

This article proposes and experimentally demonstrates a corrugated-tube-based fiber-optic sensor capable of measuring pressure, vibration, or both simultaneously. To address the limited sensitivity of conventional diaphragm-based designs, the sensor incorporates an optimized corrugated tube that balances the conflicting stiffness requirements for pressure and vibration measurements. The corrugated tube, acting as a mechanical transducer, is integrated with an extrinsic fiber-optic Fabry–Perot interferometer (EFPI). The EFPI cavity is formed between a reflective surface at the sealed end of the corrugated tube and the cleaved end face of an optical fiber fixed within a mounting assembly. In this configuration, displacement of the corrugated …

Nash Equilibrium Strategies For Multicluster Pursuit–Evasion Game With Disturbances: A Prescribed-Time Convergence Approach, Lei Xue, Xian Yu, Yongbao Wu, Jian Liu, Changyin Sun, D. C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

This article investigates the problem of prescribed-time Nash equilibrium (NE) seeking for a multicluster pursuit–evasion game (PEG) subject to external disturbances. To mitigate the impact of disturbances and reach the NE within a user-defined prescribed time, a prescribed-time disturbance observer (PTDO) is devised to estimate and compensate for them. Based on this observation, a novel control algorithm is developed, which facilitates collaboration among multiple pursuers to capture multiple evaders within the prescribed time. It is theoretically demonstrated that the designed algorithm ensures prescribed-time convergence to the NE of the multicluster PEG with disturbances. Finally, numerical simulations are conducted to verify …

Real-Time Multiregional Market-To-Market Congestion Management Through Exchange Of Relief Cost Curve, Haotian Chen, Yonghong Chen, Jose Daniel Lara, Jarrad Wright, Matthew Bossart, Sebastian De Jesus Machado, Rui Bo

Electrical and Computer Engineering Faculty Research & Creative Works

This paper introduces a novel method for multiregional market-to-market (M2M) coordinated congestion management. It identifies shortcomings in existing M2M approaches, where Regional Transmission Organizations (RTOs) exchange shadow prices and relief requests to optimize congestion relief allocations across interconnected regions. Two methods are proposed to enhance flow and price convergence. The first method proposes that both Regional Transmission Organizations (RTOs) use state-estimator flows directly to determine relief requirements, eliminating delays and potential oscillations caused by using market flows calculated from the prior period under existing M2M approach. The second method involves exchanging transmission relief cost curves, enabling each RTOs to integrate …

Transformer-Customer Relationship Identification Based On Load-Switching Fluctuation Characteristics Considering Same-Feeder-Adjacent-Transformer Condition, Yanan Zhang, Gan Zhou, Yuyuan Liu, Wei Gu, Yanjun Feng, Yujue Wang, Rui Bo

Electrical and Computer Engineering Faculty Research & Creative Works

Accurately identifying the connectivity between transformers and downstream three-phase customers in low-voltage distribution networks is challenging, because voltage curves of different phases and nearby nodes can be weakly distinguishable, especially when adjacent transformers on the same feeder serve geographically close customers with highly similar voltage curves. This paper proposes a novel method based on load-switching fluctuation characteristics recorded by smart meters. By extracting localized current and voltage fluctuations and establishing correlation matching, the method overcomes the limited discriminability using steady-state measurements. The method operates in two stages: first, switching-induced fluctuation characteristics are extracted and matched to cluster customers by the …

Lidar-Based Framework For Detecting Suspicious Human Activities, Ahd Aljumah, Charalampos Antoniadis, Hakim Ghazzai, Nawfal Guefrachi, Ahmad Alsharoa, Gianluca Setti

Electrical and Computer Engineering Faculty Research & Creative Works

This study explores the development of Human Activity Recognition (HAR) systems capable of identifying suspicious activities to enhance security in public spaces. We propose an innovative solution that integrates LiDAR sensors with deep learning technologies. Our method employs advanced models operating on LiDAR point cloud, PV-RCNN for human detection, and LidarGait++ for classifying activities into categories such as standing or walking (non-suspicious) and sneaking or fighting (suspicious). Due to the scarcity of suitable real-world datasets for training such systems, we utilize a 3D simulation tool, Blender, to create realistic environments and generate labeled point cloud data. This synthetic dataset allows …

Event-Based Predefined-Time Synchronization For Complex Networks With Deception Attacks: An Asynchronously Intermittent Strategy, Lei Xue, Jiong Yu, Haoyu Zhou, Yongbao Wu, Jian Liu, Donald C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

This article studies the practical predefined-time synchronization (PPTS) for complex networks (CNs) under deception attacks based on the asynchronously intermittent event-triggered control (AIE-TC). Notably, AIE-TC effectively integrates the advantages of asynchronously intermittent control (AIC) and event-triggered control, where AIC provides each subsystem node with independent control and rest intervals. Besides, all synchronization errors of the CNs converge to an adjustable neighborhood within the predefined time by designing a bounded time-varying function into the controller. Moreover, this article considers that the transmission network is subjected to stochastic deception attacks modeled by a Markov process, which captures the state-driven dynamic transition characteristics …

Synergistic Removal Of Microplastic Fibres: Integrating Chitosan Coagulation In Hybrid Water Pre-Treatment Systems, Jayasuriya Arachchilage Nimesha Thathsarani

Theses: Doctorates and Masters

Microplastic fibres (MPFs) are the most prevalent form of microplastics detected in the influents of water and wastewater treatment plants. Owing to their elongated shape and low density, MPFs frequently evade removal even during tertiary processes such as membrane filtration. Enhancing pretreatment, particularly through cost-effective coagulation-flocculation can reduce the treatment burden on downstream systems. However, conventional inorganic coagulants often necessitate high dosages and can leave residual ions, posing environmental concerns.

As a sustainable alternative, the use of environmentally friendly coagulants such as Chitosan has gained attention for MPF removal. While Chitosan exhibits favourable qualities, its practical application is hindered by …

Artificial Neural Network Based Protection For Dc Bus Using Solid State Dc Breaker, Eswaraiah Giddalur, Askani Jaya Laxmi

ASEAN Journal on Science and Technology for Development

Low voltage Direct Current (LVDC) microgrids have been extensively used as a hopeful technology with high access to distributed energy resources (DERs) and DC loads, due to the absence of multiple conversions. A high amplitude Fault current together with the vulnerability of electronic devices, poses a great challenge to the line protection in LVDC microgrids. Electromechanical switches are slow and take long tripping time to clear the short circuit fault. Power electronic circuit breakers are used to achieve fast fault interruption recent progress in areas like machine learning and natural language processing have affected nearly every assiduity and area of …

Beyond The Meter: Frameworks For Evaluating The Broad Spectrum Of Microgrid Benefits, Elias Henderson

Cal Poly Humboldt theses and projects

Community-scale microgrids can create a range of social benefits. These include resilient electricity, cleaner air, decarbonization, and economic development, alongside more qualitative values like energy sovereignty and community self-determination. Many such benefits, however, are illegible to California’s market and regulatory frameworks. This thesis traces this disconnect, with particular attention to grid-edge communities in Northern California.

Microgrid benefits are classified as monetized, modeled, or incommensurable according to their inclusion/exclusion in existing regulatory and revenue mechanisms. To explore each category, a mixed-methods analytic approach is employed, pairing Tribal and stakeholder interviews with the development of a microgrid valuation calculator. This tool is …