Physical Sciences and Mathematics Commons^™

Provoking Generative Ai Futures: Merging Theory And Praxis, Regina M. Luttrell Ph.D., Nick Bowman

Media Studies - All Scholarship

An accessible exploration of the myriad applications and challenges of generative AI for media and communication students, scholars, and practitioners alike.

The latest emergence of increasingly low-cost and scalable AI technologies presents a point of both celebration and concern for the contemporary media and information ecosystem. To this end, this edited volume gathers media and communications scholars and practitioners to engage in discussions and exchange ideas about current trends and developments in the field. Questions this volume asks include: What are the essentials of generative AI from a media and communication perspective? How has generative AI influenced research and scholarship? …

Michael Scott Is Not A Juror: The Limits Of Ai In Simulating Human Judgment, Sean Harrington, Hayley Stillwell

Faculty Articles

Can AI replace human jurors? More specifically, can large language models predict how jurors interpret evidence and reach decisions based on legally salient facts and demographic characteristics? As legal scholars and practitioners increasingly explore AI-generated jury simulations, this Article offers the first empirical test of whether models like GPT-4, Claude, and Gemini can faithfully replicate juror reasoning. The answer, for now, is no. Across a series of mock trial scenarios involving redacted confessions, GPT- 4, Claude, and Gemini repeatedly failed to replicate how real jurors interpret evidence or exercise judgment. Their errors were not random, but systematic. Hidden prompts, built-in …

Look-Ahead Cyber-Threat Forecasting For Connected And Automated Transport: A Spatio-Temporal Graph Learning Approach, Md Al Amin, Mohammad Shafat Ahsan, Jannatul Maua, Arifa Akter Eva, M.F. Mridha, Md. Jakir Hossen

Student Publications [Scholarly]

Modern intelligent transportation systems (ITS) increasingly rely on connected electronic control units (ECUs), exposing in-vehicle networks to cyber-attacks such as message injection on the Controller Area Network (CAN) bus. While prior work has focused on post-factum detection, this paper addresses the underexplored task of forecasting cyber-attacks before they occur. We propose a spatio-temporal graph neural network (STGNN) architecture that models CAN traffic as a dynamic graph sequence, where nodes represent active CAN IDs and edges capture statistical co-activation patterns. Each graph snapshot encodes temporal features such as inter-arrival statistics and entropy, and is processed using graph attention layers followed by …

Attention-Based Geo–Textual Fusion Network For Disaster Risk Prediction, Mohammad Shafat Ahsan, Mst Sanjida Alam, Syed Sajjad Ahmed, Md Tanzimul Islam, Hashibul Ahsan Shoaib, M.F. Mridha, Md. Jakir Hossen

Student Publications [Scholarly]

Natural disasters pose recurring threats to human life and infrastructure, demanding intelligent systems that can process heterogeneous data streams and provide actionable insights in real time. Existing approaches often treat textual signals from social media and emergency communications separately from spatial hazard attributes, limiting their effectiveness in capturing the full complexity of evolving crises. This paper proposes an AI-driven geo–textual intelligence framework that integrates disaster-related text with GIS-based hazard features for real-time risk prediction and evacuation planning. The framework employs contextual text encoders and a neural GIS encoder, fused through an attention mechanism that dynamically weights cross-modal signals. Experiments on …

The Yang-Mills Field: The Fermion Sign Problem, James Glimm

Department of Applied Mathematics & Statistics Faculty Publications

A mathematical resolution of the Fermion sign problem is accomplished based on the theory of homology groups and thimbles. The original contribution of this paper is the mathematicly rigorous convergence of residual states. The residual states remain after use of a rigorous Picard-Lefschetz saddle point optimization.

The proofs depend on an assumed principle of a maximum rate of entropy production.

Two Yang-Mills gauge field theories are constructed, one based on short distance asymptotics and the other based on long distance asymptotics.

The Pure Yang-Mills Field: The Mass Gap; Axioms Minus One, James Glimm

Department of Applied Mathematics & Statistics Faculty Publications

A strictly positive gap at low temperatures is demonstrated in the energy spectrum of the previously constructed pure Yang-Mills quantum gauge field theories. Axioms other than the cluster axiom are verified.

