Digital Commons Network^™

Multiscale Effects Of Landscape Structure, Biodiversity, And Chronic Physiological Stress In Atlantic Forest Small Mammals, Lizette Arroyo

Theses and Dissertations from DePaul University

Habitat loss and fragmentation are major drivers of biodiversity decline and can alter the physiological condition of wildlife through changes in habitat quality, resource availability, and community composition. The Atlantic Forest of South America is one of the world’s most threatened biodiversity hotspots. This study examined relationships among biodiversity, landscape structure, and chronic stress in nonvolant small mammals within the Reserva Natural Tapytá in eastern Paraguay using hair glucocorticoid concentrations as indicators of long-term physiological stress. Small mammals were sampled from forest fragments representing multiple patch sizes between 2023 and 2025. Hair cortisol and corticosterone concentrations were quantified using enzyme …

Llm-Driven Mission Control And Autonomous Planning For Search-And-Rescue Uavs: A Simulation-Based Evaluation, Naser Bader Alsaedi

Theses

Unmanned aerial vehicles (UAVs) are increasingly used in search‑and‑rescue (SAR) missions, yet many systems still rely on fragmented software where mission design, perception, and flight control are configured separately. This thesis examines whether a unified AI‑driven framework can reduce configuration effort and operator workload in UAV‑based SAR operations. The proposed system integrates natural‑language mission specification using a large language model (LLM) (LLaMA 3.1), autonomous coverage planning, YOLOv8‑based victim detection, and PX4/MAVSDK control within a single architecture. Operators describe missions through free‑form text or a graphical interface; the model converts these descriptions into structured mission parameters that are automatically planned and …

A Comprehensive Calibration Framework For The Northwest River Forecast Center, Geoffrey Walters, Cameron Bracken, Brad Gillies, Leah Pope, Henry Pai, Sonali Chokshi, Victor Stegemiller, Stephen King, Julie Bracken, Taylor Dixon, Multiple Additional Authors

Mathematics and Statistics Faculty Publications and Presentations

We present a comprehensive framework developed by the Northwest River Forecast Center for calibrating hydrologically diverse basins. The framework includes models for snow, soil moisture, routing, channel loss, and consumptive use. Data inputs include a wide range of open-access datasets for meteorology, land use, topography, and land cover. The framework uses conceptual hydrologic models to handle basins with various hydrologic regimes including rain-driven and snowmelt-dominated basins. We also develop a flexible automatic calibration system that can handle numerous unobservable model parameters in a computationally efficient manner. A single-basin automatic calibration run can typically be completed on a modern laptop in …

Modeling And Constraining Beyond Standard Model Neutrino Self-Interactions, Sabrina Hanning , ‘26

Senior Theses, Projects, and Awards

Neutrinos are the ideal subject for theories of Beyond Standard Model (BSM) physics. This is because small neutrino interaction cross-sections make them incredibly difficult to detect and result in many open questions regarding neutrino properties, particularly those that do not fit nicely into the Standard Model of particle physics, such as the existence of neutrino masses. BSM neutrino self-interactions are an interesting possibility with connections to dark matter candidates, neutrino mass generation, and in general any measurement that relies on accurate modeling of incident astrophysical neutrino flux.

These self-interactions may take various forms, with different mediator particles; we present a …

Trash Bot!, Owen Hoffman , ‘26, Ryder Maston , ‘26, Jack Nicholson , ‘26

Senior Theses, Projects, and Awards

Trash Bot! is the prototype of a smart and mobile waste receptacle for Swarthmore’s waste management system. With Swarthmore College committing to embed zero waste into campus culture and achieve a 90% diversion rate by 2035, flexible waste systems must be developed and improved. Trash Bot! seeks to improve the diversion rate and sort accuracy on campus by reducing the human error present in the current waste sorting process. Trash Bot! was also designed to seamlessly integrate with a classroom environment, maintaining safety standards and avoiding collisions. Trash Bot! uses computer vision coupled with a trained classifier algorithm to achieve …

