Digital Commons Network^™

Volatiles In Mantle Sources At Mid-Ocean Ridge And Intraplate Settings, Rebecca Lippitt

Open Access Dissertations

Volatiles play an integral role in upper mantle processes and the eruptive styles of volcanoes. Although we know the importance of volatiles to volcanic systems, processes like melting, crystallization, and degassing alter the volatile abundances of mantle melts, making it difficult to quantify mantle source compositions and the extent to which volatiles influence melting processes. Additionally, volatile content may be a key factor in setting the oxidation conditions of mantle source rocks and subsequently changing those conditions in a body of magma as it ascends through the crust and is erupted at the Earth’s surface. Recent work, however, has identified …

Superelectrophile Promoted Synthesis Of Heterospirocycles, Mariam A. Adegbenro

Graduate Research Theses & Dissertations

Spirocycles are important scaffolds with a wide range of applications in material science as well as in medicine. The introduction of a heteroatom into the spirocyclic framework can improve its electronic, photophysical, and structural properties. Various reports have been published on the incorporation of atoms other than carbon into the spirocycle. The work outlined in this dissertation involves the development of synthetic methodologies for the synthesis of hetero-spirocycles via superacid-promoted cyclization reactions. These concepts are summarized in Chapter 1.

Chapter 2: Aromatic imines were prepared from 9-fluorenone and azafluorenones, and these substrates were reacted in the Brønsted superacid, triflic acid, …

Supramolecular Self-Assemblies Of Ru( Ii ) Phototherapeutics: Biological Activity Of Micro- And Nano-Particles Acting As Reservoirs, Jérôme Laisney, Sarah M. Kriger, Dmytro Havrylyuk, Jason M. Unrine, David K. Heidary, Edith C. Glazer

Chemistry Faculty Publications

Ruthenium( II) coordination complexes have many appealing properties as prodrugs, but can suffer from poor aqueous solubility and short circulation times, drastically decreasing efficiency in vivo. Nanoformulations using a variety of carriers, such as inclusion in polymers/lipids or adsorption on inorganic nanoparticles have been applied to overcome this limitation, but unfortunately, these approaches raise additional concerns regarding the fate of the carriers, with potential long-term toxicity and accumulation in vital organs. Here, we present an alternative delivery strategy with formation of pure and polymer-supported supramolecular self-assemblies of Ru(II) complexes acting as ‘‘reservoirs’’. A facile preparation of size-controlled particles was achieved …

Characterization And Antimicrobial Activity Of Lignin-Derived Compounds In Ground Hurd Tissue Of Cannabis Sativa L. (Hemp) Cultivars, Jourdan E. Lakes, Anjam Sadik, Robert C. Pearce, Barbara L. Knutson, Stephen E. Rankin, Bert C. Lynn, Michael D. Flythe

Chemistry Faculty Publications

Objectives: Characterize lignin-derived compounds from the hurd of 10 fiber/grain Cannabis sativa L. cultivars and investigate the antimicrobial activity of select compounds on the wasteful ruminant bacterium, Acetoanaerobium sticklandii SR.   Methods: Hurd was processed using an aqueous ethanol solvent system and analyzed via GC-MS to identify and characterize lignin derivatives. Antimicrobial activity of the hurd and three monomers was assessed using solid (hurd) and liquid medium (monomers) bioassays against SR.   Results: 27 compounds were identified, most as monomers but a few were dimers, ethylated derivatives, and organic acids. There were approximately equal G- and S-units with substantially fewer H-units. Several …

Enhanced Photostability Through Rapid Exciton Decay In Desymmetrized Cyclopentannulated Acenes With Strong Face-To-Face Pi Stacking, Chad D. Cruz, Karl J. Thorley, Zachary J. Knepp, Jared Wahlstrand, Gil M. Repa, John C. Stephenson, Sean Parkin, Lisa A. Fredin, John E. Anthony, Emily G. Bittle

Chemistry Faculty Publications

The photophysics of organic semiconductors impacts their efficiency in optoelectronic devices where exciton transitions, including singlet fission, intersystem crossing and the formation of charge transfer states influence the ability to convert between bright and dark states and to dissociate into free charges. Unfortunately, photodegradation and spurious signals often confound the results of optical studies, especially of important triplet states. Here four asymmetric cyclopentannulated acenes are synthesized and studied. This system represents an extreme in photophysics achieved via molecular design to fully quench the photoluminescence and bypass triplet formation allowing for comparative studies with other highly absorbing acenes. Rapid molecular exciton …

