Physical Sciences and Mathematics Commons^™

Climate And Environmental Controls On Forest Soil Organic Matter Cycling, Genevieve Goebel

Dartmouth College Ph.D Dissertations

Climate change factors act on the soil environment in complex ways, known to disturb soil organic carbon (C). Rising global temperatures and alterations to regional precipitation and nitrogen deposition regimes may contribute to the intensification of global change by accelerating the rate of biological decomposition and CO2 efflux to the atmosphere. The magnitude and endurance of the disturbance to soil organic C across landscapes and throughout time that climate change factors may pose to soil C cycling are not comprehensively understood. These knowledge gaps in our field make it challenging to anticipate how soil environments and the vast ecosystems that …

Basis Design For Electronic Structure And Beyond, Weishi Wang

Dartmouth College Ph.D Dissertations

At the intersection of quantum physics, quantum chemistry, and materials science, electronic structure is the study of electrons in solid-state and molecular systems. Electronic-structure computation relies on discretizing the many-electron Hamiltonian with a finite single-particle basis set. However, basis-set construction is conventionally treated as an ad hoc preprocessing step. This thesis develops an expressive and flexible framework for active, system-oriented basis-set design and numerical modeling strategies that treat basis functions as tunable representations to encode electronic ground-state information.

We first introduce a multi-layered, differentiable basis-construction framework that embeds a set of primitive parameters into mixed-contracted Gaussian-type orbitals. We then develop …

Active Galactic Nuclei Across Scales: From Blazar Jets To Dark Matter Halos, Stephanie Ann Podjed

Dartmouth College Ph.D Dissertations

Within ΛCDM, galaxies form and evolve within dark matter halos, with both central and satellite galaxies contributing to the total stellar mass content. Virtually every massive galaxy harbors a supermassive black hole (SMBH) at its center, with a small fraction of the population growing by actively accreting material; we call this an active galactic nucleus (AGN). The processes giving rise to an AGN phase (i.e., growth of a SMBH) releases enormous amounts of energy in the form of radiation, outflows, and relativistic jets that are able to significantly affect the formation and evolution of galaxies. Blazars are an extreme class …

Generating Template Banks For Intermediate Duration Gravitational Waves, Michael St. Pierre

Open Access Dissertations

There are a multitude of astrophysical sources which carry the potential to produce perturbations in spacetime, known as gravitational waves, across a wide spectrum of frequencies. Over the brief history of gravitational wave astronomy, short-duration “chirp” signals emitted from black hole mergers, highly energetic “burst” signals emitted from supernovae and Gamma-Ray Bursts (GRBs), and long duration “continuous” signals emitted from galactic neutron stars and the stochastic gravitational wave background have traditionally been at the forefront of search development, leaving a class of intermediate duration  ($10² - 10⁴ seconds) gravitational wave signals poorly suited for detection. The conventional way …

Optimizing Markov Chain Monte Carlo Algorithms For Fairness, Scalability, And Interpretability In Electoral Redistricting, Madhukara Kekulandara

Open Access Dissertations

Gerrymandering undermines democratic representation by manipulating electoral district boundaries to dilute the political influence of targeted communities. While Markov Chain Monte Carlo (MCMC) based redistricting algorithms have become a standard computational tool for detecting partisan gerrymandering, their effectiveness in addressing racial gerrymandering and their scalability on modern computing systems remain limited. This dissertation advances the theory and practice of algorithmic redistricting by optimizing MCMC-based approaches across three dimensions: racial fairness, computational efficiency, and interpretability.