Monitoring Terrestrial Ecosystem Productivity Using Hyperspectral Satellite Data, Serge Tuyambaze

School of Natural Resources: Dissertations, Theses, and Student Research

Accurate estimation of terrestrial Gross Primary Productivity (GPP) is critical for quantifying global carbon sequestration and understanding the terrestrial carbon cycle. While eddy covariance (EC) towers provide standard flux measurements, upscaling these measurements to continuous global coverage remains challenging due to the sparse distribution of flux towers and their small footprints. Remote sensing (RS), especially coupled with eddy covariance data, has been used to estimate GPP at the global scale. Different RS derived productivity models have been developed, including vegetation indices, light use efficiency (LUE) models, solar-induced fluorescence (SIF), dynamic global vegetation models (DGVM), and machine learning. One widely adapted …

Impact Of In Situ Observations From Three Torus Cases On Model Analyses And Forecasts Of Severe Convection, Robert M. Szot

Department of Earth and Atmospheric Sciences: Dissertations, Theses, and Student Research

The quality of forecasts of severe deep convection in high-resolution numerical weather prediction is dependent on the representativeness of the model initial conditions. This representativeness may be negatively impacted by limited observation availability and by the presence of mesoscale heterogeneities that were not detected by conventional observations assimilated into convection-allowing models. By assimilating storm-scale observations from the Targeted Observation by Radars and Uncrewed Aircraft Systems (UAS) of Supercells (TORUS) campaign into an ensemble styled after the Warn-on-Forecast System, this study aims to investigate if data from field work platforms can improve the quality of initial conditions in the ensemble, potentially …

Ultra-High Vacuum Outgassing Characterization Of Thermally Processed Low-Carbon Steel For Advanced Particle Accelerator And Gravitational Wave Detector Applications, Aiman H. Al-Allaq

Mechanical & Aerospace Engineering Theses & Dissertations

This dissertation investigated AISI 1020 low-carbon steel as an alternative vacuum chamber material to conventional stainless steel for ultra-high vacuum (UHV) and extreme-high vacuum (XHV) applications. After a 400 °C/48 h bake, AISI 1020 tube chambers achieved a hydrogen outgassing rate of 2.4 × 10⁻¹⁶ Torr·L·s⁻¹·cm⁻², approximately 2,300 times lower than the prebaked 316L stainless-steel comparator, among the lowest hydrogen outgassing rates ever reported for an uncoated metallic vacuum chamber. Bare and magnetite-coated AISI 1020 chambers were then compared using throughput and rate-of-rise methods. The magnetite coating yielded 5× lower water outgassing at room temperature, but this advantage disappeared after …

Freedom Readers And Sustainable Development Goal 4: Advancing Literacy In Low-Income Communities Of South Carolina, Madison Hayes

Goal 4: Quality Education

No abstract provided.

Generative Ai In Enterprises: Optimizing Applications With Large Language Models, Donghao Huang

Dissertations and Theses Collection (Open Access)

This dissertation investigates how to deploy Large Language Models (LLMs) effectively in enterprise settings, where accuracy, reliability, cost, privacy, and operational constraints often matter more than benchmark performance alone. Drawing on seventeen peer-reviewed publications (eleven published and six accepted for publication), the work develops and validates optimization strategies across three connected themes: retrieval-augmented generation (RAG), agentic AI for workflow automation, and deployment guidelines for real-world enterprise environments.