Development And Application Of Lc-Ms/Ms Methods For The Determination Of Environmental Contaminants, Mawuli Wilson-Cee Macdonald

Electronic Theses and Dissertations

Environmental contaminants such as neonicotinoids and bisphenols are increasingly detected at ultratrace concentrations in environmental and consumer matrices, necessitating sensitive and efficient analytical methodologies. This dissertation presents the development, validation, and application of liquid chromatography–tandem mass spectrometry (LC–MS/MS) methods coupled with simplified sample preparation techniques. In Chapter 2, a direct aqueous injection (DAI) LC–MS/MS method was developed for the analysis of five neonicotinoids in surface water, eliminating extensive sample preparation. The method demonstrated strong linearity (R² = 0.993–0.998), with limits of detection (LOD) ranging from 0.5 to 4 ng/L and lower limits of quantification (LLOQ) of 10–50 ng/L. Accuracy (80–120%) …

All Games Have Equilibria, Arthur Paul Pedersen, M. Ali Khan, Maxwell B. Stinchcombe

Publications and Research

Research on Nash equilibrium existence for infinite games has grown into a patchwork of technical preconditions and counterexamples. This paper presents a unified program in equilibrium theory by revising the predominant model of mixed strategies based on countable additivity. A game is specified by a nonempty set of players and, for each player, a nonempty action set and a bounded von Neumann-Morgenstern utility function. Every such game is shown to admit a Nash equilibrium in finitely additive mixed strategies. In addition, the equilibrium correspondence for any such game is shown to be nonempty, compact-valued, and upper hemicontinuous, and the same …

First-Job Contract Review Cheat Sheet, Johanna Jones-Morris, Ashlee Martellacci

Teaching and Learning Resources

This cheat sheet helps first-time employees understand what to review before signing an employment contract. It highlights job duties, compensation, scheduling, employment terms, benefits, restrictive clauses, worker classification, and common red flags so that individuals can ask informed questions and recognize potentially unfair or unclear terms.

Operationalizing Cyber-Routine Activities Theory For Senior Cybercrime Prevention: An Evaluation Of The Shield Training-The-Trainer Program, Kyung-Shick Choi, Insun Park, Mijin Kim, Ji Yae Bong

International Journal of Cybersecurity Intelligence & Cybercrime

Older adults face growing risks of cyber-enabled fraud, yet scalable, evidence-based prevention programs remain limited. Guided by Cyber-Routine Activities Theory (Cyber-RAT), this study evaluates the pilot implementation of the Seniors Harnessing Internet Education for Lasting Defense (SHIELD) Training-the-Trainer program, designed to prepare law enforcement officers and community leaders to deliver cybercrime prevention education to older adults. A key innovation of SHIELD is its integration of interactive game-based simulations, which allow participants to practice verification, refusal, and reporting behaviors in realistic cybercrime scenarios. Following the December 2025 pilot session in Boston, semi-structured interviews were conducted with 12 participants from law enforcement, …

From Authorization To Loss: A Blockchain Forensic Analysis Of Transaction-Level Mechanisms In Cryptocurrency Airdrop Scams, Chanwoo Shin, Kyung-Shick Choi

International Journal of Cybersecurity Intelligence & Cybercrime

Cryptocurrency airdrop scams have emerged as a rapidly growing form of cyber-enabled financial crime, yet remain underexplored in empirical research. This study examines how transaction-level mechanisms and offender strategies influence variation in monetary loss in airdrop scam incidents. Grounded in Cyber-Routine Activities Theory (Cyber-RAT), the study conceptualizes financial harm as occurring within decentralized environments where users’ online behaviors, particularly transaction authorization, intersect with limited digital capable guardianship. Data were drawn from 112 validated airdrop scam cases reported on Chainabuse.com between January and December 2025. Blockchain forensic analysis using Breadcrumbs was conducted to reconstruct transaction pathways, identify exchange interactions, and detect …

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 …

Lens: Llm-Enabled Narrative Synthesis For Mental Health By Aligning Multimodal Sensing With Language Models, Wenxuan Xu

Dartmouth College Master’s Theses

Multimodal health sensing offers rich behavioral signals for assessing mental health, yet translating these numerical time-series measurements into natural language remains challenging. Current LLMs cannot natively ingest long-duration sensor streams, and paired sensor–text datasets are scarce. To address these challenges, we introduce LENS, a framework that aligns multimodal sensing data with language models to generate clinically grounded mental-health narratives. LENS first constructs a large-scale dataset by transforming Ecological Momentary Assessment (EMA) responses related to depression and anxiety symptoms into natural-language descriptions, yielding over 100,000 sensor–text QA pairs from 258 participants. To enable native time-series integration, we train a patch-level encoder …