First, this work introduces the Partial Map MCMC algorithm, a novel redistricting method designed specifically to address racial gerrymandering under the legal framework of Section 2 …

Linking Phytoplankton Physiology And Ecology To Global Biogeochemical Cycles: A Modeling Approach, Margaret Bernish

Open Access Dissertations

Phytoplankton, or microscopic primary producers, contribute to carbon export through the biological transformation of carbon dioxide into organic molecules. Phytoplankton are responsible for approximately half of global net primary production, while only contributing minimally to total photosynthetic biomass. Despite their important role in the global cycling of nutrients, phytoplankton physiology and ecology are often highly simplified in global biogeochemical models. This dissertation elucidates the role of phytoplankton physiology and ecology within biogeochemical cycles, allowing for better characterization of marine microbial distribution and better representation of nutrient cycling for interpretive and predictive models. The development of formulations that link physiological responses …

Submesoscale Dynamics Of Phytoplankton And Carbon Export Revealed By High-Resolution Airborne And Satellite Remote Sensing Of Currents And Ocean Color, Sarah E. Lang

Open Access Dissertations

Satellites and airborne sensors reveal submesoscale (1 - 10 km) variability in ocean color in the form of filaments, eddies, and patches. The variability in ocean color is closely tied to the physical dynamics that restructure phytoplankton distributions and drive active biological responses like changes in primary productivity and community structure. As the base of the marine food web and a key component of the biological carbon pump, phytoplankton are crucial to the overall health of marine ecosystems and to the ocean's role in climate. This dissertation focuses on the use of airborne and satellite remote sensing to study the …

Collaboration And Co-Management Ahead Of Permitting: Understanding How Actors And Their Interactions Lead To Non-Optimal Shoreline Projects, Juita-Elena (Wie) Yusuf, Mariana Saitgalina, Michelle Covi

School of Public Service Faculty Publications

Living shorelines are widely promoted as nature-based solutions to coastal erosion and wetland protection, yet hardened shoreline structures continue to dominate even in jurisdictions with explicit policy mandates prioritizing living shorelines. In this research, we examine why non-optimal shoreline modification outcomes persist in Virginia (USA) despite a regulatory framework designed to promote ecological alternatives. We use primary data from interviews with wetlands board members, marine contractors, and nonprofit organizations and findings from a secondary survey of shoreline property owners to analyze shoreline management as a multi-sector collaborative decision-making process. Findings show that shoreline outcomes are shaped less by individual regulatory …

Sea-Level Rise Adaptation And Collaboration In Polycentric Governance: A Comparison Of Three Regions, Francesca Vantaggiato, Mark Lubell, Matthew C. Nowlin, Juita-Elena (Wie) Yusuf, Majid Shafiee-Jood

School of Public Service Faculty Publications

Adaptation to sea-level rise confronts coastal communities worldwide with a new set of collective action problems that require collaboration. When does collaboration result in concrete action for adaptation? To address this question, we combine the Ecology of Games and Collaborative Governance frameworks using a comparative analysis of three coastal regions in the United States. We leverage original survey data from the San Francisco Bay Area in California (2018, N = 878), the Tri-County (Charleston) Area in South Carolina (2022, N = 152), and the Hampton Roads region in Virginia (2023, N = 153), three regions that differ in terms of …

Analysis Of Δ¹¹B As A Seawater Ph Proxy: Comparing Ocean Circulation Inverse Model Output With Marine Calcifier Geochemistry, Jesse I. Dong

CMC Senior Theses

Increasing anthropogenic carbon flux into the oceans decreases seawater pH, alters dissolved inorganic carbon speciation, and reduces biogenic calcification. The marine calcifiers— specifically corals and coralline algae—incorporate elements from surrounding seawater into their carbonate structures, which preserve past records of ocean carbon chemistry. In particular, boron in biogenic carbonate is a potentially valuable proxy for historical ocean pH across human timescales. Within seawater, boron primarily exists as boric acid B(OH)3 and borate ions B(OH)4 - , where higher pH favors the formation of borate ions. Borate ions preferentially incorporate the heavier ¹¹B isotope over 10B. On the other hand, if …

A Simple Statistical Tool To Correct The Daily Temperature Effect On Dielectric Soil Matric Potential Sensor Readings, Sebastián Bravo Peña, Meindert Commelin, Ole O. Wendroth

Plant and Soil Sciences Faculty Publications

High-resolution time series recorded by soil water dielectric sensors are often affected by other processes. Dielectric soil matric potential sensors, such as the TEROS 21, exhibit temperature sensitivity, resulting in deviations of readings from the true value due to soil temperature fluctuations. However, methods for correcting this effect remain limited. The objective of this study was to create a straightforward, scale-based statistical approach to mitigate the influence of daily temperature oscillations on matric potential dielectric readings. The temperature sensitivity correction function (TSCF) identifies, quantifies, and smooths diurnal fluctuations caused by soil temperature dynamics. We provide a detailed description of the …