First, we study RAG optimization through systematic evaluation of open and proprietary models, highlighting conditions under which efficient open-weight models can match or exceed proprietary alternatives. To address a pervasive failure mode in …

Explainable Artificial Intelligence In The Image Domain And Its Applications To The Medical Field, Mirtha Lucas

Theses and Dissertations from DePaul University

This dissertation investigates the development of Explainable Artificial Intelligence (XAI) methods for deep learning models in the image domain, with a particular focus on medical imaging applications. Although neural networks achieve high predictive performance, their lack of interpretability limits their adoption in critical domains such as healthcare, where transparency and trust are essential. This work addresses this challenge by proposing novel approaches that improve the interpretability and reliability of model predictions.   A primary contribution is the introduction of Riemann–Stieltjes Integrated Grad-CAM (RSI Grad-CAM), a gradient-based attribution method that generates more relevant and spatially localized saliency maps. The method is evaluated …

Statistical Investigation Project, Ahmad Almomani Ph.D.

School of Arts & Sciences

The assignment was developed by SUNY Geneseo Professor Ahmad Almomani for the course, MATH 242: Elements of Probability and Statistics in the spring 2026 semester.

The objective for this assignment is that students will apply statistical methods to analyze real-world data and communicate meaningful conclusions.

Agentspec: Customizable Runtime Enforcement For Safe And Reliable Llm Agents, Haoyu Wang, Christopher M. Poskitt, Jun Sun

Research Collection School Of Computing and Information Systems

Agents built on LLMs are increasingly deployed across diverse domains, automating complex decision-making and task execution. However, their autonomy introduces safety risks, including security vulnerabilities, legal violations, and unintended harmful actions. Existing mitigation methods, such as model-based safeguards and early enforcement strategies, fall short in robustness, interpretability, and adaptability. To address these challenges, we propose AgentSpec, a lightweight domain-specific language for specifying and enforcing runtime constraints on LLM agents. With AgentSpec, users define structured rules that incorporate triggers, predicates, and enforcement mechanisms, ensuring agents operate within predefined safety boundaries. We implement AgentSpec across multiple domains, including code execution, embodied agents, …

Hypersiniel: Guaranteed Output Delivery Comes (Almost) Free In Private Delegation Of Zksnarks, Yunbo Yang, Yuejia Cheng, Junkai Liang, Kailun Wang, Xuanming Liu, Xiaoguo Li, Jianfei Sun, Jiachen Shen, Xiaolei Dong, Zhenfu Cao, Meng Hao, Guomin Yang, Deng, Robert H., Kui Ren

Research Collection School Of Computing and Information Systems

Zero-knowledge Succinct Non-interactive Argument of Knowledge (zkSNARK) is a powerful cryptographic primitive that enables a prover to convince a verifier that something is true without leaking the private witness.Current zkSNARKs face significant computational costs in generating proofs, which restricts their use in areas like private payments, confidential smart contracts, and anonymous credentials. Private delegation offers a practical solution by outsourcing the heavy computation to powerful external workers without leaking any private information. In this work, we propose HyperSiniel, an efficient private delegation framework for general zkSNARKs that achieves a new feature called guaranteed output delivery (GOD). HyperSiniel is designed to …

Bridging Draft Policy Misalignment: Group Tree Optimization For Speculative Decoding, Shijing Hu, Jingyang Li, Zhihui Lu, Pan Zhou

Research Collection School Of Computing and Information Systems

Speculative decoding accelerates large language model (LLM) inference by letting a lightweight draft model propose multiple tokens that the target model verifies in parallel. Yet existing training objectives optimize only a single greedy draft path, while decoding follows a tree policy that re-ranks and verifies multiple branches. This draft policy misalignment limits achievable speedups. We introduce Group Tree Optimization (GTO), which aligns training with the decoding-time tree policy through two components: (i) Draft Tree Reward, a sampling-free objective equal to the expected acceptance length of the draft tree under the target model, directly measuring decoding performance; (ii) Group-based Draft Policy …

From Spatial To Actions: Grounding Vision-Language-Action Model In Spatial Foundation Priors, Zhengshen Zhang, Hao Li, Yalun Dai, Zhengbang Zhu, Lei Zhou, Chenchen Liu, Dong Wang, Francis E. H. Tay, Sijin Chen, Ziwei Liu, Yuxiao Liu, Xinghang Li, Pan Zhou

