Physical Sciences and Mathematics Commons^™

Iere Annual Report 2026, Erin Largo-Wight, Heather Barnes Truelove, Kelly Rhoden, Jessica Barnes, Lauren Canady, Nicole Lowe, Ruby Cox, Sydney Barrett, Kathleen Hutton

Annual Reports

2026 Annual Report of the Institute for Environmental Research and Education

Understanding Phishing Susceptibility Through Expert Consensus Using Digital Marketing Parallels And A Machine Learning-Based Implementation, Mansoor Ahmad

All Graduate Theses, Dissertations, and Other Capstone Projects

Phishing remains one of the most effective attack vectors for gaining unauthorized access to organizational systems, yet defenders often lack systematic methods to assess their exposure before an attack. This study develops a framework that uses machine learning to encode the collective expertise of cybersecurity practitioners into a portable phishing susceptibility assessment tool, with the goal to help security teams proactively identify patterns, prioritize awareness training, and strengthen detection controls. The study surveyed 27 practitioners with extensive experience in social engineering, red teaming, penetration testing, and threat analysis to identify which factors most influence phishing susceptibility. Practitioners provided quantitative ratings …

Teaching Effectiveness On Secondary Mathematics: Evidence From Pisa—Shanghai-China, Ting Shen

Psychological Science Faculty Research & Creative Works

Educational researchers and policymakers around the world have a strong interest in understanding the underlying reasons for the remarkable academic achievement of Chinese students in the Programme for International Student Assessment (PISA). Although teachers have a significant impact on student achievement, empirical evidence on teaching effectiveness in the Chinese education system has been scarce. This study uses the PISA 2012 Shanghai-China data and employs both multilevel models and quantile regression models to investigate effective teaching factors and their differential effects for students at different mathematics achievement levels. The results reveal the importance of cognitive activation and disciplinary climate as consistent, …

Legal Ethics Of Ai Snake Oil: Navigating The Hype, Harm, And Hope Of Legal Ai, Drew Simshaw

Scholarly Works

This paper argues that the legal ethics of AI extend far beyond competence and hallucinations. It shows how AI often functions as a mirror, exposing deeper ethical questions about institutional incentives, lawyer wellbeing, access to justice, and AI's broader social and environmental impacts.

Unsafe2safe: Controllable Image Anonymization For Downstream Utility, Minh Dinh

Dartmouth College Master’s Theses

Large-scale image datasets frequently contain identifiable or sensitive content, raising privacy risks when training models that may memorize and leak such information. We present Unsafe2Safe, a fully automated pipeline that detects privacy-prone images and rewrites only their sensitive regions using multimodally guided diffusion editing. Unsafe2Safe operates in two stages. Stage 1 uses a vision--language model to (i) inspect images for privacy risks, (ii) generate paired private and public captions that respectively include and omit sensitive attributes, and (iii) prompt a large language model to produce structured, identity-neutral edit instructions conditioned on the public caption. Stage 2 employs instruction-driven diffusion editors …

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 …

Evaluation Of Chromium-Crosslinked Amps-Hpam Copolymer Gels: Effects Of Key Parameters On Gelation Time And Strength, Maryam Sharifi Paroushi, Baojun Bai, Thomas P. Schuman, Yin Zhang, Mingzhen Wei

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Controlling CO₂ channeling in heterogeneous reservoirs remains a major challenge for both enhanced oil recovery (EOR) and secure geological storage. AMPS-HPAM copolymers exhibit high-temperature resistance and brine tolerance compared with conventional HPAM gels, making them well suited for the harsh environments associated with CO₂ injection. Chromium-based crosslinkers (CrAc and CrCl₃) were investigated because sulfonic acid groups in AMPS can coordinate with trivalent chromium ions, enabling dual ionic crosslinking and the formation of a robust gel network. While organic crosslinked AMPS-HPAM gels have been widely studied, the behavior of chromium-crosslinked AMPS-containing systems, particularly their gelation kinetics under …

Multivariate Quantile Autoregression-Mixed Data Sampling (Mvqar-Midas) Modeling Of Cost Of Living And Supply Chain Dynamics In Canada., Patrick Gbolonyo

Theses and Dissertations (Comprehensive)

