Physical Sciences and Mathematics Commons^™

Markov Chain Wave Generative Adversarial Network For Bee Bioacoustic Signal Synthesis, Kumudu Samarappuli, Iman Ardekani, Mahsa Mohaghegh, Abdolhossein Sarrafzadeh

Electrical & Computer Engineering Faculty Publications

This paper presents a framework for synthesizing bee bioacoustic signals associated with hive events. While existing approaches like WaveGAN have shown promise in audio generation, they often fail to preserve the subtle temporal and spectral features of bioacoustic signals critical for event-specific classification. The proposed method, MCWaveGAN, extends WaveGAN with a Markov Chain refinement stage, producing synthetic signals that more closely match the distribution of real bioacoustic data. Experimental results show that this method captures signal characteristics more effectively than WaveGAN alone. Furthermore, when integrated into a classifier, synthesized signals improved hive status prediction accuracy. These results highlight the potential …

Interpretable Battery Soh Prediction: A Comparative Interpretability Framework For Multi-Architecture Ml Models, Shafiyee Islam, Gon Namkoong

Electrical & Computer Engineering Faculty Publications

This work introduces a unified interpretability-efficiency framework for lithium-ion battery state of health (SOH) prediction using hybrid deep learning architectures. We comparatively analyze four hybrid models: CNN LSTM MultiHead, CNN Feature Extractor LSTM, DNN LSTM, and DNN BiLSTM to disentangle how network topology, feature composition, and computational design influence both predictive fidelity and physical interpretability. By integrating Monte Carlo Shapley (MC Shapley), background occlusion SHAP (BoSHAP), and ablation analysis, we quantify the contribution and robustness of five electrochemical feature groups: time, capacity, voltage, dQ/dV and peaks of dQ/dV from NASA battery dataset. The results reveal a consistent dominance of differential …

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. …

Stabilized Multi-Objective Optimization For Camouflage Attack In Remote Sensing, Omid Rajabi Rostami, Kazi Aminul Islam, Liuwan Zhu, Rui Ning, Chungsheng Xin, Hongyi Wu, Jiang Li

Electrical & Computer Engineering Faculty Publications

We investigate the vulnerability of multi-task maritime surveillance systems in which object detection and semantic segmentation operate concurrently. We propose Stabilized Multi-Objective Optimization (SMO), a framework that jointly targets Mask R-CNN and U-Net with a single universal adversarial texture in a digital white-box setting, with additional cross-model transfer evaluation. SMO combines a logarithmic-inversion loss transformation, feature inconsistency, and a systematic comparison of six optimization strategies for seven heterogeneous objectives. The loss transformation controls loss-dependent gradient scaling, while a U-Net generator supplies a spatially coherent texture and a hull-aligned application function accommodates target geometry without explicit Expectation over Transformation (EoT) sampling. …

A Comprehensive Survey Of Prompt Engineering Techniques In Large Language Models, Tonmoy Debnath, Md Nurul Absar Siddiky, Muhammad Enayetur Rahman, Prosenjit Das, Antu Kumar Guha, Muhammad Rezaur Rahman, H. M. Dipu Kabir

Electrical & Computer Engineering Faculty Publications

Prompt engineering has arisen as a pivotal discipline in optimizing the performance of Large Language Models (LLMs) by structuring inputs to enhance coherence, accuracy, and task alignment. This paper comprehensively surveys various prompting techniques, systematically categorizing them according to their application domains and methodological foundations. Fundamental approaches like zero-shot and few-shot prompting are examined along with advanced strategies, including chain-of-thought reasoning, retrieval-augmented generation, and self-consistency mechanisms. A rigorous qualitative analysis is conducted to evaluate each technique's strengths, limitations, and optimal use cases, offering a structured framework for selecting the most effective prompting strategies. Theoretical insights and empirical findings are consolidated …

Application Of Organic Geochemistry To The Characterization Of Hydrochar And Biochar: Insights Into Composition And Optimization, Michael A. Kruge, Teresa A. Centeno, Alvaro Amado-Fierro, José Manuel González-Lafuente, Rubén Forján, José Luis Gallego

Department of Earth and Environmental Studies Faculty Scholarship and Creative Works

Hydrothermal carbonization has garnered significant attention in recent studies due to potential applications of the hydrochar produced, which overlap with those of torrefied biochar, including soil improvement, carbon sequestration, and energy production. However, unlike the relatively uniform properties of biochar, hydrochar characteristics can vary greatly depending on the feedstock used. Advanced characterization techniques are essential to better understand these materials and enhance their practical applications. Research on hydrochar requires a multi-faceted approach, including molecular-level studies, to optimize production processes and identify specific applications.

