Digital Commons Network^™

Advancing Electrical Stimulation: Full-Head Mri Segmentation For Abnormal Brain Anatomy With Tdcs, Andrew Birnbaum

Dissertations and Theses

Evaluating the effectiveness of transcranial direct current stimulation (tDCS) is essential for guiding its integration into therapeutic and performance-enhancement applications. In our laboratory, we investigate the efficacy of tDCS across multiple experimental models, including both animal and human studies. I have contributed significantly to the execution and analysis of these experiments, which include studies in rats and healthy human participants aimed at evaluating whether electrical stimulation of the motor cortex can enhance motor learning. These studies assess improvements in fine motor performance resulting from tDCS. In stroke patients, I contribute to our investigation of tDCS as a rehabilitative intervention, particularly …

Development Of An Quadcopter Unmanned Aerial Vehicle For Atmospheric Remote Sensing, Omar J. Addasi

Dissertations and Theses

This paper explores the development of a quadcopter unmanned aerial vehicle (UAV, a.k.a. drone) for atmospheric remote sensing of temperature, pressure, humidity, and PM2.5 particulate matter quantities. A 3-D printed drone body is designed and flight tuning is performed. An optimal length for an upward extending mast for the drone body is determined. Low cost, lightweight sensors are compared against higher precision sensors in both static and dynamic conditions. In addition, an initial investigation into the design of a pressure sensor based anemometer is performed.

From Tpu To Qpu: Bridging Fidelity Gaps Across Next-Generation Computing Systems, Mohammad Walid M. Charrwi

Dissertations and Theses

Modern computing systems continue to advance rapidly, enabling unprecedented computational capabilities across diverse domains. Among these powerful architectures are AI accelerators such as Tensor Processing Units (TPUs) and quantum computing systems utilizing Quantum Processing Units (QPUs) stand out as trans-formative technologies. Despite their potential, these systems share significant challenges related to hardware infidelity, manifested as error-induced degradation in computational accuracy and reliability. This thesis explicitly addresses cost-effective methods to screen-out defective AI accelerators across next-generation computing architectures and boost quantum circuit output fidelity through hierarchical circuit optimization using advanced machine learning methodologies.

This thesis first proposes a cost-effective testing methodology …

A Novel Dual-Imaging Approach For Visualizing Multiphase And Droplet Interactions Including Shock-Driven Flows, Tad Jerzy Misztal

Dissertations and Theses

Shock-droplet/particle interactions have garnered substantial interest in recent years, fueled by advancements in aerospace and defense technologies, including supersonic and hypersonic systems in highly humid conditions, as well as the ongoing space exploration efforts from global space centers as well as private space companies. Understanding the interactions between shockwaves and droplets that impact on solid surfaces is critical due to the potential to cause significant damage to materials and structures in high-speed flow environments. Studies like these have applications in areas ranging from aerospace engineering to blast mitigation systems, where these interactions play a pivotal role in designing the relevant …

Classification Of Variable Stars Using Convolutional Neural Network, Abhina Premachandran Bindu

Dissertations and Theses

This research focuses on developing Convolutional Neural Networks (CNNs), for the process of classifying and identifying variable stars through the analysis of unprocessed light curves from Transiting Exoplanet Survey Satellite (TESS). As astronomical data is becoming increasingly complex, and as advanced missions deploy sophisticated instruments for data collection, both the quality and quantity of the data are improving at a rapid pace. This has created an urgent need to automate the analysis process using efficient and effective methods, such as those based on machine learning. While previous research has explored machine learning approaches, there has been limited focus on implementing …

Characterization Of The Intrinsic Neural Timescale Indices As A Reliable Fingerprint Of Brain Dynamics, Helya Honarpisheh