General Relativity: Existence, James Glimm

Department of Applied Mathematics & Statistics Faculty Publications

A mathematically rigorous renormalized perturbative expansion, truncated to all finite orders, establishes the existence of quantum general relativity theories with Fermion matter fields. The expansion is initialized with the selection of a vacuum state.

Distinct infrared (IR) and ultraviolet (UV) vacuum states are considered. The IR vacuum state defines a cosmology model. The UV vacuum state defines a black hole model.

The vacuum state is initialized with a $\mathfrak{o}^3$ gauge Lie algebra defining a quark-gluon plasma. With this choice, string theory is avoided.

Finite order renormalized perturbation theory, defined using Bosons to avoid Fermion sign cancellation, is convergent to all …

Can An Experienced Qualitative Researcher Distinguish Ai From Human Qualitative Content Analysis?, Alexandra T. Lucas, Jianna Ramos, Maria Bajwa, Aaron Calhoun, Mark W. Scerbo, Janice C. Palaganas

Psychology Faculty Publications

Background

Artificial intelligence (AI) has become increasingly embedded in research workflows. Large language models (LLMs) are being used to code segments of text, organise codes into themes and interpret patterns within contexts. Recent comparisons between human and AI analyses demonstrate up to 80% thematic overlap, yet humans consistently exhibit deeper interpretive integration and contextual understanding. This study assesses whether experienced researchers can distinguish between entirely human-generated and AI-generated qualitative content analyses of a simulation debriefing.

Methods

We conducted a qualitative descriptive study comparing human-generated qualitative content analysis (QCA) with ChatGPT-4o-generated QCA using a single focus group transcript on emotion management …

Plastic Waste Imports & Coastal Litter: Evidence From Citizen Science Data, Rebecca L.C. Taylor, Hebe Williams, Shan Zhang

Economics Faculty Publications

Plastic waste is an internationally traded commodity, where importing countries recycle plastic waste into usable materials. However, there are concerns that the importation process creates plastic litter - a negative externality - in importing countries. While this concern has received much media and policy attention, quantifying the magnitude of this externality has been hindered by a lack of data on plastic litter across countries and over time. To this end, we use unconventional citizen science data on litter from Ocean Conservancy's International Coastal Cleanup, together with the United Nations Comtrade Database, to estimate the correlation between traded plastic waste and …

Equity In Coastal Resilience: A Framework For University Engagement In Community-Based Projects, Juita-Elena (Wie) Yusuf, Jennifer L. Whytlaw, Marina Saitgalina, Ogechukwu N. Nwandu-Vincent, Khairul A. Anuar, Thomas Allen, Joshua Behr

School of Public Service Faculty Publications

As communities face intensifying climate hazards, it is vital to strengthen resilience in ways that explicitly prioritize social equity. This study examines how higher education institutions can better support government, nonprofit, and community partners in advancing equity-centered coastal resilience in the U.S. Utilizing a qualitative research design, we analyze discussions among researchers and practitioners during a three-day workshop. We present a framework derived from a thematic analysis of breakout group transcripts from a three-day national virtual workshop involving 113 researchers and practitioners. The analysis identified four core themes: the necessity of aligning projects with community-defined priorities; the foundational role of …

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 …

Towards Breach Hypothesis Based Predictive Autonomous Cyber Defense Ecosystem: Proactive Prevention Of Imminent Threats And Productivity Losses, Yogesh Chavarkar

Master's Theses and Doctoral Dissertations

As cyberattack tools and techniques get sophisticated and persistent, reactive cybersecurity has been unable to effectively prevent breaches and compromises. Organizations and institutions with large complex environments have wide vulnerable exposure with higher chances of a cyberattack. This also increases overall security and financial risk. Possibility of repetitive attacks from cyber threats increases, too, despite the use of standard defense measures. Security tools and system vulnerabilities often need manual patching and updates to keep the environment secure and functioning effectively. Productivity suffers from manual trade-offs in keeping the systems secure from adverse impact. Persistent high-severity cyberattacks, despite continued reactive defensive …

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) …

Crispr-Cas9 Unleashed: Gene-Slicing Adventures In The Cancer Battlefield, Shivakuma Sonnaila, Shilpi Agrawal