From Physical Correlation To Emotional Connection: The Role Of Passive Haptics On Empathy In Virtual Reality, Jemely Robles

Dartmouth College Master’s Theses

Virtual reality is increasingly explored as a tool for cultivating empathy, and haptic feedback as a tool for enhancing immersion. This paper investigates the effects of combining the two.  Fifty-two participants experienced a custom-built VR scene in which a character was shown packing up a room. Participants were assigned to either a haptic condition, receiving passive haptic feedback corresponding to the character's actions, or a non-haptic control condition that did not receive any haptic input. Trait empathy was measured beforehand, and state empathy and engagement were measured after the experience. Thematic analysis was conducted on post-study interviews, and headset recordings …

Assessment Of Local Amplification Causes At Coastal Strong Motion Stations With Elevated Ground Motion Residuals From The 2024 Tewksbury, New Jersey Earthquake, Patrick Daniele

Open Access Master's Theses

Elevated ground motion amplitudes at individual stations are commonly observed in earthquake datasets, yet the physical causes of these residuals are often not explicitly identified. This study investigates two seismic monitoring stations in southeastern New York that recorded amplified motions during the 2024 Mw 4.8 Tewksbury, New Jersey earthquake. A consistent set of established geophysical methods -- including multichannel analysis of surface waves (MASW), passive ambient noise arrays (MAM), stochastic inversion, horizontal-to-vertical spectral ratio (HVSR) analysis, and directional spectral ratio techniques -- was applied to directly characterize site conditions and identify the mechanisms contributing to amplification.

Results show that elevated …

Black Hole Spectroscopy And Tests Of General Relativity With Gw250114, Jaclyn (Jax) Sanders

Physics Faculty Research and Publications

The binary black hole signal GW250114, the loudest gravitational wave detected to date, offers a unique opportunity to test Einstein’s general relativity (GR) in the high-velocity, strong-gravity regime and probe whether the remnant conforms to the Kerr metric. Upon perturbation, black holes emit a spectrum of damped sinusoids with specific, complex frequencies. Our analysis of the postmerger signal shows that at least two quasinormal modes are required to explain the data, with the most damped remaining statistically significant for about one cycle. We probe the remnant’s Kerr nature by constraining the spectroscopic pattern of the dominant quadrupolar (ℓ=m=2) …

Measuring The Efficiency Of The Arts And Culture Sector Relative To Gdp: A State-Level Analysis Of The United States, Ameerkhan Jaffar Khan Khader Khan

Selected Full-Text Master Theses 2021-

Arts and cultural production contributed $1.17 trillion to United States gross domestic product in 2023, 4.2% of the national total, but that contribution is spread very unevenly across states, and the official statistics describe how large the sector is rather than how efficiently it operates (Bureau of Economic Analysis, 2024). This study asks how efficiently each state convert growth in arts and culture employment into growth in value added, how many inputs the efficiency model can carry before it stops distinguishing 51 observations, and whether observable state characteristics explain the differences found. The analysis uses 2023 state-level data for all …

The Soft Target Curriculum: Using Nigeria's 2026 Cascading Breaches To Teach Foundational Cybersecurity Failures, Chinedum Amaechi, Doris Asogwa, Samuel Alade

Journal of Cybersecurity Education, Research and Practice

SourceURL:file:///home/amaechi/Documents/ *Journal of Cybersecurity Education, Research and Practice (JCERP)*. **Title:** The Soft Target Curriculum: Using Nigeria's 2026 Cascading Breaches to Teach Foundational Cybersecurity Failures.docx

Background: Between March and April 2026, a single threat actor allegedly compromised four Nigerian institutions across banking, payment infrastructure, government registry, and power distribution sectors. The breaches exposed millions of records and disrupted critical services, yet all four exploited elementary vulnerabilities taught in introductory cybersecurity courses. Objective: This pedagogical case study analyzes the four breaches as a unified phenomenon of "normalized negligence" and provides ready-to-use teaching materials for cybersecurity educators. Methods: Using open-source intelligence analysis of …

Synthesis And Characterization Of Succinate Dehydrogenase Inhibitors H2/Z14 And C6/Z96 In Order To Combat Small Cell Lung Cancer, Lauren M. Graves

