Digital Commons Network^™

Assessing Forest Health And Small Mammals After A Limited Suppression Wildfire In Colville National Forest, Samantha J. Kennel

EWU Masters Thesis Collection

In the face of a wildfire crisis, there is a growing need to expand the pace and scale of forest treatments, including the strategic use of wildfire as a management tool. However, ecological outcomes remain uncertain in forests shaped by more than a century of fire suppression. The 2022 Thor Fire, a managed wildfire in the Colville National Forest, Washington, provides an opportunity to evaluate forest health in a relatively understudied system. Using a control–impact design, I established 100 monitoring plots in burned and adjacent unburned forests to assess how wildfire and fire severity influence forest structure, fuels, and tree …

Modeling The Effects Of Subsurface Tile Drainage On Atrazine And Nitrate Transport In Till Soils Of Iowa, Madison Hobbs

West Chester University Graduate Theses, Dissertations, and Final Projects

Tile drainage design influences groundwater flow pathways, residence time, and agrochemical transport in agricultural soils. This study evaluates how differences in drainage design affect subsurface flow behavior and contaminant transport in glacial till soils. A groundwater model was developed to compare conventional and controlled drainage systems with a shallow drainage system, and particle tracking was used to examine flow paths and residence times.

The results show that tile drainage creates distinct shallow and deeper flow pathways. In the conventional and controlled model, 88% of particles were captured by drains, while 12% reached the general head boundary. The shallow system drains …

Impact Of Land Use On Soil Properties, Clay Mineralogy, And Behavior Of Ammonium Across A Precipitation Gradient, Isabel Delamater

Theses and Dissertations--Plant and Soil Sciences

Like soil texture, clay mineralogy is considered a permanent or inherent characteristic of soils reflecting the influence of soil forming factors over long periods of time. Despite the importance of clay mineralogy in providing ecosystem services, much remains unknown about whether land use (native grassland sod versus no-tillage) can modify clay mineralogy under short periods of time. Furthermore, in no-tillage agroecosystems, lower crop yields are often ascribed to immobilization of added N fertilizer where inorganic nitrogen (such as ammonium) is converted to organic nitrogen. The possible role of clay mineralogy in retaining added ammonium has been overlooked in no-tillage systems. …

A Quantitative Analysis Of Burrowing And Subterranean Locomotion In The Sand Lance Using A Transparent Sediment, Issei Fujita, Makoto Tomiyasu, Jun Yamamoto, Yasuzumi Fujimori

Journal of Marine Science and Technology–Taiwan

Sand lances (Ammodytes spp.) rely on rapid burrowing into sediment for predator avoidance. Although their sediment grain-size preferences are well documented, the biomechanics underlying burrowing success remain unclear because natural substrates are opaque. Using a transparent-sediment system, we directly visualized and quantified burrowing kinematics to: (1) test the effect of body size (total length, TL) on burrowing success; (2) examine entry mechanisms (e.g., swimming speed, entry angle); and (3) describe locomotion within sediment. Logistic regression on the full dataset (N = 28 fish) identified TL as the primary determinant of success, with larger individuals exhibiting significantly higher success …

Examining The Spatial Variability Of Summer Heat In New York City Using Landsat Land Surface Temperature, Ryan Kabir

Dissertations and Theses

Extreme heat is the leading weather-related cause of death in the United States, and in New York City (NYC) approximately 370 heat-related deaths occur annually on average. The spatial distribution of surface heat exposure across NYC is highly uneven, low-income neighborhoods and communities of color consistently face higher temperatures, yet the intraurban spatial structure of land surface temperature (LST) during heatwave events has not been systematically characterized at high resolution. The New York City Panel on Climate Change Fourth Assessment (NPCC4) identifies this as an explicit research gap, noting that its own projections, based on global climate models downscaled to …

Climate Change Impacts On Hydrology In The Upper James Watershed, Imiya Mudiyanselage Chathuranika, Dalya Ismael