Chemistry & Biochemistry Faculty Publications and Presentations

Cancer, a global health menace, continues to pose significant challenges in terms of incidence and mortality, necessitating innovative therapeutic strategies. Despite existing treatments, the limitations persist, prompting a quest for novel approaches. The emergence of immunotherapy marked a transformative era in solid tumor treatments, yet its efficacy is constrained by adverse effects. Concurrently, the integration of advanced technologies into cancer treatment explores the vast potential residing at the molecular level through gene analysis and manipulation. This review articulates the role of state-of-the-art genome editing technology, notably clustered regularly interspaced short palindromic repeats (CRISPR-Cas9), in overcoming the constraints of immunotherapy for …

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 …

Recent Advances In Triboelectric Nanogenerators For Biomedical And Cardiovascular Monitoring, Amit Sarode, Jegan Rajendran, Gymama Slaughter

Center for Bioelectronics Publications

Triboelectric nanogenerators (TENGs) have emerged as versatile self-powered platforms for wearable and implantable biomedical sensing, offering an alternative to battery-dependent electronic devices. By converting biomechanical energy from physiological motion into electrical signals, TENGs enable simultaneous energy harvesting and active sensing within flexible, lightweight, and biocompatible architectures. This review summarizes recent advances from 2020 to 2025 in triboelectric nanogenerator (TENG)-based cardiovascular monitoring. The discussion focuses on material systems, device configurations, sensing mechanisms, and applications including pulse detection and cuffless blood pressure estimation. Representative studies are compared to highlight emerging trends in wearable and self-powered sensing technologies. However, differences in experimental conditions, …

Advances And Challenges In Digitally Connected Point-Of-Care Biosensing, Abdellatif Ait Lahcen, Jegan Rajendran, Gymama Slaughter

Center for Bioelectronics Publications

Point-of-care (POC) biosensors are undergoing a paradigm shift from isolated diagnostic tools to digitally connected, intelligent platforms that enable continuous and decentralized healthcare delivery. This review critically examines recent advances in wearable, implantable, and portable biosensors, highlighting how integration with wireless communication, the Internet of Medical Things (IoMT), and artificial intelligence is transforming their functionality and clinical utility. Particular attention is given to innovations such as smartphone-enabled interfaces, cloud-based analytics, and machine learning-assisted analysis, which collectively enhance sensitivity, specificity, and user accessibility across diverse healthcare settings, from personalized home monitoring and bedside diagnostics to deployment in resource-limited regions. The review …

Ai-Assisted Surface-Enhanced Raman Spectroscopy For Cardiovascular Diagnostics: From Plasmonic Materials To Clinical Translation, Anju Joshi, Gymama Slaughter

Center for Bioelectronics Publications

Raman spectroscopy (SERS) has emerged as a powerful analytical technique, offering molecular fingerprint specificity and ultrasensitive detection of cardiac biomarkers. Recent advances in plasmonic nanostructures, surface functionalization strategies, and flexible sensing platforms have significantly improved the analytical performance of SERS-based biosensors. In parallel, the integration of artificial intelligence (AI) and machine learning has enabled robust interpretation of complex spectral datasets, facilitating automated biomarker classification and improved diagnostic accuracy in heterogeneous biological environments. Despite these advances, the field remains fragmented, with limited integration between nanomaterial design, biomarker selection, and data-driven analysis, and persistent challenges related to reproducibility, standardization, and clinical validation. …

Engineering Electrospun Pvdf Nanofibers For Next-Generation Wearable Bioelectronics, Reshmi Banerjee, Gymama Slaughter

Center for Bioelectronics Publications

Electrospun poly(vinylidene fluoride) (PVDF) and P(VDF-TrFE) nanofibers offer a mechanically flexible, high-surface-area platform for wearable bioelectronics, including piezoelectric sensing and self-powered operation. This review establishes a process–structure–property–performance framework for engineering electrospun PVDF- and P(VDF-TrFE)-based wearable systems. Relationships amongst precursor composition, electrospinning parameters, fiber morphology and alignment, crystalline phase formation, dipole orientation, and device-level piezoelectric performance are examined. Electrospinning is compared with other established fabrication methods including solution casting, spin coating, melt processing, phase inversion, and printing in terms of electroactive β-phase fraction, flexibility, scalability, manufacturing complexity, cost, and wearable-device suitability. Device architectures and performance are evaluated across pressure and strain …

