Digital Commons Network^™

Fighting Against Recruitment Scams: Theory-Driven Supervised Learning And Empirical Analysis For Digital Fraudulent Recruitment Posting Behavior, Tom (Tianteng) Wang, David (Jingjun) Xu, Keng Siau, Zhongju (John) Zhang

Research Collection School Of Computing and Information Systems

The number of recruitment postings on digital recruitment hiring platforms has increased since the COVID-19 pandemic. However, the weak surveillance and operations of these platforms, combined with the fact that most job seekers have relatively low vigilance and a strong desire for recruitment offers, enable scammers to easily deceive job seekers for their money and confidential information. In this work, we combine prevailing text mining techniques (i.e., ChatGPT with prompting engineering and supervised machine learning) with interpersonal deception theory (IDT) from social science to design an interpretable IT system to predict fraudulent recruitment postings on digital recruitment-hiring platforms. We compare …

Low-Vision Students’ Experiences With And Suggestions To Improve Desktop Virtual Reality: A Qualitative Study, Tanveer Mansur Syed

Theses and Dissertations

Visually impaired students have faced challenges in the field of education, particularly in science education. Not surprisingly, the unemployment rate for working-age visually impaired people has been above 70%. This persists despite federal laws to support the education and employment of visually impaired people,

Virtual Reality (VR) technology has evolved for several decades. Its use has proliferated in areas that include education. Today, VR science lab simulations are presented as a cost-effective alternative to traditional science labs. Research shows these to be viable substitutes to traditional science labs without a significant drop in learning outcomes. However, this researcher was unable …

An Experimental Study On Turbine-Based Energy Harvesters For Thermal Underwater Gliders, Stephen Edward Coster

Theses and Dissertations

Buoyancy-driven underwater gliders have been optimized for long-duration missions primarily for continual monitoring of the ocean’s water column. However, longer missions and higher-powered sensors have increased the energy requirements of gliders. While current solutions aim to increase battery capacity, the development of energy-harvesting systems (EHMs) could greatly improve mission capabilities in both ability and duration. This thesis discusses the experimental testing of an underwater glider with an EHM as detailed in the theoretical study, “Development of a Turbine-Based Ocean Thermal Energy Harvesting Mechanism Applied to the Underwater Glider” by Hongbo Hou, PhD. The rear-mounted turbine captures energy using the kinetic …

Degradation And Metabolic Analysis Of Synthesized Quorum-Sensing Signals By The Model Eukaryote Chlamydomonas Reinhardtii, Adam Josef Bach

Theses and Dissertations

Bacterial organisms can “talk” with one another on a colony-wide scale using a communication system known as quorum-sensing (QS) via chemical signals. These signals are produced at a basal rate throughout the life of the bacteria and are classified as autoinducers, which diffuse across the cell membrane. Once the critical concentration is reached, they begin to diffuse back into the cell and initiate a phenotypic change. As these signals exist outside the membrane of their producer, they are also accessible to other organisms. It has already been noted that some plant species uptake these compounds through their roots and break …

Aircraft Fault Detection Via Weight And Bias Analysis Of A Custom First Neural Network Layer, George Harrison Chen

Theses and Dissertations

Fault detection in aircraft is traditionally handled through redundant hardware and comparison algorithms to detect failures. Alternatives like model-based residual generation and data-driven approaches such as supervised fault classification and unsupervised anomaly detection have been explored, but they suffer from practical limitations; model-based methods require accurate system models, and data-driven methods have large constraints on the data limiting scalability and adaptability. This work presents a purely data-driven neural network architecture featuring a custom first layer designed for real-time fault detection where the weights and biases of this layer are used to detect faults. The network requires zero supervision and complements …

Manned Exploration Of Satellites Of The Outer Planets: An Analysis Of Current Technologies And A Discussion Of Advancements Required, Joshua Carson Meador

Theses and Dissertations

The Solar System’s outer planets have many satellites, some of which are known to be the most likely places to find extraterrestrial life in our celestial backyard. Multiple scientific missions have been sent to glean information from these satellites, yet the possibility of a manned expedition has long been exclusive to science fiction stories. This paper intends to collect and analyze the current state of space travel technology to explore the feasibility of such a mission with modern technology and to discuss which future advancements should be the focal point for making such missions a reality. To begin, data on …