Engineering Technology Faculty Publications

Hydrological modeling of the Upper James Watershed (UJW), Virginia, is critical for predicting water availability, flood management, agriculture, ecosystem protection, and hydropower production under increasing climate change. The Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) is applied to evaluate climate change impacts on key hydrological components within the watershed. Future climate conditions were assessed for the near (NF: 2026-2050), mid (MF: 2051-2075), and far (FF: 2076-2100) periods using three Global Climate Models (GCMs) under Shared Socioeconomic Pathways SSP 2-4.5 and SSP 5-8.5. Climate data were bias-corrected using the Linear Scaling Method (LSM) and used to drive the HEC-HMS model. Results project …

Defining Clinically Meaningful Within-Patient Changes In Heart Failure: Jacc Heart Failure Position Statement, Amin Yehya, Rebecca Hahn, Barry Borlaug, Victoria Delgado, Andrew Ambrosy, Ross Arena, Nosheen Reza, Gregory Lewis, Natalie Tapaskar, Salvatore Carbone, Cynthia Chauhan, Mitchell A. Psotka, Norman Stockbridge, James Januzzi, Javed Butler, Jennifer Cowger, Biykem Bozkurt, John Spertus

Department of Medicine Faculty Publications

Heart failure (HF) clinical trials increasingly rely on diverse endpoints beyond mortality, including hospitalizations, imaging, hemodynamic status, biomarkers, functional capacity, and patient-reported outcomes. Although statistically significant differences between treatment groups are commonly reported, interpreting whether these changes are clinically meaningful for individual patients remains challenging. This JACC: Heart Failure position statement proposes a pragmatic framework for defining clinically meaningful within-patient change across commonly used HF outcomes by integrating measurement variability, prognostic associations, and patient-anchored evidence where available. Relative and absolute risk reductions should both inform interpretation of clinical events, while imaging, hemodynamic, biomarker, and functional measures require changes exceeding measurement …

A Comparative Study Of Classification Methods For Healthcare Analytics, Xueting Zhao

UNF Graduate Theses and Dissertations

This thesis presents a comparative study of logistic regression, Linear Discriminant Analy- sis (LDA), and Quadratic Discriminant Analysis (QDA) for binary classification in healthcare analytics, integrating theoretical derivation, simulation, and real-data application. A facto- rial simulation study crosses the covariance structure (equal vs. unequal), predictor correla- tion (ρ ∈ {0, 0.5, 0.9}), dimensionality (p ∈ {2, 5, 10}) and sample size (n ∈ {50, 100, 200}) across 54 scenarios with 1,000 Monte Carlo replicates each. Three main findings emerge. Logistic regression and LDA are nearly interchangeable when the assumption of equal-covariance holds. QDA achieves substantially better discrimi- nation when class-specific …

Can We Read Ai’S Mind? A Quest For Transparency, Santhosh Kumar Ravindran, Estera Kot, Fiona Fui-Hoon Nah

Research Collection School Of Computing and Information Systems

Although Artificial Intelligence (AI) systems are playing an increasing role in critical domains such as healthcare, finance, and autonomous systems, their decision-making processes remain largely opaque. This paper examines the challenges of AI transparency, addressing the “black box” problem using Explainable AI (XAI) techniques such as SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME). It also examines the ethical, regulatory, and societal implications of AI opacity and proposes a Comprehensive AI Observability (CAO) Framework that integrates deep explainability, provenance tracking, and real-time monitoring to enhance AI accountability. By bridging technical solutions with governance structures, this research emphasizes the …

Interpretable Machine Learning For In-Home Mild Cognitive Impairment Detection, Budhitama Subagdja, Shanthoshigaa D, Ah-Hwee Tan, Iris Rawtaer

Research Collection School Of Computing and Information Systems

This paper introduces a novel system for in-home cognitive health assessment using ambient sensors and a machine learning technology that can robustly detect mild cognitive impairment (MCI) despite limited available data. The learned model can explain the aspects of individuals’ daily lives led to the prediction, while reliably predicting MCI, providing more insights to healthcare workers for further clinical interventions. We developed the robust transparent machine learning model, based on fusion adaptive resonance theory (Fusion ART) neural network to learn individuals’ daily patterns of activity from continuous sensor data in terms of a suite of digital biomarkers reflecting four key …

Dual-Lora And Quality-Enhanced Pseudo Replay For Multimodal Continual Food Learning, Xinlan Wu, Bin Zhu, Feng Han, Pengkun Jiao, Jingling Chen