How Should Ai Talk About Us? Llms And Social Generics, Tiffany A. Zhu

Philosophy Faculty Publications

How should AI-generated speech balance epistemic aims, such as precision and accuracy, with ethical and social considerations? This paper examines a subtle yet consequential aspect of LLM-driven communication: the use of generic generalizations that convey information about social groups (e.g., “immigrants work low-wage jobs”). While central to human epistemic and pedagogical practices, generics are theorized to reinforce stereotypes, essentialism, and injustice. Using ChatGPT-3.5 as a case study, I uncover tendencies for AI chatbots to inconsistently hedge and refuse generics, including those that reflect well-documented social structural patterns, such as “women are more likely to get attacked while walking alone at …

You Only Need One Stage: Novel-View Synthesis From A Single Blind Face Image, Taoyue Wang, Xiang Zhang, Xiaotian Li, Huiyuan Yang, Lijun Yin

Computer Science Faculty Research & Creative Works

We propose a novel one-stage method, NVB-Face, for generating consistent Novel-View images directly from a single Blind Face image. Existing approaches to novel-view synthesis for objects or faces typically require a high-resolution RGB image as input. When dealing with degraded images, the conventional pipeline follows a two-stage process: first restoring the image to high resolution, then synthesizing novel views from the restored result. However, this approach is highly dependent on the quality of the restored image, often leading to inaccuracies and inconsistencies in the final output. To address this limitation, we extract single-view features directly from the blind face image …

Qura: Reinforcement Learning Based Routing For Quantum Networks, Tasdiqul Islam, Engin Arslan, Md Arifuzzaman

Computer Science Faculty Research & Creative Works

Quantum routing deals with identifying a set of quantum repeaters to use to create entanglement between distant endpoints. Previous approaches proposed shortest-path and linear programming methods to find a solution to this problem. While the shortest path approach results in suboptimal performance, linear programming takes too long to find a solution as the network size and constraints increase. In this paper, we apply Deep Q-Reinforcement Learning (DQRL) to optimize routing in quantum networks both in terms of execution time and performance. The proposed Quantum Routing Algorithm (QuRA) first chooses which request to schedule among all requests. It then determines which …

Utility-Preserving Federated Graph Learning With Dual-Perspective Fairness, Renqiang Luo, Huafei Huang, Shuo Yu, Fengqi Yu, Feng Xia, Sajal K. Das, Chengqi Zhang

Computer Science Faculty Research & Creative Works

Fairness-aware federated graph neural networks (FedGNNs) necessitate consideration of both the server and the clients. However, fairness-aware methods struggle to enhance dual-perspective (i.e., server and clients) fairness without sacrificing utility due to the distributed learning framework. As a consequence, the utility sacrifices of fairness-aware graph learning methods are even exacerbated in federated frameworks. In this work we propose F3GL, a dual-perspective fairness federated graph learning method that enhances both global (for the server) and local fairness (for clients) while preserving utility. Through theoretical analysis, we delineate the similarity between original sensitive features and those after convolution under different spectra. Our …

Feddot: Defending Federated Learning Against Overwhelming Targeted Attacks, Priyesh Ranjan, Ashish Gupta, Federico Coro, Sajal K. Das

Computer Science Faculty Research & Creative Works

Federated Learning (FL), which facilitates collaborative model training and protects users' privacy, has drawn great interest from the research community. With FL, participants train their models on local data and submit the corresponding updates for aggregation to a server. While concealing the identities of the participants, FL may attract adversaries in order to hamper the underlying model. In this paper, we propose an FL framework, FedDOT, to defend against adversaries performing targeted attacks. FedDOT incorporates two powerful defense algorithms, Maximum Spanning Tree based attacker detection (MSTAD) and Densest graph-based attacker detection (Density-AD), which leverage correlation between weight updates and graph …