Quantifying Noise In Monte Carlo Simulations Of Cosmic Rays In A Shock, Abhro Rahman

Theses and Dissertations

Cosmic rays are a phenomenon known to humankind from the early 1900s, and yet today the details of their physical behavior are not fully settled. While the observed spectral fluxes of cosmic rays follow a power-law trend when plotted against particle energy, the exact mechanisms under which they get to such high energies are still under study. This thesis will undertake a subsection of that task, where the Monte Carlo simulation of particle accelerations is described in Warren (2015), and we aim to identify and quantify the noise inherent in such simulations. This document takes a long path through particle …

Moodify: A Mood-Based Music Recommendation System, Meghana Kagitha

Theses and Dissertations

Music has long been recognised as a powerful tool for emotional regulation, yet existing music streaming platforms often fail to align song recommendations with a user's current emotional state. Moodify is a mood-based music recommendation system designed to bridge this gap by delivering personalised playlists that reflect how a user feels in real time.

This project presents the design, development, and evaluation of Moodify, a mobile application that leverages the Circumplex Model of Emotion to capture user mood through an intuitive two-dimensional valence-arousal interface. Rather than relying on text input or manual search, users plot their emotional state directly onto …

Adaptive Artificial Potential Field Guidance And Control For Autonomous Docking With Uncooperative And Unknown Spacecraft, Steven Holmberg

Theses and Dissertations

The increasing demand for on-orbit servicing (OOS), active debris removal (ADR), and space domain awareness (SDA) missions has increased the need for autonomous spacecraft rendezvous and proximity operations (RPO) with uncooperative and unknown targets. Traditional guidance and control methods are typically designed for cooperative systems with known geometry and state information. This work builds on previous research to develop and evaluate an artificial potential field (APF)-based control framework capable of autonomous operation with minimal prior target knowledge and applicability to both relatively static and tumbling spacecraft.

The proposed APF formulation incorporates established safety constructs from cooperative docking systems, including an …

Ecological Responses To Living Shoreline Restoration, Aaron S. Bland

Graduate Theses and Dissertations (2019 - present)

Coastal built communities and natural coastal ecosystems face similar and interrelated hazards: shoreline sediment erosion, shoreline retreat, and worsening storms, each of which is exacerbated by relative sea level rise. Living shorelines are coastal defense projects that incorporate natural habitats to provide more sustainable shoreline defense and improve the coastal ecosystem. Living shorelines have been widely adopted, but their performance has been highly variable, and the mechanisms of project success and failure are unclear. The overarching goal of this dissertation was to better understand the ecosystem responses to coastal restoration, focusing on the primary habitats of coastal salt marsh and …

Fatigue And Cyclic Loading In Aircraft Engine Turbomachinery, Sarah Hilliard

Senior Honors Theses

The fatigue and cyclic loading of aircraft engine turbomachinery was explored in order to better understand and predict its failure. This exploration included a review of central topics such as aircraft engine components, fatigue, and the microstructural nature of metal. Also, the ANSYS Mechanical microstructural crystal plasticity tool was evaluated for its utility in fatigue prediction studies. No validation of the tool in question was possible within the scope of this thesis. Numerical fatigue life prediction methods were discussed, and current advancements in metal coatings and surface treatments for prolonging the life of certain superalloys were investigated. Further work in …

Lyapunov-Krasovskii Based Approach To Time-Delay Systems: Computational Efficiency And Feedback Control, Huan Phan

Dissertations

Time-delay systems arise naturally in many engineering applications due to sensing,actuation, communication, and computation delays. The presence of delays has a significant influence on system performance both positively and negatively. Although Lyapunov--Krasovskii functional (LKF) methods provide a systematic framework for stability analysis and controller synthesis, their implementation often suffers from high computational complexity, particularly for large-scale systems and systems involving multiple feedback channels. This dissertation addresses both computational efficiency and feedback control for linear time-delay systems.The first part of this dissertation develops a decomposition-based approach to reduce the computationalburden of stability analysis. By exploiting structured invariant subspaces associated with the …

Intelligent Surveillance For Weapon Threat Detection: Transformer-Based Detection And Segmentation Using Thermal And Rgb Imagery, Akhila Kambhatla