Research Collection School Of Computing and Information Systems

Existing vision-language-action (VLA) models act in 3D real-world but are typically built on 2D encoders, leaving a spatial reasoning gap that limits generalization and adaptability. Recent 3D integration techniques for VLAs either require specialized sensors and transfer poorly across modalities, or inject weak cues that lack geometry and degrade vision-language alignment. In this work, we introduce FALCON (From Spatial to Action), a novel paradigm that injects rich 3D spatial tokens into the action head. FALCON leverages spatial foundation models to deliver strong geometric priors from RGB alone, and includes an Embodied Spatial Model that can optionally fuse depth, or pose …

Elementary Abelian Sylow Subgroups Of The Multiplicative Group, Geremías Polanco

Mathematics Sciences: Faculty Publications

Erdős and Pomerance have shown that 𝜑⁡(𝑛) typically has about 1 2 ⁢(log⁡log⁡𝑛)2 distinct prime factors. More precisely, 𝜔⁡(𝜑⁡(𝑛)) has normal order 1 2 ⁢(log⁡log⁡𝑛)2. Since 𝜑⁡(𝑛) is the size of the multiplicative group (𝐙/𝑛⁢𝐙)×, this result also gives the normal number of Sylow subgroups of (𝐙/𝑛⁢𝐙)×. Recently, Pollack considered specifically noncyclic Sylow subgroups of (𝐙/𝑛⁢𝐙)×, showing that the count of those has normal order log⁡log⁡𝑛/⁢log⁡log⁡log⁡𝑛. We prove that the count of noncyclic Sylow subgroups that are elementary abelian of a fixed rank 𝑘 ≥2 has normal order 1 𝑘⁢(𝑘−1) ⁢log⁡log⁡𝑛/⁢log⁡log⁡log⁡𝑛. So for example, (typically) among the primes p for …

The Importance Of Body Size And Spatial Scale In Modeling Winter Bird Distributions, Carissa M. Mobley

Student Publications

Birds’ annual cycles include biologically linked breeding and non-breeding seasons, yet most research and bird atlases focus exclusively on the breeding season. There have been very few winter bird atlas projects in North America, and none have been published for the northeastern United States. Short daylight hours and harsh weather limit winter survey coverage, making species distribution models (SDMs) essential for addressing spatial gaps.

I evaluated the optimal spatial scale for SDMs across bird species differing in body size and tested whether optimal scale varies with body mass. Using 16,967 eBird checklists collected during the first winter survey period (December …

Interspecific Overlap In Seabird Diet Throughout The Breeding Season, Alexis V. Berkey

Student Publications

Seabirds are central-place foragers that breed in multi-species colonies, providing an opportunity to study species coexistence. I quantified interspecific competition among four seabird species (common terns Sterna hirundo, Arctic terns Sterna paradisaea, Atlantic puffins Fratercula arctica, black guillemots Cepphus grylle) breeding in the Gulf of Maine in 2022, 2024, and 2025. I hypothesized that, throughout the breeding season, 1) seabird diet would change and 2) interspecific overlap in diet would increase. I compared diets across species and time using direct observation of prey feeds and stable isotope values of seabird egg membranes and blood, which provide …

Ai-Assisted 3d Pre-Visualization: Streamlining Animatics For Filmmakers, Nishit Thapa

Honors Theses

Pre-visualization is a core part of film pre-production. Creators use it to test blocking, lighting, camera movement, and spatial relationships before committing to a shot. Despite this, three-dimensional pre-visualization remains out of reach for many entry-level filmmakers, as the software is expensive and takes substantial time to learn.

Recent AI developments have produced generative systems capable of creating highly re-alistic imagery and video from text prompts. These systems open new possibilities for visual sto-rytelling but are not built for the iterative, hands-on process that pre-visualization requires, where creators must adjust scene parameters, camera logic, and spatial configurations throughout.