In this regard, analytical methods commonly used in organic geochemistry could play a crucial role in deepening …

The Influence Of Scale In Modeling Social Vulnerability And Disaster Assistance, Sina Razzaghi Asl, Oronde Drakes, Eric Tate, Samuel Brody, Wesley Highfield, Kayode Atoba

Political Science & Geography Faculty Publications

Understanding how social vulnerability relates to disaster impacts is critical for addressing social equity, yet the role of spatial scale in this relationship is often overlooked. Most studies use aggregated data, risking ecological fallacy-misinterpreting individual outcomes from group-level data. This study examines how spatial scale influences the relationship between social vulnerability and federal disaster assistance after Hurricane Harvey. Using spatial econometric models at both household and census tract levels, we assessed the strength of key vulnerability indicators in explaining disaster assistance. Results show that disability, housing tenure, household size, and income predict assistance at the household level, but their influence …

Beyond Discrete Indicators: Modeling Intersectional Flood Vulnerability, Sina Razzaghi Asl, Eric Tate, Christopher T. Emrich, Md Asif Rahman, Kaeleb Royster

Political Science & Geography Faculty Publications

Social vulnerability to flooding is shaped by intersectional social marginalization, yet most quantitative assessments employ indicators of single populations. This study applies spatial machine learning to examine how the intersectional social vulnerability indicators of poverty-race, poverty-housing tenure, and race-housing tenure compare with traditional discrete indicators of single populations in predicting flood exposure in California. Using geographically weighted random forests and partial dependence plots, we model spatial heterogeneity and non-linear relationships between social vulnerability and exposure. We quantified flood exposure using a population-adjusted measure derived from building footprints and modeled 500-year fluvial and pluvial flood hazard. The results reveal distinct explanatory …

Autonomous Weapons And Strategic Stability, Chick Edmond

Political Science & Geography Faculty Publications

The increasing number of artificial intelligence (AI) elements within military systems has introduced new forms of security dilemmas related to speed, level of secrecy, and transfer of responsibility from humans to machines. This article addresses the question of whether or how AI enabled autonomous weapons can lead to greater levels of strategic instability. Three causal mechanisms were determined by this study to potentially create destabilizing effects due to the introduction of autonomy; the first mechanism is a reduction in time available for decision making. The second mechanism involves the creation of multiple pathways of escalation. The third mechanism is the …

Global Evidence On Scaling Nature-Based Solutions For Compound Hazards, Sina Razzaghi Asl, Anahita Azadgar

Political Science & Geography Faculty Publications

Nature-based solutions (NbS) are increasingly promoted for climate adaptation to compound hazards, but systematic evidence of their effectiveness is still scarce. We address this gap with a systematic review of 38 studies. Each study was sorted by what its measurement design can support, not by what its authors claim. This produced four groups. Direct studies attribute a measured hazard reduction to the intervention, proxy studies infer protection from ecosystem condition, prioritization studies map suitable locations without estimating performance, and qualitative studies describe effectiveness without measuring it. We detect trends in geographic distribution, hazard combinations, intervention types, methodological approaches, and strength …

Small-𝑥 Asmptomatics Of The Leading-Twist Flavor Singlet Quark Tmds, Daniel Adamiak, M. Gabriel Santiago, Yossathorn Tawabutr

Physics Faculty Publications

In this paper, we investigate the small-𝑥 behavior of the flavor-singlet, leading-twist quark transverse-momentum-dependent parton distribution functions (TMDs) using the light-cone operator treatment. This formalism allows us to express TMD operators at small 𝑥 in terms of polarized dipole amplitudes, enabling a systematic approach to their small-𝑥 evolution. We derive the evolution equations for these TMDs and solve them within the large-𝑁𝑐 approximation under the linearized, double-logarithmic approximation, where 𝑁𝑐 represents the number of quark colors. Expanding on previous work on unpolarized and helicity TMDs, we present the small-𝑥 asymptotics for a comprehensive set of TMDs, including the Sivers function, …