Dissertations

Proactive public-safety surveillance requires deep learning systems that can detect and segment weapons reliably under real-world conditions. This research develops and evaluates a progressive, multi-phase pipeline that moves (i) from RGB object detection to pixel-level semantic segmentation and (ii) from visible-spectrum cameras to thermal imagery to improve robustness under low-light, clutter, and occlusion. Early phases establish strong detection baselines using one-stage and two-stage detectors (YOLOv5 and Faster R-CNN) for multi-class weapon localization, followed by practical optimization through pruning and ensembling to reduce false positives and computational overhead while maintaining real-time feasibility. Building on the limitations of bounding-box localization, the methodology …

Analysis Of Collective Behavior In Living And Nonliving Systems, Kaitlyn Cohan

Theses, Dissertations and Culminating Projects

This thesis aims at understanding the phenomenon of of self-organization in complex dissipative systems, living and nonliving. Dissipative systems are characterized by their search for energy, interactions with their surroundings and the production of entropy, all of which result in the creation of stable structures or patterns, which persist as long as the initial environmental conditions are maintained. The two specific models that we chose to study here are (a) Futbol (or Soccer) and (b) a chemical system involving free-floating menthol crystals floating on a fluid surface to represent nonliving systems. Using experiments and mathematical models, we will try to …

Mount Laurel’S Fourth-Round Affordable Housing Obligations: Do The Recent Obligations Pass The Constitutional Muster Of The Takings Clause?, Agnieszka Karkoszka

Seton Hall Journal of Legislation and Public Policy

No abstract provided.

A Machine Learning-Based Apogee Prediction Methodology For Experimental Student Rockets, Price Hamilton Drawdy

Senior Honors Theses

The ability to predict the maximum altitude of a rocket (apogee) in real-time is incredibly useful for collegiate-level competition rockets. This project creates a machine learning-based real-time apogee prediction methodology. Three model types were tested: linear regression, random forest, and a 3-layer multi-layer perceptron (MLP) neural network. These models were trained on a large dataset of simulated flights. All models performed well on simulated test flights, with the linear regression model showing most promise for use on edge compute. More development and real-world testing are necessary to determine how applicable this method is for real-time operation. Nevertheless, this methodology provides …

The Trouble Of Energy: Theoretical Foundations Of Loop Quantum Gravity, Hallie Gift

Senior Honors Theses

While general relativity and quantum mechanics have proved to be successful theories with elegant explanations of special cases, an underlying theory is needed to harmonize their copious discrepancies. Popular theories to reconcile the two are broadly termed “quantum gravity,” which refers to theories that introduce a discretized component of gravity either by directly suggesting gravitational quanta or attempting to discretize spacetime geometry. Loop quantum gravity falls under the latter category; it respects the background independence intrinsic to general relativity while attempting to provide an explanation of underlying quantum behavior via Planck-scale-level geometrical discretization. This thesis aims to provide a primer …

Analyzing The Writing Style Of Generative Ai When Prompted With Writing Samples, Samuel Mcdowell

Senior Honors Theses

Authorship attribution is an important topic in today’s world of Large Language Models (LLMs). It is the technology that helps to verify the author of a written work. This study explores whether LLMs can successfully mimic an individual’s writing style if they are given a text sample. A dataset of human-written texts was collected and used to prompt several LLMs to generate new texts that attempt to replicate the original author’s stylistic characteristics. The generated texts were then tested with modern authorship attribution models to determine whether they would be identified as being written by the original author. The results …

Volume 17, Christian O’Neill, Kyara Greene, Savva Sidorov, Laura Bisaillon, Luke Clemmer, Hannah Gordon, Kitt Benson, Taylor Blount, Rachel Danzitz, Nicholas Duellman, Chase Gionis, Hima Fernando, Seth Franzyshen, Onyx Gonzalez, Bryan Lin, Samantha Start, Ysabel Wells, Maggie Duncan

Incite: The Journal of Undergraduate Scholarship

Introduction Dr. Amorette Barber, Director, Office of Student Research

From the Editor Dr. Hannah Dudley-Shotwell

Cover Artist’s Statement Maggie Duncan

On Mentoring Dr. Yulia Uryadova

Ukrainian Resistance in the Face of Russification: Nestor Makhno and Anarchism

by Christian O’Neill

Life Vest by Kyara Greene

Isolation and 16S rRNA Identification of Bacteria from Fire Department Connection Pipe by Savva Sidorov