Research Collection School Of Computing and Information Systems

Food analysis has become increasingly critical for health-related tasks such as personalized nutrition and chronic disease prevention. However, existing large multimodal models (LMMs) in food analysis suffer from catastrophic forgetting when learning new tasks, requiring costly retraining from scratch. To address this, we propose a novel continual learning framework for multimodal food learning, integrating a Dual-LoRA architecture with Quality-Enhanced Pseudo Replay. We introduce two complementary low-rank adapters for each task: a specialized LoRA that learns task-specific knowledge with orthogonal constraints to previous tasks’ subspaces, and a cooperative LoRA that consolidates shared knowledge across tasks via pseudo replay. To improve the …

Analysis Theories On Artificial Intelligence, Chatgpt, Data Science, And Metaverse: The Case Of Digital Medicine, Yin Yang, Xingyun Liu, Jorge Luis Cuyubamba Dominguez, Yuan Fang, Wen Xie, Bairong Shen, Keng Siau

Research Collection School Of Computing and Information Systems

Healthcare organizations are increasingly adopting digital technologies, with Artificial Intelligence (AI), Data Science, and the metaverse driving significant advancements in smart healthcare. Al facilitates personalized medicine and efficient drug development, while Data Science enables predictive analytics and big data management, enhancing patient outcomes and healthcare quality. The metaverse introduces immersive training and telemedicine platforms, revolutionizing patient engagement and healthcare research. This study conducts' a scoping review of 6,171 articles, analyzing the transformational impact of AI, ChatGPT, Data Science, and the metaverse on healthcare. It highlights the benefits and risks of these technologies, identifies research gaps in their application within the …

Llamoco: Instruction Tuning Of Large Language Models For Optimization Code Generation, Zeyuan Ma, Yue-Jiao Gong, Hongshu Guo, Jiacheng Chen, Yining Ma, Zhiguang Cao

Research Collection School Of Computing and Information Systems

Recently, combining the strength of large language models (LLMs) and Evolutionary Computation (EC) has shown promising results for addressing optimization problems. It typically involves either iterative next-step solution seeking or directly prompting LLMs to generate critical optimization codes. However, these methods often suffer from low computational efficiency, high sensitivity to prompt design, and a lack of domain-specific knowledge. We introduce LLaMoCo, the first instruction-tuning framework designed to adapt LLMs for solving optimization problems in a code-to-code manner. LLaMoCo features a comprehensive instruction set that includes code-style problem descriptions as input prompts and robust optimization codes from expert EC optimizers as …

Bayesian Modelling On Periodically And Multiple Periodically Correlated Time Series Data, Jie Yao

Electronic Theses & Dissertations (2024 - present)

Time series with multiple periodically correlated (MPC) components present a complex challenge, with relatively limited prior research. Most existing models are designed for simpler periodically correlated (PC) components and often struggle with over-parameterization, optimization issues, and capturing complex PC patterns within a time series. Frequency separation techniques can help preserve the correlation structure of individual PC components, while Bayesian methods can integrate new and prior information to refine beliefs about these components. This study proposes a two-stage approach that combines frequency separation and Bayesian techniques to forecast PC and MPC time series data. This method aims to demonstrate improved effectiveness …

Analysis Of The Thermodynamic Variables Associated With Hawaiian Extreme Precipitation Using The Fraction Of Attributable Risk (Far), Matthew J. Sinnenberg

Electronic Theses & Dissertations (2024 - present)

The Hawaiian Islands experience extreme maxima in precipitation that vary spatially, by season, and in response to different teleconnection patterns. Due to the islands’ unique geography, they are not well-resolved by global climate models, necessitating a focused assessment to identify  climatological trends. In this study, a probability-based analysis was conducted on regionally averaged thermodynamic variables relevant to the formation of extreme events. The primary objective was to evaluate whether changes in the occurrence of extreme conditions have occurred and to understand how the El Niño-Southern Oscillation (ENSO) influences these occurrence rates.