Trigonometric Continuous-Variable Gates And Hybrid Quantum Simulations Of The Sine-Gordon Model, Tommaso Rainaldi, Victor Ale, Matt Grau, Dmitri Kharzeev, Enrique Rico, Felix Ringer, Pubasha Shome, George Siopsis

Physics Faculty Publications

Hybrid qubit-qumode quantum computing platforms provide a natural setting for simulating interacting bosonic quantum field theories. However, existing continuous-variable gate constructions rely predominantly on polynomial functions of canonical quadratures. In this work, we introduce a complementary universality paradigm based on trigonometric continuous-variable gates, which enable a Fourier-like representation of bosonic operators and are particularly well suited for periodic and non-perturbative interactions. We present an ancilla-based framework for implementing trigonometric gates with arguments given by arbitrary Hermitian functions of qumode quadratures. The protocol yields unitary gates deterministically, and non-unitary gates through probabilistic post-selection. As a concrete application, we develop a hybrid …

Geometry Of Almost-Conserved Quantities In Symplectic Maps: Approximate Invariants In Nonlinear Accelerator Systems, T. Zolkin, S. Nagaitsev, I. Morozov, S. Kladov

Physics Faculty Publications

We present a perturbative method for constructing approximate invariants of motion directly from the equations of discrete-time symplectic systems. This framework offers a natural nonlinear extension of the classic Courant-Snyder (CS) theory for systems with 1 degree of freedom—a foundational cornerstone in accelerator physics now spanning seven decades and historically focused on linear phenomena. The original CS formalism emerged under conditions where nonlinearities were weak, design goals favored linear motion, and analytical tools—such as the Kolmogorov-Arnold-Moser theory—had not yet been fully developed. While various normal-form methods have been proposed to treat near-integrable dynamics, the approach introduced here stands out for …

Synthesis Of The Naga(S1−Xsex)2 Solid Solution From Mechanically Activated Precursors, Louisiane Verger, Julien Trébosc, Santhoshkumar Sundaramoorthy, Amitava Choudhury, Olivier Hernandez, Eric Furet, Sébastien Chenu, David Le Coq, Laurent Calvez, Olivier Lafon

Chemistry Faculty Research & Creative Works

NaGaS₂ and NaGaSe₂ are two recently discovered compounds that crystallize in the same structure type. In this work, NaGa(S_1−xSe_x)₂ (x = 0.5, 0.75 and 1) are prepared using an alternative synthesis route, mechanochemistry followed by heat treatment. Na₂S, Na₂Se, Ga₂S₃ and Ga₂Se₃ are milled in stoichiometric proportions. Differential scanning calorimetry, X-ray diffraction and solid-state nuclear magnetic resonance (²³Na and ⁷¹Ga) show that the compounds after milling are composed of crystalline NaGa(S_1−xSe_x)₂ with an amorphous part. …

Designing Narrative-Based Ai Assistance For Sensemaking In Collaborative Environments: Case Studies In Education And Dementia Care, Dylan Edward Moore

Dartmouth College Ph.D Dissertations

This thesis addresses a gap in the human-computer interaction literature regarding the design, development, and evaluation of narrative-based AI assistance for collaborative, complex problem solving. I explore this design space through three case studies across the domains of education and dementia care. This work encompasses multi-year industry partnerships and longitudinal fieldwork, user-centered design, dataset curation, model training, and system evaluation.