The Effectiveness of Planned Exercise in Reducing ADHD Symptoms in Children by Laura Bisaillon & Luke Clemmer

Linguistic Analysis on Confidence and Communication Strategies with Disparities Between Sign Fluency and Hearing Impairment by Hannah Gordon

Freedmen in Indian Territory by Kitt …

Analyzing The Evolution Of Science: Topological Cycles And Community Detection In Knowledge Networks, Frances C. Mcconnell

Mathematics, Statistics, and Computer Science Honors Projects

How scientific knowledge grows and organizes itself is a central question in the study of science. This thesis uses tools from topology and network science to detect and characterize knowledge gaps—places in a field’s literature where related concepts do not co-occur. We develop a metric to quantify the degree of interdisciplinarity of each gap, using the community structure of the underlying network as a proxy for subfields. Across a wide range of fields, gaps reliably span multiple subfields and evolve in recognizable temporal patterns, highlighting new insights into how scientific fields are structured and their stage of development.

Understanding Delays In Emergency Department Care: A National Analysis Of Wait Times, Gregory Forsberg

Mathematics, Statistics, and Computer Science Honors Projects

Emergency department (ED) wait times remain a persistent bottleneck in the United States healthcare system, impacting patient outcomes, hospital efficiency, and equitable access to care. This study analyzes nationally representative data from the National Hospital Ambulatory Medical Care Survey (NHAMCS), a complex, multi-stage probability sample. Using survey-weighted analyses and predictive modeling, we examine the effects of patient characteristics, triage acuity, and visit timing. Results indicate that operational and system-level factors, including hospital capacity, geographic region, and temporal variation, are among the most influential predictors of ED wait times

Avoiding White Elephants: An Empirical Assessment On The Failure Of Airport Capacity To Drive Tourism Receipts, Somanithel Kheang

Theses

This study investigates the relationship between airport infrastructure capacity and international tourism receipts across 12 major Asian and European economies from 2000 to 2019. Using a Panel Fixed Effects framework, the analysis tests the "Supply-Led Growth" hypothesis to determine if physical infrastructure expansion independently drives tourism revenue. To address potential endogeneity and reverse causality, the study employs specification where all independent variables are lagged by one year.The empirical results yield a robust null finding: airport capacity does not exhibit a statistically significant impact on tourism receipts. Conversely, government consumption is identified as a potent driver of tourism performance, with a …

Deep Reinforcement Learning-Based Equitable Post Disaster Resource Allocation Incorporating Social Vulnerability, Trevor John Mogaka

Theses

Prior Deep Reinforcement Learning (DRL) approaches frame post‑disaster recovery as a sequential decision-making problem over infrastructure elements, using graph-based system models and resilience-oriented rewards. This thesis builds on an existing DRL framework by introducing a modified CDC Social Vulnerability Index (mCDC-SVI), developed using Max-min scaling, to quantify social vulnerability.A synthetic interdependent infrastructure testbed is divided into four socio‑economic regions using federal poverty and SNAP eligibility guidelines. The DRL simulation and Deep Q‑Network architecture are retrained under a multi-objective reward function that is efficiency-equity weighted (α:β). Agents are evaluated across unconstrained and constrained budget scenarios to examine how varying α and …

Clear-Air Turbulence Climatology And Trends, Liam Rodgers, Mark Sinclair

Publications

Clear‑air turbulence (CAT) is a major aviation hazard that occurs near airline cruising altitudes in both cloud and cloud‑free environments. Its lack of a distinct visual signature makes detection and avoidance difficult. CAT is associated with wind shear near jet streams, gravity waves, and Kelvin–Helmholtz instability and may be further enhanced by climate change. This study examines the climatology, spatial distribution, seasonal variability, and trends of CAT over the contiguous United States.