Dissertations and Theses

Intrinsic neural timescales (INTs) characterize the persistence of neural activity over time and offer insight into the brain’s temporal hierarchy. This thesis evaluates and compares six autocorrelation-based INT metrics—AR(1) Coefficient (Autoregressive Coefficient at 1), First Zero Lag, Lag at ACF = 1/e, Sum of Positive ACFs, ACF at Half Lag, Exponential Decay Constant (τ)— using resting-state fMRI data from 400 cortical regions across a large, lifespan sample (ages 6– 85) from the NKI-Rockland dataset. A discriminability analysis revealed that the AR(1) coefficient provided the highest test-retest reliability and individual specificity across repeated scans, establishing it as the most reliable INT …

Metasurfaces And Their Applications In Photonic Devices, Shuwei Guo

Dissertations and Theses

Metasurfaces are flat artificial optical elements composed of dielectric and/or metallic nanostructures. They can manipulate light in unprecedented ways by altering amplitude, phase, and polarization at subwavelength scales. The ability of these two-dimensional elements to perform multiple optical functions within compact optical systems—while offering high functionality, small form factors, and easy integration into optoelectronic devices—has sparked significant interest across various fields and industries. These applications span imaging, sensing, nonlinear and quantum optics, optical computing, automotive technology, augmented and virtual reality, and more.

In this dissertation, we explore and design new multifunctional metasurfaces with a focus on manipulating spectral responses. First, …

Using Deep Neural Nets In Writer Identification & Analysis, Aditya Majithia

Dissertations and Theses

This thesis focuses on developing automated deep learning methods for writer identification and writer attribute prediction from handwriting. It introduces two novel architectures: Convolutional Transformer Encoder (CTE) and Convolutional Swin Encoder (CSE). CTE is designed for determining authorship from handwritten text, be it modern or historical handwriting. CTE tracks subtle features and cues from handwriting strokes to distinguish authorship. It is the first of its kind capable to operate on historical handwritten fragments, setting it apart from existing methods that rely on entire document pages. CSE is designed to determine multiple attributes of an author such as authorship, gender, age …

Utilizing Deep Learning Audio Models For Blind And Low Vision Crosswalk Assistance, Wayne Lam

Dissertations and Theses

Navigating urban environments poses significant challenges for blind and low vision (BLV) individuals, particularly at street intersections where determining when it is safe to cross can be life-threatening. In New York City, where pedestrian fatalities are on the rise and only 2% of intersections are equipped with Accessible Pedestrian Signals (APS), alternative solutions are urgently needed. This thesis proposes an audio-based deep learning approach to support BLV individuals at crosswalks by detecting traffic movement direction and idling states using spatial sound. With 4-channel audio capturing capabilities of wearables, such as Meta Project Aria glasses, we explore state-of-the-art sound event localization …

Cognitive Map Generation For Vision And Language Navigation, Alexander Sandoval Mesa

Dissertations and Theses

Visual-Language Navigation (VLN) presents significant challenges for autonomous agents, such as robots and virtual assistants, particularly in complex, dynamic environments where the seamless integration of visual perception and natural language understanding is critical. Traditional VLN systems often struggle with effectively aligning language instructions and visual scene understanding, limiting their adaptability and navigation efficiency.

This thesis proposes a novel Cognitive Map-based framework that addresses these challenges by transforming natural language navigation instructions into structured graph representations. The Cognitive Map consists of nodes representing waypoints, landmarks, decision points, and edges encoding spatial relationships and navigational actions. These maps are generated using Large …

F-Layer Parameters Derived By Airglow Emissions At Pituffik Sb, Greenland, Jessica Norrell, Ivana Molina, Michael Negale, Jeffrey Holmes

Space Dynamics Laboratory Publications

Atomic oxygen airglow has long been used as tracer for the peak height and electron density of the F-layer (e.g. Sahai et al., 1981). Tinsley and Bittencourt (1975) described a method by which OI airglow is used to determine F-layer parameters. They found that the square root of the column emission rate of an optically thin layer created by radiative recombination emission was proportional to the peak electron density of the F-layer.