Specifically, the first case study considers a story-based web platform for teaching AI literacy through peer-generated, personalized narrative scaffolding. Learners on the platform showed significant knowledge gains and other learning-related outcomes. To describe the novel design of this system, I …

Lifestyle Factors' Effect On Political And Religious Behavior, Madison A. Price

SPARK Symposium Presentations

There is a general understanding of an individuals’ political/religious beliefs when you analyze predictors like support of same sex marriage or views on abortion, but there is not a ton of research on how everyday lifestyle factors might affect how someone aligns themselves politically or religiously. Researchers have begun to expand the horizons of political and religious research by investigating how income, health, and politics affect religion (Francis-Tan & Tian, 2022), but there is a need to expand into more specific lifestyle factors. Which brings reason to question a few things: what lifestyle and demographic factors predict how religious someone …

Temporal Patterns Of Storm Surges: Event-Identification, Clustering, And Count Modeling, Ariadna Martin Oliva

Graduate Studies Theses and Dissertations 2026

Coastal regions face compounding risks when extreme events strike in rapid succession, yet most statistical frameworks treat such events as independent, a simplification that can critically underestimate hazard. This thesis addresses that gap through three interconnected contributions focused on the temporal clustering of storm surges at the global scale. A new automated method is developed to extract independent storm surge events from continuous tide gauge records. By leveraging event correlations and a soft-margin approach that accounts for local variability, the method identifies characteristic event durations across 1,485 global tide gauge stations, revealing the influence of both local site conditions and …

Driving Into The Past: Road Logs And The History And Geology Of The Eastern Sierra Of California, Char Miller

Eastern Sierra History Journal

Road Logs are a pedagogical tool that geologists use on field trips with their students. Dee Trent's 1982 Road Log, which reproduced in this issue of ESHJ, also functions as a cultural artifact; it demonstrates what was known then, and how that scientific knowledge and the human geography of the region has changed over time.

Road Log For The Natural History Of The Owens Valley And Eastern Sierra Nevada, D. D. Trent

Eastern Sierra History Journal

D.D. Trent, who taught Geology at Citrus College in Southern California for many years, developed this 1982 Road Log for his students for use on field trips. It contains many scientific findings and historical insights about the region and he also contributed hand-drawn maps and illustrations to capture their attention. The Road Log is introduced by Char Miller's essay, "Driving into the Past" and then annotated and updated by Prof. Nicole Moore in an article that follows.

D.D. Trent 1982 Natural History Of Owens Valley/Eastern Sierra: Annotations And Updates, Nicole E. Moore

Eastern Sierra History Journal

Prof. Moore offers some annotations to and updates of D.D. Trent's 1982 Road Log. These revisions are part of the evolution of scientific knowledge and the adaptation of new technologies that enable a deeper understanding of the geology of the Owens Valley and Eastern Sierra. Like Trent, Moore has routinely taken her students up US 395 to explore and assess the region's geological formations.

Last Glacial Maximum And Late Glacial Aeolian Sand Activity, South-Central Indiana, Usa, H. M. Loope, J. L. Antinao, P.R. Hanson, P. M. Jacobs, D. A. Grimley

Conservation and Survey Division: Faculty and Staff Publications

No abstract provided.

Report 17—Revisions To The Articles Of Organization And Procedure Of The Commission On Stratigraphic Nomenclature Of The Americas, Lynn T. Dafoe, Robert B. Macnaughton, Marie-Perre Aubry, Carlton E. Brett, James R. Lehane, Fernando Núñez-Useche, Randall C. Orndorff, Frank Brunton, Lucy E. Edwards, Richard H. Fluegeman, Howard Harper, Robert Matthew Joeckel, Norman P. Lasca, B. H. Tew

Conservation and Survey Division: Faculty and Staff Publications

Several revisions to the Articles of Organization and Procedure for the North American Commission on Stratigraphic Nomenclature have been adopted following the 75th, 79th, and 80th annual meetings of the Commission in 2020, 2024 and 2025, respectively. Of these, the most substantial change was revision of Article III regarding membership composition of the Commission and the addition of new member organizations from Central and South America and the Caribbean region. As a result, the Commission also voted to change its name to be more inclusive of the expanding membership, with approval of the name: Commission on Stratigraphic Nomenclature of the …

Rf Design Progress Of The 197 Mhz Crab Cavity For Eic, B. Xiao, J. C. Brutus, A. Blednykh, Z. Conway, J. Cox, J. R. Delayen, S. U. De Silva, R. Fernandes, J. Guo, D. Holmes, N. Huque, G. Li, Z. Li, J. B. Mandel, S. Verdú-Andrés, G. Wang, Q. Wu, W. Xu