Performance Assessment And Design Improvements For An Urban Coastal Detention Basin Under Intensifying Rainfall Extremes, Imiya Mudiyanselage Chathuranika, Agyare Asante, Faeghe Borhani, Xixi Wang, Mujde Erten-Unal, Dalya Ismael

Civil & Environmental Engineering Faculty Publications

Coastal urban areas are increasingly exposed to flooding driven by more frequent and intense rainfall events, rising sea levels, and expanding impervious surfaces. Norfolk, Virginia, a low-lying coastal city with aging stormwater infrastructure, faces heightened vulnerability to these hydrologic pressures. This study evaluates the hydraulic performance of an existing urban detention basin within the Edgewater–Larchmont catchment under 10-, 50-, and 100-year, 2-h design storms using the U.S. Environmental Protection Agency’s Storm Water Management Model (SWMM). Simulations were conducted for both pre- and post-development conditions to assess changes in peak discharge, storage capacity, and water level dynamics. Results show that urbanization, …

Optimizing Maintenance Routes For Highway Infrastructure Using Leader-Follower Autonomous Vehicles, Qing Tang, Chenxi Chen, Xianbiao Hu, Yuxin Ding, Tianjia Yang

Civil & Environmental Engineering Faculty Publications

The Autonomous Truck Mounted Attenuator (ATMA), a leader–follower style connected and automated vehicle system, enhances safety during transportation infrastructure maintenance in work zones. However, the significantly lower speed of ATMA, compared to regular vehicles, causes moving bottlenecks that reduce roadway capacity and prolong queuing, leading to further delays. Different ATMA routes lead to varying patterns of time-dependent capacity drop, affecting the user equilibrium traffic assignment and resulting in differing system costs. This study aims to optimize ATMA routing within a network to minimize the system cost associated with its slow-moving operation. To this end, a queuing-based traffic assignment approach is …

Short-Term Response Mechanisms Of Water Quantity And Quality Of Daihai Lake Under Temperature-Driven Changes, Hao Zhang, Xiaohong Shi, Xianhua Li, Junping Lu, Ruizhong Gao, Xixi Wang, Shuhao Zhang, Longmei Xie, Yu Liu

Civil & Environmental Engineering Faculty Publications

Temperature-driven mechanisms involving complex feedback and lag that affect the evolution of hydrological processes and ecological functions in cold- and arid-region lakes represent a core scientific issue in current hydrology and lake ecology research. In this study, based on month-scale temperature and environmental factor data from Daihai Lake in Inner Mongolia from January to December 2023, statistical methods (redundancy analysis, Tukey's test analysis, correlation analysis, structural equation modeling), time series analysis methods (dynamic time warping), and machine learning methods (random forest) were combined. A hierarchical and phased response framework was constructed that encompassed driver identification, path tracing, lag characterization, and …

Data Repository For: Dorsey Et Al., 'Rapid Transient Uplift Driven By Active Slab Tear, Southern Calabria, Italy', Rebecca J. Dorsey, Nathan D. Brown, Sergio G. Longhitano, Marco Meschis, Domenico Chiarella, Charles P. Ogle

Earth & Environmental Sciences Datasets

Southern Italy preserves a well-studied record of deformation and uplift related to migrating oblique collision, trench retreat, and tearing of subducted ocean slabs. However, the surface expression of these processes is incompletely understood due to a lack of reliable ages for marine terraces >200 m above sea level (masl). Here we use luminescence methods to date shallow marine sands from terraces ~ 100 to 1,000 masl in southern Calabria and interpolate the results with regional surface mapping. Burial ages overlap across the full range of sampled elevations with a weighted mean of 118.8 ± 8.7 thousand years before present (ka). …

Numerical Modeling Of Multi-Scale Jet Feedback In Seyfert Galaxies, Julianne Goddard