An all-sky imager (ASI) was recently installed at Pituffik, Greenland (76.51˚N, 68.74˚W), with four filters relevant to this study: 5725, 6300, 7715 and 7774 Å. We have used …

Structural Design & Analysis, Poti G. Doukas

Space Dynamics Laboratory Publications

Agenda

• Structural Requirements
• Deployers
• Engineering Drawings
• Verification Testing

Software Management, Space Dynamics Laboratory

Space Dynamics Laboratory Publications

Agenda

1. Software Organization Methods
2. Example Software Workflow
3. Small Sat Specific Tips

Requirements And C&Dh Design, Space Dynamics Laboratory

Space Dynamics Laboratory Publications

• What is it?
• Processor Selection
• CDH Hardware
• OS Selection
• Memory - Radiation
• Memory - Radiation Mitigation
• Memory - Partitions
• Memory - File System
• System Startup
• Startup Stages
• Software Start-up
• Serial Protocol
• I2C / One Wire Communication Tips
• Fault Handling: Watchdog Timer
• CDH Watchdog (Simple)
• CDH Watchdog (Use Case)
• Watchdog Timer Tips
• Synchronizing and Managing Time

Radiation Hardness Drivers For Mission Success – What We Have Learned, Space Dynamics Laboratory

Space Dynamics Laboratory Publications

RHA consists of all activities undertaken to ensure that the electronics and materials of a space system perform to their design specifications throughout exposure to the mission space environment

Smallsat Pcb Engineering, Space Dynamics Laboratory

Space Dynamics Laboratory Publications

• Concept
• Design
• Build
• Lessons Learned

Communications (Comm) Subsystem: Smallsat Antennas, Jim White

Space Dynamics Laboratory Publications

• A Practical Approach to SmallSat and Cubesat Antennas
• Antennas Designed Using Requirements
• Antenna Requirements
• Systems Engineering is Required
• What is a Radio Wave?
• What is an Antenna?
• Best Practices
• Testing
• Summary

Deep Learning Method For Image Processing In Cold Atom Experiments, Joshua Wilson, Robert Leonard, Jacob Morrey, Issac Peterson, Francisco Fonta, Spencer Olson

Space Dynamics Laboratory Publications

“Ideal” Absorption Imaging

Not always simple in practice

Atmospheric Waves Experiment (Awe) Mission Overview, Pedro Sevilla

Space Dynamics Laboratory Publications

The AWE instrument images atmospheric gravity waves from the ISS, quantifying energy and momentum fluxes into the ionosphere-thermosphere system to better understand how tropospheric weather influences space weather

How To Increase The Public’S Awareness Of Car-T Therapy? A Pilot Mixed Methods Study, Pei-Chun Cha

Public Health Theses

Background: Chimeric Antigen Receptor (CAR) T-cell therapy is a combination of antibody and T cell receptor (TCR), which allows T-cells to recognize and attack cancer cells. It has shown effectiveness in treating hematological malignancies, but has been less effective for solid tumors. Clinically, it is the first FDA-approved gene therapy as it uses lentivirus to modify the genome of T cells that are infused back into patients. Preclinically, laboratory research is being conducted to enhance this treatment through multiple genetic modifications. Concurrently, integrating community research to understand the public’s awareness of CAR-T is essential for making this therapy more widely …

Scale-Up Of Access To Medication For Opioid Use Disorder For People Who Inject Drugs On Parole In Ukraine: A Modeling Analysis, Brendan Caver

Public Health Theses

Background: Human Immunodeficiency Virus (HIV) and opioid-use disorder have long beendescribed as a syndemic; the two health issues go hand-in-hand to exacerbate each other (Perlman & Jordan, 2018). While the HIV pandemic has improved greatly in many regions such as the United States over the past several decades, it continues to become worse in Eastern Europe and Central Asia. HIV prevalence and opioid dependence are particularly high in carceral settings, but access to opioid agonist therapy (OAT) is extremely limited. This paper shows the results of a modeling analysis undertaken to see the impact of opioid agonist therapy (OAT) scale-up …