Hourly data from ERA5 spanning 1980-2019 were resampled to …

Photo And Spatial Control Of Rna Structures And Functions: Exploring Positional Effects And Photo-Responsiveness Of Azobenzene Modified Nucleotides, Jinxi Du

Electronic Theses & Dissertations (2024 - present)

RNA molecules perform many important biological functions by forming complex three-dimensional structures stabilized by base pairing, tertiary interactions, and metal ion coordination. Because RNA function depends strongly on its structure, methods that allow reversible control of RNA conformation and activity are important for understanding RNA behavior and for potential therapeutic applications. However, achieving reversible and site-specific control of RNA structure, especially at the single-nucleotide level, remains challenging. In this dissertation, I developed and applied azobenzene-modified nucleotides as a chemical tool to enable optical and spatial control of RNA structure and function.

The first part of this dissertation focuses on establishing …

Spectral Analysis Of Traffic Accidents In New York's Capital Region, Michael Barr

Electronic Theses & Dissertations (2024 - present)

In this paper we estimate the spectral density of traffic accident events in the Capital Distict, NY area using a band-pass filter known as the Kolmogorov-Zurbenko Fourier Transform (KZFT). The source data is provided by Moosavi, et al. (2019) and originally captured from various public entities and sensors in the road network. Spectral density estimation with KZFT suppresses noise to reveal the constituent frequencies embedded in the noisy signal. Signal reconstruction based on KZFT produces an approximate weekly accident arrivals for this noisy signal, or in other words a pattern which is proportionate to the event expectation viewed over a …

The Chemistry Of Red Lipstick: The Impact Red Lipstick Has On Society, Lilliana Weldeslassie

Electronic Theses & Dissertations (2024 - present)

Cosmetic chemistry is deeply embedded in everyday life, shaping the products people use from morning to night. Among these, red lipstick stands out as a formulation that blends complex chemistry with a cultural meaning. This thesis examines the scientific foundations of red lipstick through its chemical composition, pigment structure, toxicological history, analytical methods, and evolving sustainability practices. Historically, red lipsticks relied on high-risk ingredients such as lead, mercury, and other toxic metals, reflecting an era before chemical safety and regulatory oversight were incorporated.²¹ Modern formulations have established safer synthetic dyes, natural waxes, plant-based oils, and stabilizers created to optimize …

Artificial Intelligence, Fundamental Motives, And Evolutionary Mismatch, Amy J. Lim, Jose. C. Yong, Edison Sora Tan

Research Collection School of Social Sciences

In recent years, the intersection of artificial intelligence (AI) and psychology has garnered unprecedented attention, particularly following the advent of generative AI tools in 2022. These tools, capable of producing human-like text, images, and even deepening our understanding of cognitive processes, have not only captured the public imagination but also sparked new concerns and debates within the psychological community. While AI has been a subject of research for decades, the emergence of its generative capabilities has truly thrust AI into the spotlight. This article explores how these advancements are reshaping our understanding of human cognition and behavior, as well as …

Challenges And Best Practices For The Development Of Coastal Hazard Decision Support Tools, Noah Hallisey

Open Access Dissertations

There is growing interest in developing Decision Support Tools (DSTs) supporting emergency managers and other decision-makers during coastal storm events. Yet many tools fail to inform decision-making due to challenges associated with their development and transition into operational tools for real-time decision-making. This dissertation investigates these challenges and offers best practices for their development and implementation for real-world decision-making through a literature review, interviews with subject matter experts in the United States, and a real-world case study.

Manuscript 1 reviews existing published literature to understand whether and how tool developers are incorporating emergency managers in the development of DSTs and …

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 …

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%) …

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 …

Digital Technologies And Calculus: Students’ Peaks And Pits, Ricela Feliciano-Semidei, Kevin A. Palencia Infante, Alcibiades Bustillo-Zarate

Faculty Articles, Papers, and Other Scholarship

Understanding calculus students’ perspectives can provide valuable insights into their learning needs and help develop strategies to enhance persistence in STEM programs. Numerous studies have shown that the use of digital technologies (DT) influences student engagement, motivation, and mathematics achievement. In this project, we explored students’ perspectives on using DT in calculus. We interviewed eight calculus students from a midwestern doctorate-granting institution in the United States and used thematic analysis informed by the didactical tetrahedron, accounting for internal and external factors that may influence the teaching and learning process. Students reflected on their use of technology, identifying various benefits, including …

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 …

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 …