Physics Faculty Publications

The interaction region (IR) crab cavity system is a special RF system designed to compensate for the luminosity loss caused by the 25 mrad crossing angle at the interaction point (IP) of the Electron-Ion collider (EIC). The configuration includes six crab cavities in the Hadron (proton or ion) Storage Ring (HSR) - four operating at 197 MHz and two at 394 MHz - installed on each side of the IP, along with one 394 MHz crab cavity on each side of the IP in the Electron Storage Ring (ESR). This paper presents the recent progress in the RF design of …

Dystop: Dynamic Staleness Control And Topology Construction For Asynchronous Decentralized Federated Learning, Yizhou Shi, Qianpiao Ma, Yan Xu, Junlong Zhou, Ming Hu, Yunming Liao

Research Collection School Of Computing and Information Systems

Federated Learning (FL) has emerged as a potential distributed learning paradigm that enables model training on edge devices (i.e., workers) while preserving data privacy. However, its reliance on a centralized server leads to limited scalability. Decentralized federated learning (DFL) eliminates the dependency on a centralized server by enabling peer-to-peer model exchange. Existing DFL mechanisms mainly employ synchronous communication, which may result in training inefficiencies under heterogeneous and dynamic edge environments. Although a few recent asynchronous DFL (ADFL) mechanisms have been proposed to address these issues, they typically yield stale model aggregation and frequent model transmission, leading to degraded training performance …

Purified Zero-Shot Sketch-Based Image Retrieval, Yang Zhou, Jingru Yang, Jin Wang, Kaixiang Huang, Guodong Lu, Shengfeng He

Research Collection School Of Computing and Information Systems

Sketches, as a new solution in multimedia systems that can replace natural language, are characterized by sparse visual cues such as simple strokes that differ significantly from natural images containing complex elements such as background, foreground, and texture. This misalignment poses substantial challenges for zero-shot sketch-based image retrieval (ZS-SBIR). Prior approaches match sketches to full images and tend to overlook redundant elements in natural images, leading to model distraction and semantic ambiguity. To address this issue, we introduce a distraction-agnostic framework, purified cross-domain matching (PuXIM), which operates on a straightforward principle: masking and matching. We devise a visual-cross-linguistic (VxL) sampler …

Integrated Seismic And Sequence Stratigraphic Analysis Of The Abu Madi Formation, Baltim Field, Nile Delta, Egypt, Nouran Mostafa Eissa, Mostafa Toni, Alhussein Adham Basheer, Amr Talaat, Amir Ismail

Trends in advanced sciences and technology

Despite the economic significance of the Messinian Abu Madi Formation as a primary gas reservoir target in the Nile Delta Basin, a high-resolution, field-scale integration of seismic sequence stratigraphy with quantitative petrophysical characterization remains lacking for the Baltim area. This study addresses this gap by presenting an integrated seismic sequence stratigraphic and petrophysical analysis of the Upper Miocene Abu Madi Formation within the prolific Baltim gas fields, offshore Nile Delta, Egypt. The objectives are to characterize its depositional architecture, delineate reservoir distribution, and assess hydrocarbon potential. The workflow synthesizes well log data from five key wells with a grid of …

Enhanced Hydrophilicity And Self-Cleaning Properties Of Tio₂ Thin Films On Glass For Photovoltaic Applications: Influence Of Precursor Concentration And Light Irradiation, Fatma Refaat, Hany Hashem, Mohamed Mahmoud Gouda, Ibrahim Ahmed, Khaled Abdelwahed, Ahmed Bakr El Basaty

Trends in advanced sciences and technology

This study investigates the modification of glass substrates with TiO₂ thin films to enhance surface hydrophilicity and self-cleaning behavior. The films were deposited on glass by spin coating using different precursor concentrations, followed by thermal treatment. Structural and optical properties were characterized using X-ray diffraction (XRD), scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), and UV–Vis spectroscopy, while surface wettability was evaluated through water contact angle measurements. XRD confirmed the formation of pure anatase TiO₂, while FTIR spectra verified the presence of Ti–O and Ti–O–Ti bonds. Wettability analysis showed that films prepared with the lowest precursor concentration exhibited the …

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 …

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 …