Dcmm-Transformer: Degree-Corrected Mixed-Membership Attention For Medical Imaging, Huimin Cheng, Xiaowei Yu, Shushan Wu, Luyang Fang, Chao Cao, Jing Zhang, Tianming Liu, Dajiang Zhu, Wenxuan Zhong, Ping Ma

Computer Science Faculty Research & Creative Works

Medical images exhibit latent anatomical groupings, such as organs, tissues, and pathological regions, that standard Vision Transformers (ViTs) fail to exploit. While recent work like SBM-Transformer attempts to incorporate such structures through stochastic binary masking, they suffer from non-differentiability, training instability, and the inability to model complex community structure. We present DCMM-Transformer, a novel ViT architecture for medical image analysis that incorporates a Degree-Corrected Mixed-Membership (DCMM) model as an additive bias in self-attention. Unlike prior approaches that rely on multiplicative masking and binary sampling, our method introduces community structure and degree heterogeneity in a fully differentiable and interpretable manner. Comprehensive …

Adversarial Robustness In Biomedical Time-Series Models, Rohan Tiwari

Bioengineering Theses

This study investigates adversarial vulnerabilities in deep learning models for biomedical time-series classification across two clinically important modalities: electrocardiography (ECG) and electroencephalography (EEG). Using the MIT-BIH Arrhythmia and CHB-MIT seizure datasets, I evaluate time-domain attacks (FGSM, PGD), Fourier-domain constrained attacks, and learned spectral perturbations designed to reveal modality-specific sensitivity patterns. Across both tasks, a consistent trend emerges low-frequency components (0–5 Hz) constitute a dominant axis of adversarial vulnerability, with perturbations in this range producing the steepest degradation in classification performance. In ECG models, protecting the physiologically relevant QRS band (5–20 Hz) significantly improves robustness, whereas EEG models remain highly sensitive …

Drivers Of Phytoplankton Bloom Interannual Variability In The Amundsen And Pine Island Polynyas, Guillaume Liniger, Delphine Lannuzel, Sébastien Moreau, Michael S. Dinniman, Peter G. Stutton

CCPO Publications

The Amundsen Sea Embayment (ASE) experiences both the highest ice shelf melt rates and the highest biological productivity in West Antarctica. Using 19 years of satellite data and modelling output, we investigate the long-term influence of environmental factors on the phytoplankton bloom in the Amundsen Sea (ASP) and Pine Island (PIP) polynyas. We test the prevailing hypothesis that changes in ice shelf melt rate could drive interannual variability in the polynyas' surface chlorophyll-a (chl a) and Net Primary Productivity (NPP). We find that the interannual variability and long-term change in glacial meltwater may play an important role in …

On The Contribution Of The Gulf Stream To High Frequency Coastal Sea Level Variability, Tal Ezer

CCPO Publications

Long-term Atlantic Ocean and Gulf Stream (GS) variability were linked in past studies to coastal sea level (CSL) change along the U.S. East Coast - a weakening GS can lead to rise in CSL and increased coastal flooding. However, high frequency variability (HFV) in CSL is, in most cases, attributed to atmospheric weather events. This study is focused on HFV (intraseasonal variations with periods between similar to 1 week and similar to 2 months) in the GS and in CSL. First, wavelet and spectral analysis of observations of the Florida Current transport and CSL characterized the HFV in the data, …

Ecological Feedbacks In The Earth System, Eugene J. Murphy, Jessica J. Williams, Isla H. Myers-Smith, Vivienne P. Groner, David M. P. Jacoby, Lester Kwiatkowski, Jess Melbourne-Thomas, Emma Ransome, Cristina Banks-Leite, Laurent Bopp, Marion Gehlen, Eileen E. Hofmann, Babette Hoogakker, Nadine M. Johnston, Yadvinder Malhi, Emma L. Cavan