Theses and Dissertations--Physics and Astronomy

We present a suite of high-resolution cosmological zoom-in simulations investigating the impact of mechanical and thermal feedback from low-luminosity jets launched from supermassive black holes (SMBHs) on Seyfert galaxy evolution from early cosmic times to the present (z ≤ 10). Our models follow the formation of central galaxies within identical dark matter halos of logMhalo/M⊙ ∼ 11.8 at z = 0, seeded with ∼ 10^6M⊙ SMBHs at z ∼ 9.1 and z ∼ 3.7. Feedback from active galactic nuclei (AGN) is implemented in the form of bipolar jets launched along the SMBH spin axis, with jet powers spanning Ljet ∼ …

Taxonomy, Distribution And Ecology, Biology, Nutritional Composition, And Conservation Status Of Critically Endangered Lake Chala Tilapia Oreochromis Hunteri (Günther, 1889): Implications For Aquaculture Development, Jonathan Munguti, Mavindu Muthoka, Domitila Kyule, Esther Magondu, Jacob O. Iteba, Kevin Obiero, Paul Orina, Menaga Meenakshisundaram, Tanga M. Chrysantus

All Peer-Reviewed Publications

Oreochromis hunteri Günther, 1889, commonly known as Lake Chala tilapia, is an endemic cichlid restricted to the small crater lake, Lake Chala, on the border of Kenya and Tanzania. This review synthesizes existing knowledge on the taxonomy, and phylogenetic placement, distribution and ecology, and biology including nutritional composition, and conservation status of O. hunteri and identifies knowledge gaps to assess its potential for aquaculture development. The knowledge gaps include its reproductive biology, population structure, and adaptive capacity to environmental and climatic variability. As the type species of the genus Oreochromis, O. hunteri is a unique, genetically isolated lineage within a …

No Groundwater, No Fish: The Critical Role Of Groundwater In Supporting Non-Glacial, Salmon-Bearing Rivers In South-Central Alaska, Tyelyn Brigino, Kai Rains, Edgar Guerron-Orejuela, Jacob Argueta, Syverine Bentz, Coowe Walker, Mark Rains

School of Geosciences Faculty and Staff Publications

Groundwater discharge plays an important role in the hydrologic and ecologic functioning of rivers including sustaining streamflow and related habitat year-round. Simultaneously, groundwater supports the increasing demands of people as the global population continues to grow. Balancing the needs of users becomes increasingly important as climate change introduces greater uncertainty in water resources and fisheries, especially for economically important anadromous species that depend on freshwater resources. We investigated the seasonal and regional variability of groundwater contributions to six non-glacial mainstem salmon-bearing rivers in south-central Alaska. We hypothesized that groundwater contributes more than half of the annual streamflow, and nearly all …

Key-Point-Based Sign Language Translation With Multi-Stream Motion Modeling And Cross-Lingual Transfer, Alex Muchiri Kagozi

Dissertations and Theses

Automatic sign language translation remains challenging because models must recover linguistic structure from complex visual motion while relying on limited annotated data. This thesis investigates whether compact skeletal key-points can serve as an effective alternative to RGB video for continuous sign language recognition and translation, motivated by the privacy, computational efficiency, and transferability of skeletal representations. To address this problem, a segmented key-point-based framework is evaluated in continuous sign language recognition (Sign2Gloss), gloss-mediated translation (Sign2Gloss2Text), gloss-free translation (Sign2Text), and transfer learning within a common modeling pipeline. The proposed framework is a motion-aware multi-stream transformer encoder. Rather than processing the signer …

Factors Influencing Tree Vigor And Structural Failure Along Powerline Corridors In Northern California: A Multi-Species Analysis, David Justin Dorval

Cal Poly Humboldt theses and projects

Tree failures (e.g., stem breakage, uprooting, or branch drop) can pose significant risks to public safety, particularly along powerline corridors for the risk of power outages and wildfire ignitions. In California, tree-related powerline failures have led to severe ecological and human costs in recent years. Despite extensive research on tree failure in urban environments and post-disturbance contexts, comparatively little empirical work has examined where powerlines intersect largely unmanaged stands. This study evaluates tree failure trends across multiple forest types in Northern California by examining how tree-level characteristics influence recent growth and failure occurrence in regionally dominant tree species. Specifically, the …