Honors Undergraduate Theses

The use of ubiquinone inhibitors to combat cancer is a recent development in medicinal science and procedures to efficiently synthesize these drugs remain limited. C6/Z96 and H2/Z14, two succinate dehydrogenase inhibitors, have been previously tested in vitro against NSCLC with positive results. Their similarity to ubiquinone is what makes them a strong candidate for a succinate dehydrogenase inhibitor at the ubiquinone binding site. The heterocyclic scaffolds and substituents are proposed to bind strongly through a combination of electronics, hydrogen-bonding, and fit, allowing them to bind well and prove to be more favorable than ubiquinone when competing for the Q-site. By …

Generative Ai-Driven Optimization In Flexible And Reconfigurable Manufacturing Systems, Salah Hammedi, Hicham Chaoui, Lotfi Nabli

Electrical & Computer Engineering Faculty Publications

Flexible and Reconfigurable Manufacturing Systems (FRMSs) are essential for coping with variability in modern production environments; however, efficient scheduling and rapid reconfiguration remain challenging. This paper presents a hybrid optimization framework that integrates Colored Petri Net (CPN) modeling with Generative Artificial Intelligence (GenAI) to enhance scheduling performance and system adaptability. The CPN formalism ensures verifiable modeling of system dynamics, while a transformer-based generative model produces candidate scheduling and reconfiguration strategies. Simulation experiments were conducted under static, dynamic, and adaptive scenarios, including machine breakdowns and dynamic job arrivals. Performance was evaluated using makespan, mean flow time, machine utilization, and reconfiguration latency. …

Advances In Active And Passive Integrated Photonic Devices On Thin-Film Lithium Niobate, Pooja S. Kulkarni

Graduate Studies Theses and Dissertations 2026

Thin-film lithium niobate (TFLN) has emerged as a powerful platform for integrated photonics, combining the exceptional electro-optic, nonlinear, and optical properties of bulk lithium niobate with the scalability and compactness of planar nanophotonic technologies. Building on these principles, advanced device architectures such as adiabatic dichroic filters have demonstrated exceptional spectral performance spanning over two octaves of bandwidth on the TFLN platform. Beyond reciprocal devices, it also offers promising pathways toward integrated nonreciprocal components. By leveraging broadband filtering structures and advanced nonlinear photonic design strategies, compact and monolithic optical isolators can be envisioned without relying on traditional magneto-optic materials. In the …

Dimension Reduction Involving Exogenous Variables With Applications In Manufacturing And Healthcare, Linxi Li

Theses and Dissertations

High-dimensional data analysis presents diverse challenges, including the curse of dimensionality, the complexities of working with datasets that combine large feature spaces with limited sample sizes, and difficulties in identifying meaningful relationships among variables. As datasets grow in size and complexity across different fields, it is increasingly important to develop practical approaches for extracting essential information from such data. Dimension reduction methods address these challenges by alleviating the effects of high dimensionality, enhancing the ability to reveal hidden patterns, and uncovering latent structures within the data to support further analysis. Some methods reduce dimensionality while preserving all relevant information, offering …

Medical Linear Accelerator Water Tank Measurements Optimization For Commissioning, Tyler R. Laugh

Theses and Dissertations

Beam commissioning is a critical component of linear accelerator (LINAC) implementation, requiring accurate measurement of percent depth dose (PDD), beam profiles, and output factors across a wide range of field sizes. Conventional commissioning workflows often require multiple detectors and can be time-intensive, particularly for small-field dosimetry where measurement accuracy is highly sensitive to detector characteristics. This study evaluated the performance of a plastic scintillation detector (BP-PSD) for full water tank commissioning and its potential to improve efficiency while maintaining dosimetric accuracy.

Beam data were acquired for field sizes ranging from 1 × 1 cm² to 40 × 40 cm² using …

Towards Sample-Efficient Deep Reinforcement Learning, Guang Yang

Theses and Dissertations

Deep reinforcement learning (DRL), combining reinforcement learning and high-performance function approximations such as deep neural networks (DNN), is a powerful approach to solving complex sequential decision-making problems. However, due to the complex solution space of the sequential decision-making problems and the inefficient design of the DRL algorithms, DRL algorithms usually require a prohibitively large number of data samples to train effective strategies. Consequently, it is difficult to apply these DRL algorithms to complex real-world problems that require high costs to collect a large volume of data samples. This dissertation proposes new mechanisms to address this sample inefficiency issue, realizing sample-efficient …

An Integrated Data-Driven Framework For Arctic Shipping: Analyzing Vessel Speed, Environmental And Ecological Factors Through Innovative Statistical Spatio-Temporal Methods, Inverse Optimization And Machine Learning, Mauli Pant