In recent years, the rising cost of living as a result of persistent inflationary pressures, disruptions in the global supply chains, and changes in the macroeconomic landscape has become a critical topic of discussion. To address this, we move beyond a mean-based framework and employ a quantile regression approach. This allows the persistence of each series and the transmis- sion of shocks between the Consumer Price Index (CPI) (the total CPI which is a percentage change over the past 12 months), the Interest Rate (IR)(the target for the overnight rate), the New Housing Price Index (NHPI), and high-frequency supply chain …

Implementation Of A 3-Dimensional Hydrodynamic Numerical Model On A Large Northern Lake, Kirin Lorente

Theses and Dissertations (Comprehensive)

Climate change is impacting high latitude regions disproportionately more than those at a lower latitude. With polar lakes being highly sensitive to changes in the climate, shifts in lake ice dynamics and hydrodynamics are indicators of such changes in regional and global climates. These changes impact future seasons in a positive feedback loop, where changes in winter ice cover impact open water nutrients levels and lake thermal structure. This highlights the importance for long-term monitoring. Despite the abundance of lakes distributed across Northern Canada, there has been limited research conducted on these sentinels of climate change.

Specifically, Great Bear Lake …

Error-Driven Density Control For Compact Gaussian Splatting Under Sparse Supervision, Abdelrhman Elrawy

Theses and Dissertations (Comprehensive)

This thesis studies efficiency and stability challenges in Gaussian-splatting-based reconstruction under sparse supervision. In few-shot novel view synthesis, standard 3D Gaussian Splatting (3DGS) can overfit the limited training views and grow an unnecessarily large number of primitives due to limitations in its Adaptive Density Control (ADC) mechanism. This thesis introduces an error-driven reformulation of ADC that triggers densification using opacity gradients as a lightweight proxy for rendering error, and shows that such aggressive densification must be paired with delayed and conservative pruning to prevent destructive create--destroy cycles. When combined with depth-based geometric regularization, the resulting framework produces substantially more compact …

Impacts Of Burn Severity On The Hydro-Physical, Chemical And Thermal Properties Of Sub-Arctic Peat, Joshua B. Meyer Mr

Theses and Dissertations (Comprehensive)

Under current climate change, summer season in the boreal regions of Canada is becoming increasingly warmer and drier. This shift is driving increases in the frequency and intensity of naturally occurring wildfires and allowing access to areas not normally susceptible to fire such as wetlands. In peatland areas, these increasingly large fires can create complex burn scars, which contain a wide variety of different burn severities across different landcover types. This variety calls for a greater understanding of how varying fire intensity affects the hydro physical, chemical, and thermal characteristics of boreal peat. This study aims to deepen that understanding …

Impacts Of Management Practices And Soil Properties On Free-Living Nitrogen Fixation In Eastern South Dakota, Laura Galvan Nuevo

Electronic Theses and Dissertations

Free-living nitrogen (N) fixation (FLNF) may represent a sustainable pathway to supplement crop N demand in agroecosystems, yet edaphic and management factors regulating this process under field conditions remain poorly understood in the Northern Great Plains. Using 15N2 direct method to quantify potential FLNF (pFLNF) and baseline FLNF (bFLNF), this thesis investigates how crop rotations (2-, 3-, and 4-year systems), tillage management (conventional vs. no-till), cover cropping, and soil properties affect the potential of free-living diazotrophs to supply N via FLNF in a long-term no-till corn-based system in Southeastern South Dakota. A two-year field study evaluated pFLNF across four growing …

Solubility And Dissolution Mechanism Of Novel Multi-Ester Headgroup Surfactants In Supercritical Co2, Ning Xu, Yan Ling Wang, Baojun Bai, Shi Zhang Cui, Yu Zhang, Wen Jing Shi, Zhao Nian Zhang, Wen Hui Ding, Pei Xu Ma, Zan Gao

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

To address the limited solubility and applicability of conventional hydrocarbon surfactants in supercritical CO₂, a series of multi-ester headgroup surfactants were designed and synthesized by leveraging the CO₂-philic properties of ester groups. The molecular structures were characterized using Fourier transform infrared (FT-IR) spectroscopy and ¹H NMR. A custom-designed laser-based apparatus was developed to quantify surfactant solubility and systematically investigate phase behavior in CO2. Molecular dynamics (MD) simulations were employed to elucidate structure–solubility relationships across multiple scales, including solubility parameters, interaction energies, radial distribution functions (RDFs), and free volume fractions. Results indicate that, at 323.15 K, …