This thesis …

Exploring The U.S. Public Service Employees’ Experiences With Gamified Cybersecurity Awareness Training, Bisola Adepoju

Human Resource Development Theses and Dissertations

The purpose of this qualitative study was to explore the U.S. public service employees' experiences with gamified cybersecurity awareness training. The primary research question guiding this inquiry was: How do U.S. public service employees make sense of their lived experiences with gamified cybersecurity awareness training? Two secondary questions examined what aspects of the training employees perceive as meaningful or disengaging, and how they interpret their motivation, engagement, and cybersecurity awareness during training. I chose an interpretive qualitative research design grounded in a constructivist paradigm and purposefully selected 11 U.S. public service employees who had participated in gamified cybersecurity awareness training. …

Information Theory Analysis Of The Solar Wind Magnetic Structures For Space Weather Prediction, Katherine Holland

Doctoral Dissertations and Master's Theses

Forecasting space weather at Earth is highly complicated, because of the limited measurements of the dynamic processes in the Sun that span multiple temporal, spatial, and energy-scales. The solar wind is a highly structured, multi-scale, evolving plasma and consists of coronal mass ejections (CMEs), stream interaction regions (SIRs), expanding flux tubes (Borovsky, 2008), and interplanetary magnetic field (IMF) discontinuities and fluctuations. The aim of this research is to improve our understanding of the evolution and dissipation of different scale-size solar wind magnetic structures as they move from the Sun-Earth Lagrange point 1 (L1) to Earth's bow shock and, ultimately, to …

Learning Feature Inversion For Multi-Class Anomaly Detection Under General-Purpose Coco-Ad Benchmark, Jiangning Zhang, Chengjie Wang, Xiangtai Li, Guanzhong Tian, Zhucun Xue, Yong Liu, Guansong Pang, Dacheng Tao

Research Collection School Of Computing and Information Systems

Anomaly detection (AD) is often focused on detecting anomaly areas for industrial quality inspection and medical lesion examination. However, due to the specific scenario targets, the data scale for AD is relatively small, and evaluation metrics are still deficient compared to classic vision tasks, such as object detection and semantic segmentation. To fill these gaps, this work first constructs a large-scale and general-purpose COCO-AD dataset by extending COCO to the AD field. This enables fair evaluation and sustainable development for different methods on this challenging benchmark. Moreover, current metrics such as AU-ROC have nearly reached saturation on simple datasets, which …

Semat: Semantic Enhanced Natural Image Interactive Matting, Ruihao Xia, Yu Liang, Peng-Tao Jiang, Hao Zhang, Qianru Sun, Yang Tang, Bo Li, Pan Zhou

Research Collection School Of Computing and Information Systems

Recent approaches attempt to adapt powerful interactive segmentation models, such as SAM, to interactive matting and fine-tune the models based on synthetic matting datasets. However, models trained on synthetic data fail to generalize to complex and occlusion scenes. We address this challenge by proposing a new matting dataset based on the COCO dataset, namely COCO-Matting. It selects real-world complex images from COCO and converts semantic segmentation masks to matting labels. The built COCO-Matting comprises an extensive collection of 36,980 human instance-level alpha mattes in complex natural scenarios. Furthermore, existing SAM-based matting methods extract intermediate features and masks from a frozen …

Visual Loop: Bridging The Cognitive Gap In Software Development Through Visual-Ai Collaboration, Luis Filipe Fernandes Gomes, Xin Zhou, David Lo, Rui Abreu

Research Collection School Of Computing and Information Systems

Software development remains predominantly text-centric, despite decades of evidence showing that developers think and communicate visually. While sketches and diagrams externalize developers’ mental models, they remain disconnected from source code and quickly become outdated. Recent advances in foundation models, capable of both code and visual reasoning, create an opportunity to unify these representations. In this vision paper, we introduce Visual Loop, a continuous visual development environment that keeps code and informal sketches in bidirectional synchronization. Our prototype connects a code editor with a tablet-based visualization workspace, allowing developers to explore, annotate, and modify systems through freehand sketches interpreted by multimodal …