Pilot Reports (PIREPs) from 2001–2025 between 100 and 400 hPa are analyzed alongside jet stream, shear, and stability diagnostics derived from NCEP–NCAR Reanalysis data. Proxies such as inverse …

Prediction Of The Nga-West2 Average Vertical Peak Ground Acceleration Using Genetic Expression Programming, Dipankar Karki

Theses

Ground Motion Predicting Equation (GMPEs) has been proposed with the use of Genetic Expression Programming (GEP) tool using 10,080 ground motion records obtained from the NGA West2 database. For this research, moment magnitude, dip angle, rake angle, Joyner Boore Distance, depth to top of fault distance, closest distance to ruptured fault area and the shear wave velocity in top 30 m of the site have been selected as the predictor set. It was found that the results from the GEP model compared well with the existing GMPEs and validates the use of GEPs for creating GMPEs along with other regressions …

Weavecc: Symbolically-Guided Joint Exploration Of Inputs And Schedules For Concurrency Bug Detection, William Philip Dinauer

Dartmouth College Master’s Theses

Concurrent programs introduce a class of bugs that depend jointly on both program inputs and thread schedules. Exposing these bugs requires simultaneously reasoning about which code paths are reachable and which thread interleavings are possible. At the same time, many existing tools handle the problem insufficiently. Race detectors observe only the interleavings that the OS happens to produce. Fuzzers explore inputs without controlling schedules. Tools that address both dimensions together exist, but are built on interpretation-based symbolic executors that incur considerable overhead.

We present WeaveCC, a practical concurrency testing tool for C/C++ programs that jointly explores inputs and thread schedules. …

Pattern Dynamics And Stochasticity Of Brain Rhythms And Spike Trains In A Tauopathy Mouse Model Of Alzheimer’S Disease, Clarissa M. Hoffman

Dissertations and Theses (Open Access)

Systems neuroscience posits that every aspect of perceived physical reality, every aspect of animal and human behavior, and every cognitive phenomenon emerges from patterns of neuronal activity. While most researchers embrace this idea, there are major difficulties in describing and analyzing these complex neuronal dynamics—spike flows produced by cells ensembles, synchronized extracellular field oscillations, and other patterns—which limits our understanding of how the activity of individual neurons and the whole-animal cognition and behavior might be connected. In particular, we lack the approaches and even the semantics for connecting the individual cell outputs and the integrated results of their activity. Current …

Characterization Of Staphylococcus Aureus Clinical Isolates From Deep Musculoskeletal Infections For An In Vitro Biofilm Model For Phage Therapy, Raquel C. Luna

Dissertations and Theses (Open Access)

The rise of multidrug-resistant (MDR) infections is a growing concern in the treatment of deep musculoskeletal infections (MSKIs) and orthopaedic device-related infections (ODRIs). Staphylococcus aureus is responsible for about one third of these infections due to its strong biofilm forming capacity, presence on human skin, and the large number of antibiotic-resistant variants. Bacteriophage (phage) therapy is now being considered as a treatment for MDR MSKIs/ODRIs. We are investigating the use of phage encapsulated into or adsorbed onto biodegradable microspheres for local delivery to the site of these MDR infections. We determined that the anti-staphylococcal phage K effectively lyses the S. …

Technical Development, Optimization, And Clinical Implementation Of Quantitative Magnetic Resonance Imaging Methods For Radiation Oncology, Lucas Mccullum

Dissertations and Theses (Open Access)

Magnetic resonance imaging (MRI) has gradually grown more important in the field of radiation oncology. This paradigm shift has grown from initial experiments with MRI of cancerous lesions in the early 1970s, to the integration of MRI images for radiation therapy treatment planning (i.e., MRI-Simulation) in the early 1980s, to the combined MRI linear accelerator (MR-Linac) becoming commercially available in 2018 which can simultaneously image a tumor and treat it with radiation. However, due to its novelty in radiation oncology, its full potential for the integration of quantitative MRI-based biomarkers has yet to be fully realized.

Therefore, this dissertation will …

Membrane Organization Of Rheb And Rhoa: Roles In Function And Allosteric Druggability, Chase M. Hutchins

Dissertations and Theses (Open Access)

Small GTPases of the Ras superfamily are membrane-bound molecular switches that regulate nearly all major cellular processes, and their dysregulation drives cancers, neurological disorders, and metabolic diseases. While anchored to cellular membranes, the catalytic domains of small GTPases undergo orientation dynamics, adopting distinct configurations that can occlude or expose effector-binding surfaces and modulate signaling output. These dynamics have been characterized in the Ras oncoproteins, but whether they extend across the superfamily and influence allosteric druggability has remained unknown.

In this dissertation, I address these questions through Rheb and RhoA, two small GTPases with complementary differences in subfamily lineage, membrane localization, …