CCPO Publications

Ecological feedbacks are fundamental features of the Earth system, affecting physical processes and chemical cycles. Our understanding of the interactions underlying these feedbacks at different spatial and temporal scales and the extent to which feedbacks affect Earth system functioning remains limited. Climate change and other anthropogenic pressures are already negatively affecting ecological processes in marine, freshwater, and terrestrial ecosystems. These will most likely be amplified in the coming decades under our current warming and socioeconomic pathways. The knock-on impacts on ecological feedbacks have the potential to cause rapid perturbations to the Earth system, and may significantly impact the structure and …

Results Of The Second Ice Shelf-Ocean Model Intercomparison Project (Isomip+), Claire K. Yung, Xylar S. Asay-Davis, Alistair Adcroft, Christopher Y. S. Bull, Jan De Rydt, Michael S. Dinniman, Benjamin K. Galton-Fenzi, Daniel Goldberg, David E. Gwyther, Robert Hallberg, Matthew Harrison, Tore Hattermann, David M. Holland, Denise Holland, Paul R. Holland, James R. Jordan, Nicolas C. Jourdain, Kazuya Kusahara, Gustavo Marques, Pierre Mathiot, Adele K. Morrison, Yoshihiro Nakayama, Olga Sergienko, Robin S. Smith, Alon Stern, Ralph Timmermann, Qin Zhou

CCPO Publications

Ocean-driven basal melting of Antarctic ice shelves plays an important role in the mass loss of the Antarctic Ice Sheet. Ice shelf cavity-resolving ocean models are a valuable tool for understanding ice shelf-ocean interactions and for simulating projections of ice shelf and ocean states under future climate. Designed to assess the current state of ice shelf–ocean modelling, the second Ice Shelf–Ocean Model Intercomparison Project, ISOMIP+, consists of 12 ocean model configurations submitted with a common, idealised experimental setup. Here, we focus on the experiments Ocean0–2 (Asay-Davis et al., 2016), which are ocean models with idealised, static ice …

Basal Melting Variability Of The Ross Ice Shelf From Mixing Ratios Of Simulated Water Masses (1993-2018) And Potential Climatic Drivers, Enrico Pochini, Andrea Bergamasco, Florence Colleoni, Manuel Bensi, Giorgio Budillon, Pasquale Castagno, Michael S. Dinniman, Pierpaolo Falco, Emanuele Forte, Vedrana Kovačević, Stefanie L. Mack

CCPO Publications

Several water masses circulate under the Ross Ice Shelf (RIS), the largest ice shelf in Antarctica, each causing basal melting with a specific spatio-temporal pattern. To investigate these patterns and their variability, we applied a mixing ratio analysis to simulated water masses from a new numerical ocean model of the Ross Sea. The simulation, which was run over 26 years (1993–2018) and which includes the RIS cavity, shows good agreement with seaborne and mooring observations. The total RIS basal melt rate is 90 Gt/yr on average, and is caused mostly by High Salinity Shelf Water (HSSW) (68%), which enters the …

Lessons For Transformative Ocean Science From The Integrated Marine Biosphere Research (Imber) Project, Deborah Santos Prado, Ella-Kari Muhl, Mia Strand, Derek Armitage, Nina Bednaršek, Stephanie Brodie, Christopher Cvitanovic, John Claydon, Sam Dupont, Karen Evans, Alistar Hobday, Eileen Hofmann, Raleigh Hood, Laura Kaikkonen, Dongyan Liu, Jessica Melbourne-Thomas, Franz Mueter, Eugene Murphy, Prateep Kumar Nayak, Alice Newton, Heidi Pethybridge, Carol Robinson, Samiya Selim, Rowan Trebilco, Micaela Trimble, Fang Zuo

CCPO Publications

This paper synthesizes the key contributions and lessons learned from Integrated Marine Biosphere Research (IMBeR), a large-scale global research project aimed at fostering ocean sustainability under global change for the benefit of society. The UN Decade of Ocean Science for Sustainable Development has catalyzed a renewed focus on the importance of transforming ocean science. IMBeR’s global activity over the past decade has focused on promoting transformative science by generating, mobilizing, and communicating the knowledge needed to support ocean governance. Key contributions from IMBeR participants include quantifying and comparing historic and present structure and functioning of linked ocean and human systems, …

Accounting For Spatial Effects And Social Norms In Making Algorithmic Law: Insights From And Applications In Urban Mobility, Jingkang Gao

Journal of Law and Mobility

This Article examines a prominent idea in the law and technology literature: that algorithms and big data can be used to make law dynamic and personalized. As currently envisioned by legal scholars, “algorithmic law” entails laws that adjust in real time to changing conditions and vary across individuals, improving welfare by tailoring legal rules and standards to personal characteristics.