Can Financial Literacy Enhance Esg Disclosures?, Xiaoran Jia, Kiridaran Kanagaretnam, Kiat Bee Jimmy Lee, Chee Yeow Lim

Research Collection School Of Accountancy

Using an international sample of firms and two country-level measures of financial literacy, we find robust evidence of a positive relation between financial literacy and firms' Environmental, Social and Governance (ESG) disclosures. In cross-sectional analyses, we find that the effect of financial literacy in enhancing ESG disclosures is more prominent in poorer information environments, weaker legal institutions and weaker ESG reporting environments. Lastly, we find that financial literacy also enhances ESG performance. Our study contributes to and extends the literature by providing strong evidence that citizens' financial literacy enhances firms' ESG disclosure and the associated ESG performance.

On Autopilot? An Empirical Study Of Human-Ai Teaming And Review Practices In Open Source, Haoyu Gao, Peerachai Banyongrakkul, Hao Guan, Mansooreh Zahedi, Christoph Treude

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) increasingly automate software engineering tasks. While recent studies highlight the accelerated adoption of “AI as a teammate” in Open Source Software (OSS), developer interaction patterns remain under-explored. In this work, we investigated project-level guidelines and developers’ interactions with AI-assisted pull requests (PRs) by expanding the AIDev dataset to include finer-grained contributor code ownership and a comparative baseline of human-created PRs. We found that over 67.5% of AI-co-authored PRs originate from contributors without prior code ownership. Despite this, the majority of repositories lack guidelines for AI-coding agent usage. Notably, we observed a distinct interaction pattern: AI-co-authored PRs …

Continuum Observations Of Water Maser Sites, Aman Khan

Theses and Dissertations from DePaul University

Stars form in cores deep within cold (10 K) clouds of hydrogen gas. As material is accreted onto the central object inside these cores (the protostar), powerful outflows that channel mass away from the protostar are also launched. The launching mechanism for these outflows is still not fully understood. This thesis presents observations of the 22 GHz continuum emission from two star-forming regions, NGC 7129 FIRS2 and IC 1396-n. The immediate aim of these observations is to find where this continuum emission is coming from, particularly if it is free-free emission from a jet or outflow, and the long term …

Portrait Shadow Removal Via Self-Exemplar Illumination Equalization, Qian Huang, Cheng Xu, Guiqing Li, Ziheng Wu, Shengxin Liu, Shengfeng He

Research Collection School Of Computing and Information Systems

We introduce the Self-Exemplar Illumination Equalization Network, designed specifically for effective portrait shadow removal. The core idea of our method is that partially shadowed portraits can find ideal exemplars within their non-shadowed facial regions. Rather than directly fusing two distinct classes of facial features, our approach utilizes non-shadowed regions as an illumination indicator to equalize the shadowed regions, generating deshadowed results without boundary-merging artifacts. Our network comprises cascaded Self-Exemplar Illumination Equalization Blocks (SExmBlock), each containing two modules: a self-exemplar feature matching module and a feature-level illumination rectification module. The former identifies and applies internal illumination exemplars to shadowed areas, producing …

Patchgpt: Multi-Agent Patch Backporting Without Model Fine-Tuning, Ye Liu, Ruidong Han, Chengyan Ma, Yuqing Niu, David Lo

Research Collection School Of Computing and Information Systems

Patch backporting is crucial and prevalent in the maintenance of modern open-source software such as Linux kernels and forked repositories. However, porting patches across program versions remains a challenging problem due to the complexity of synergizing diverse patches with divergent program versions. In this paper, we propose PatchGPT, an agentic patch backporting framework for fine-grained patch generation. PatchGPT encompasses three agents: Miner for decomposing a sequence of atomic change steps as the original patch plan, Adapter for adapting the patch plan, and Executor for executing the adapted patch plan according to predefined change semantics. We conduct experiments on the PPatHF’s …