Evaluating Clinical Outcomes And Cost-Effectiveness Of Aspirin And Clopidogrel Versus Factor Xa Inhibitors And Clopidogrel For Treatment Of Patients With Peripheral Arterial Disease After Lower Extremity Revascularization, Uday Dhanda

Public Health Theses

Introduction and Objective: Antithrombotic treatment plays a critical role in managingperipheral arterial disease (PAD) and reducing the risk of major adverse limb events (MALE) and major adverse cardiovascular events (MACE) after revascularization. Dual antiplatelet therapy (DAPT) with aspirin and clopidogrel is the most commonly used regimen for medical management to prevent cardiovascular events following revascularization. Still, emerging studies suggest that dual antithrombotic pathway inhibition (DAPI), which combines a Factor Xa inhibitor with clopidogrel, may offer enhanced protection. This study evaluates the clinical outcomes and cost-effectiveness of DAPI compared to DAPT in patients undergoing lower extremity revascularization (LER).

Methods: A retrospective …

Swift: Strong-Signal Weighted Prs Integration For Fine-Tuning, Yikai Dong

Public Health Theses

Polygenic risk scores (PRS) hold immense promise for personalized medicine but suffer from reduced accuracy in non-European populations, hindering equitable application. We introduce SWIFT (Strong-signal Weighted PRS Integration for Fine-Tuning), a novel framework to enhance cross-population PRS prediction by optimally combining base scores from diverse ancestries using only GWAS summary statistics. SWIFT employs a GWAS subsampling technique based on data fission to generate internal training and tuning sets, circumventing the need for external validation cohorts. A key innovation is its focus on strong genetic signals (p < 0.1) for estimating robust combination weights via cross-validation, which mitigates overfitting and significantly reduces computational burden. We evaluated SWIFT across 22 complex traits using data from five populations (EUR, AFR, AMR, EAS, SAS) and independent validation cohorts (UK Biobank, All of Us). Applied to state-of-the art base methods (PRS-CSx, X-Wing, JointPRS), SWIFT achieved predictive accuracy comparable or superior to the recent LEOPARD integration method and baseline implementations. Crucially, SWIFT demonstrated a 3.5- to 9.5-fold reduction in computational runtime compared to LEOPARD and exhibited robust performance even when using a single, unified LD reference panel. SWIFT provides an efficient, robust, and practical approach to improve the accuracy and applicability of PRS across diverse populations, representing a significant step towards more equitable genomic medicine.

The Evolution And Effectiveness Of Sterile Insect Technique For Vector Control: A Historical And Modern Analysis, Katerina Karaiskos

Public Health Theses

Sterile Insect Technique (SIT) has emerged as an important tool in the control of vector-borne diseases, providing an alternative to traditional chemical insecticides. Initially based on the irradiation of insects to induce sterility, SIT has proven effective in eradication campaigns, including the cases of the screwworm and tsetse fly. SIT’s primary strengths lie in its species specificity, environmental safety, and the avoidance of pesticide resistance. However, “classic” irradiating SIT is limited by high operational costs, reduced mating competitiveness of sterilized males, and difficulties in addressing high-density vector populations. Recent advancements in genetic-based SIT methods, such as the Release of Insects …

Effectiveness Of Wolbachia Introgression On Dengue Incidence In A Complex Urban Environment, Karthika Satyanarayanan

Public Health Theses

Dengue is an arboviral disease of increasing global public health importance for which more than half the world’s population is at risk. The burden of dengue has increased greatly in the last decade; in 2024, over 14 million cases and 10,000 dengue-related deaths were reported, marking a historical milestone. The endosymbiotic bacterium Wolbachia (wMel strain) offers a promising alternative to traditional control methods through its reduction of Aedes aegypti vector competence and introgression into wild Ae. aegypti populations. This thesis evaluated the dynamics of Wolbachia introgression and its effect on reducing dengue incidence up to 31 months after the completion …