This Article argues that this vision of algorithmic law is incomplete—and often counterproductive. Existing proposals treat personalization as a function of individual attributes alone, overlooking the fact that effects of individual behavior are fundamentally interactive. Individual behavior is shaped by spatial …

Artificial Intelligence–Enabled Revenue Cycle Management And Financial Performance In Healthcare Organizations, K’Reesa Webster

Theses, Dissertations and Capstones

The purpose of this review was to examine how artificial intelligence–enabled revenue cycle management (AI-enabled RCM) systems have been associated with financial performance outcomes in healthcare organizations. A literature review following a systematic process consistent with PRISMA 2020 guidelines was conducted to identify quantitative studies published between 2015 and 2026. Eligible studies were required to report at least one financial outcome related to claim denial rate, days in accounts receivable, or operating margin. Twenty-seven studies met all inclusion criteria. Findings across these studies indicated that AI-enabled RCM systems have been associated with lower denial rates, shorter accounts receivable timelines, and …

Distilling The Complexity Of Agent-Based Simulations Into Textual Explanations Via Large Language Models, Noé Y. Flandre, Philippe J. Giabbanelli

VMASC Publications

Communicating the design and results of agent-based models (ABMs) to subject matter experts is challenging, which hinders participation and limits trust in simulation-based decision support. Large language models (LLMs) can communicate ABMs as textual summaries, thus complementing traditional disclosure through statistical and visualization techniques. While prior work translated the structure of conceptual models into narratives via LLMs, our extension covers the dynamics of simulation models via an automated simulation-to-text method that extracts contextual information from NetLogo ABMs, performs repeated simulations, and generates narrative descriptions (including the model’s purpose, parameters, and simulation dynamics) using mutimodal LLMs. Furthermore, four summarization algorithms spanning …

An Analysis Of Volcanic Lightning And Its Controlling Factors, Nathan Stamford

Honors Theses

Volcanic lightning, first documented during the 79 AD eruption of Mount Vesuvius, includes any electrical discharge that is generated during a volcanic eruption rather than a thunderstorm. By analyzing individual eruptions and performing laboratory experiments, previous research has identified multiple controlling factors that influence the charging mechanisms and generation of volcanic lightning. While the previous research has made significant discoveries about the phenomenon, the exact relationship between many of these controlling factors and volcanic lightning generation has been poorly quantified. In this study, we investigate these controlling factors and show that they have a wide range of influence over volcanic …

Interpretable Sample Uncertainty Measures For Ranked-Choice Election Polls, Jason Liang

CMC Senior Theses

Polling results from traditional single-choice plurality elections are readily interpretable. Simple frequentist population parameters are estimated, including each candidate’s total support and the size of the front runner's lead. If the point estimate for the size of the front runner's lead exceeds the margin of error of the lead, we can conclude that the poll shows a statistically significant front runner. However, the interpretability of these population statistics disappears when applied to ranked-choice voting elections. Because ballots rank multiple candidates and candidates are eliminated in rounds, simple population-wide parameters are not well-defined. In RCV elections, a candidate’s ability to win …

Representations Of Finite Groups And Diagrammatic Algebras, Hudson Yeend

CMC Senior Theses

Representation theory allows mathematicians to study abstract mathematical objects using the powerful and concrete tools of linear algebra. This thesis aims to present some foundational concepts in representation theory and apply these concepts to specific groups and algebras. We begin by examining representations of finite groups, culminating with a proof of Maschke's theorem. We then use the correspondence between a group and its group algebra to segue into a study of representations of diagrammatic algebras, where we introduce analogous notions of decomposition. We end with a study of quiver representations, noting that Gabriel's theorem and the kQ-modular structure transcend …