Ai Vs. Genai: Combating Llm Generated Prescription Fraud With Transformer Based Detection Models, Ankitha Vokkaleri Shankarappa

Selected Full-Text Master Theses 2021-

The rapid evolution of Large Language Models (LLMs) has introduced a sophisticated new vector for healthcare fraud: the generation of high-fidelity, synthetic medical prescriptions. Traditional fraud detection systems, which rely on rule-based engines and basic statistical anomalies, are increasingly ill-equipped to identify these AI-generated forgeries that mimic the structural and clinical logic of authentic records. This thesis presents a robust detection framework using Transformer-based architectures to distinguish between human-authored Medicare Part D prescriptions and fully synthetic records generated by GPT-4.

The research was conducted across two distinct phases: an initial pilot study using 4,000 samples and a rigorous validation stress …

Unlocking Green Potential In Indonesian Small Medium Enterprises: Challenges And Opportunities For Sustainable Vermicelli Production, Togar W. S. Panjaitan, Karina Agustin, Prayonne Adi, Jani Rahardjo, I Nyoman Sutapa

ASEAN Journal on Science and Technology for Development

Due to limited initiatives and support, the transition to green manufacturing among small and medium enterprises (SMEs) in developing countries remains challenging. Through a critical literature review and case study of the Indonesian vermicelli industry, this study highlights the importance of collaboration and strong policies to address this. Other findings also reveal that the energy intensity and emissions in the sector exceed best practice benchmarks, with the cooking and drying stages contributing significantly to emissions due to high energy consumption. Lack of government regulations, company awareness, and understanding of applicable practices and technologies creates challenges in green manufacturing. The study …

Evaluating The Role Of Power Electronics In Enhancing Renewable Energy And Smart Grid Performance: A Review, Muhammad Irfan Habib

ASEAN Journal on Science and Technology for Development

The review article demonstrates the importance of power electronics in propelling the increase of renewables and increasing the efficiency of contemporary electrical grids. In particular, it highlights the need for bidirectional converters for effective energy management in vehicle-to-grid (V2G) systems with two-way power transfer. The paper examines the series of complications offered by the significantly large integration of power electronic converter-interfaced renewables, such as those related to system steadiness, power quality, and resignation bottlenecks. Also, it recognizes a mix of new technologies, such as wide bandgap (WBG) semiconductors like silicon carbide (SiC) and gallium nitride (GaN), that deliver superior performance …

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 …

Part Qm: Quantum Mechanics, Konstantin Likharev

Essential Graduate Physics

Includes: Introduction; 1D Wave Mechanics; Higher Dimensionality Effects; Bra-ket Formalism; Some Exactly Solvable Problems; Perturbation Theories; Open Quantum Systems; Multiparticle Systems; Introduction to Relativistic Quantum Mechanics; Making Sense of Quantum Mechanics

Part Cm: Classical Mechanics, Konstantin Likharev

Essential Graduate Physics

Includes: Review of Fundamentals; Lagrangian Formalism; A Few Simple Problems; Oscillations; From Oscillations to Waves; Rigid Body Motion; Deformations and Elasticity; Fluid Mechanics; Deterministic Chaos; A Bit More of Analytical Mechanics

Erosion Of Trust In Online Information, Tirth Desai

A with Honors Projects

Researching how AI spreads misinformation and impacts trust in information.

In Situ Observations Of Thermal Ions In Perturbed Ionospheres: Techniques And Results, Magdalina Louise Moses

Dartmouth College Ph.D Dissertations

Prediction and mitigation of space weather events are active research topics that require knowledge of the physics governing the ionosphere. Sounding rockets can be used to make in situ observations. The Lynch Rocket Lab created the Petite-Ion-Probe (PIP), a small retarding potential analyzer, to measure thermal ion parameters (i.e., ion density and temperature). A PIP's raw data consists of a series of measured anode currents as a function of screen bias voltages, called IV curves. PIPs can be integrated onto a sounding rocket’s main payload and/or be deployed from the rocket on small platforms called ``PIP-Bobs''. Note that as the …

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 …