The Impact Of California’S Behavioral Health Services Act (Bhsa) On The Need And Utilization Of Behavioral Health Services In California Counties, Lucia Shen

Public Health Theses

This thesis evaluates the impact of California’s Behavioral Health Services Act (BHSA) on behavioral health need and service utilization across 44 California counties from 2001 to 2023. Using data from the California Health Interview Survey and Department of Health Care Services expenditure reports, this study employs time-series visualizations and Ordinary Least Squares panel regressions to assess whether BHSA investments are associated with reduced behavioral health need and increased service use. Statewide trends show that while behavioral health needs rose steadily, utilization increased moremodestly, resulting in a persistent access gap. Some counties, such as Nevada, Monterey, and Mendocino, exhibited temporary convergence …

Expanding The Trypanosoma Brucei Parasite Detection Limit Through Tb177 Gene Detection In Qpcr, Yi Yan

Public Health Theses

Trypanosomiasis, caused by protozoan parasites of the Trypanosoma genus, remains a significant health and veterinary burden in sub-Saharan Africa, with Human African Trypanosomiasis (HAT) and Animal African Trypanosomiasis (AAT) posing ongoing threats to underserved populations. Despite advances in therapeutics, the lack of sensitive and scalable diagnostic tools continues to limit early detection, effective disease management, and evaluation of emerging vaccine candidates. Current diagnostic methods such as light microscopy and serological tests frequently fail to detect low levels of parasitemia during the early stages of infection, thereby delaying intervention and increasing the risk of disease progression and transmission.

Our study mainly …

Temporal Analysis In Connectivity Structure Of Human Disease Co-Occurance Network, Jingxuan Xu

Public Health Theses

Recent research on human disease networks (HDNs) has revealed the importance of jointly analyzing multiple diseasesand their interconnections, yet many existing approaches focus on molecular-based data and do not fully capture non-molecular relationships. Building on the concept of the phenotypical HDN, we leverage Medicare inpatient claims data to construct a comprehensive disease co-occurrence network. In our framework, two diseases are deemed interconnected if their observed probability of co-occurrence significantly deviates from that expected under independence. We adopt the weighted gene co-expression network analysis (WGCNA) framework to build our network, using a ϕ-correlation measure and a hard threshold to ensure a …

Codecontext: Integrating External Context For Enhanced Source-Code Model Performance In Software Development, Mohammad Arjamand Ali

Senior Honors Theses and Projects

In the ever evolving field of software development, understanding and maintaining complex codebases is crucial. There exist machine learning models and algorithms that aid in this by specifically learning to ‘understand code’, allowing engineers to build applications that help develop and maintain these large codebases. Although existing source-code machine learning models often overlook an important factor: the code's context. Our research focuses on leveraging external contextual information to enhance source-code model performance. We’ve developed a data pipeline that utilizes CodeQL to extract contextual information from the CodeSearchNet benchmark dataset to extend and create an augmented version of the dataset. We …

Fairness-Aware And Culturally Adaptive Machine Learning For Predicting Adolescent Substance Use, Stephanie Nworgu

Senior Honors Theses and Projects

Early initiation of substance use during adolescence poses significant risks to long-term health, educational attainment, and social outcomes, making early identification a critical public health priority. Machine learning models have increasingly been used to predict substance use risk; however, many such models do not explicitly examine whether predictive performance differs across demographic groups. This senior project examines the fairness of logistic regression models used to predict first-time alcohol use among adolescents. Using nationally representative survey data from the Youth Risk Behavior Surveillance System (YRBSS), pooled across the 2017, 2019, 2021, and 2023 survey cycles, this study develops logistic regression–based predictive …