Theses and Dissertations

This dissertation develops an integrated data-driven framework to analyze vessel navigation and ecological risk in the United States Arctic from 2010 to 2019. As environmental change and maritime activity increase in the region, understanding how vessels respond to dynamic conditions and how those responses interact with marine ecosystems has become increasingly important. A central theme of this dissertation is the treatment of vessel speed as both an observed outcome and a decision variable reflecting trade- offs among operational, environmental, and ecological factors. The first chapter develops a predictive framework for vessel speed over ground (SOG) using Gaussian Process Boosting (GPBoost), …

Plumbed 3-Manifolds And Neumann Moves, Noah J. Pope

Theses and Dissertations

We give a constructive proof that every weakly negative definite plumbing tree can be transformed into a negative definite one by a finite sequence of Neumann moves. The argument combines Neumann’s plumbing calculus with the diagonalization algorithm of Duchon, Eisenbud, and Neumann, which extracts the eigenvalues of the framing matrix directly from the combinatorics of the tree. We show that any positive eigenvalues are supported on linear branches and can be eliminated systematically via controlled applications of Neumann moves. This provides an explicit algorithm reducing weakly negative definite plumbing trees to negative definite ones.

A Mathematical Frameworks For Singular, Nonlinear Phenomena: Applications To Nematocyst Firing And Inhomogeneous Nls With Coulomb Potential, Abdulrahman Alharbi

Theses and Dissertations

Nematocysts are specialized cellular organelles found in all cnidarians, including corals and jellyfish, as well as in some single-celled protists such as dinoflagellates. These organelles display remarkable diversity in morphology and function, playing roles in prey capture and defense. The firing of a nematocyst is one of the fastest accelerations in nature, yet the underlying physical mechanisms remain not fully understood. In this work, we address key questions: how sufficient force is generated to overcome the fluid boundary layer, whether fluid–structure interaction models can reproduce observed dynamics, and what mechanisms trigger discharge.

Our research investigates models based on osmotic pressure …

De Novo Protein Binding To Zinc Oxide Through Biomineralization Pathways, Jean-Mark A. Francis

Theses and Dissertations

De novo proteins are structurally distinct from proteins found in nature and thus capable of having their amino acid sequence modified to accomplish tasks such as increasing protein-nanoparticle binding without unfolding or decomposing. Zinc Oxide nanoparticles are functionally distinct from their bulk counterparts and are widely used as semiconductors in a variety of fields such as medicine and agriculture. With demand for these nanoparticles increasing, environmentally sustainable methods of Zinc Oxide nanoparticle synthesis are being investigated as an eco-friendly alternative to currently utilized but environmentally hazardous chemical and physical techniques. This research investigates the binding characteristics between Zinc Oxide nanoparticles …

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 …

Mg-Spair: Multi-Grade Sparse-Guided Implicit Representation For Training-Data-Free Image Restoration, Jianmin Liao, Lei Huang, Ronglong Fang, Ashley Prater-Bennette, Lixin Shen, Yuesheng Xu

Mathematics & Statistics Faculty Publications

MG-SpaIR is a training-data-free framework for restoring a clean image from a single observation corrupted by a mixture of blur, downsampling, noise, and missing pixels. Building on implicit neural representations (INRs), we introduce a multi-grade residual hierarchy that progressively refines the reconstruction from low to high spatial frequencies across grades, improving representational fidelity and mitigating spectral limitations. To stabilize reconstruction optimization and suppress INR-induced artifacts, we further propose an explicit sparse proximal regularization (e.g., ℓ0 type) applied directly in the high-resolution image domain, which discourages spurious high-frequency patterns while preserving sharp structures. The resulting optimization is solved efficiently via a …

Machine Learning-Based Intrusion Detection System For Iot Networks Using The Rt-Iot 2022 Dataset, Bukunmi Ebenezer Afolabi

Theses, Dissertations and Capstones

The rapid expansion of the Internet of Things (IoT) has transformed modern computing by enabling seamless connectivity among heterogeneous devices across diverse application domains. However, this increased interconnectivity has significantly enlarged the attack surface of IoT networks, exposing them to a wide range of sophisticated cyber threats. Conventional security mechanisms often lack the capability to detect emerging attacks in real time, thereby necessitating the development of intelligent Intrusion Detection Systems (IDS) capable of accurately identifying malicious network activities. This study developed and evaluated a machine learning-based intrusion detection framework for multiclass IoT attack detection using the RT-IoT2022 dataset. The dataset …