Digital Commons Network^™

Examining The Feasibility And Influencing Factors Of Large Language Models In Source Code Security Analysis, Jie Lin

Graduate Thesis and Dissertation post-2024

Large Language Models (LLMs) have recently emerged as promising tools for analyzing source code; however, their capabilities for automated vulnerability detection and localization remain underexplored, particularly given the widespread reliance on closed-source models which introduce significant privacy, security, and transparency risks. This dissertation rigorously examines the feasibility and key influencing factors determining the efficacy of open-source LLMs in source code security analysis. Initially, we investigate the impact of tokenized input length on vulnerability detection accuracy and explicitness across Java vulnerability datasets using ten distinct LLM architectures, identifying robust performance in specific models while others demonstrate considerable accuracy deterioration. Subsequently, we …

Multiscale Modeling And Experimental Evaluation Of Droplet Evaporation In High-Pressure Pulsed Spray Cooling For Thermal Management Applications, Fernando Soria Proano

Graduate Thesis and Dissertation post-2024

Droplet evaporation plays a vital role in spray cooling applications, helping to maintain safe operating temperatures for high-powered devices such as lasers and supercomputers. There exists an ongoing debate regarding the appropriateness of diffusion-limited versus kinetically limited models for describing this complex process. This work seeks to bridge the macro-scale evaporation, governed primarily by capillary forces, with the nano-scale interactions among the vapor, liquid, and solid phases through a comprehensive description of disjoining pressure.

By integrating principles from lubrication theory, heat conduction, diffusion, and statistical methods, a detailed model for evaporative mass flux is developed. This model incorporates various interactions, …

Algorithms And Benchmarking For Parallel Identity-By-Descent Segment Detection, Kecong Tang

Graduate Thesis and Dissertation post-2024

As genomic biobank initiatives continue to grow, the availability of large-scale genotype datasets, encompassing hundreds of thousands to millions of individuals, has transformed genetic research and biomedical discovery. However, the sheer volume of this data presents major computational barriers. Efficient and scalable methods are urgently needed to process and extract meaningful signals from biobank-scale data using modern multi-core architectures. One central task in this domain is the detection of identity-by-descent (IBD) segments, which underpins a range of applications including genealogical inference, disease mapping, phasing, and population structure analysis.

This dissertation addresses these challenges by presenting a sequence of contributions that …

A Numerical Assessment Of Shock-Raindrop Interaction: An Investigation Of Cavitation Dynamics Driven By Internal Pressure Wave Focusing, Reed Forehand

Graduate Thesis and Dissertation post-2024

This dissertation investigates the formation and evolution of cavitation within liquid droplets subjected to shock wave interactions, with emphasis on internal pressure wave focusing as a fragmentation mechanism. A numerical framework is developed in which high-resolution Volume-of-Fluid (VoF) simulations resolve shock transmission into spherical, cylindrical, and cubic water droplets across a range of flow conditions. The resulting pressure histories at droplet centers are extracted and post-processed using the Rayleigh–Plesset equation to model the dynamics of spherical vapor bubbles.

The modeling framework is validated against established benchmarks, including canonical shock tube behavior, shock-droplet interaction studies, and experimental cavitation observations. A series …

The Elliptical Instability In Turbulent Flows: A Mechanistic Framework, David M. Smerina

Graduate Thesis and Dissertation post-2024

The conveyance of energy through the formation, interaction, and destruction of eddies over a wide range of spatial scales, from the largest scale where energy is injected to the smallest scales where the energy is dissipated through viscosity remains one of the most fundamental unsolved problems in fluid mechanics. This dissertation provides a comprehensive investigation spanning multiple flow regimes consisting of high-speed reacting flows and transitional boundary layer turbulence to establish the necessary conditions for turbulent flows to sustain cascades of energy to arbitrarily small scales while demonstrating how the nonlinear development of the elliptical instability leads to the emergence …

Detonation Morphology For Hypersonic Propulsion, Adam R. Kotler

Graduate Thesis and Dissertation post-2024

Standing detonations are uniquely stabilized in hypersonic flows. The portion of the detonation containing the stabilized shock front is remotely similar to non-reacting normal and oblique shocks and is uniquely differentiable from the latter by its coupling to a reaction front characterized by intense heat-release rates and fast chemical kinetics. The formation of an inert induction region characterized by slow-evolving chemical kinetics, subsequent development of a transition region within which these rates escalate, and terminal coalescence between shock wave and reaction front define the formation sequence of the standing detonation. Observations of this process are reported across multiple research groups. …

When Sediment Meets Strategy: Linking Shoaling Dynamics, Vessel Behavior, And Dredging Optimization, Matthew P. Davies

Graduate Thesis and Dissertation post-2024

Navigation channels are the economic arteries of U.S. maritime trade, yet their efficiency and safety are continually threatened by sedimentation and the escalating costs of dredging. Traditional tools used by the U.S. Army Corps of Engineers (USACE) to forecast shoaling and estimate dredging needs often fall short of capturing the complex interplay between channel condition, vessel behavior, and maintenance decisions. This dissertation develops and integrates three complementary innovations to address this challenge. First, a Parametric Linear Regression Methodology (PLRM) is introduced to improve shoaling predictions by blending long-term and short-term sedimentation rates with tunable weighting factors. This method achieves forecasting …

On Transient Dynamics And Persistent Organization Of Landscape Evolution, Aysan Hassanzadeh Bavojdan

Graduate Thesis and Dissertation post-2024

The evolution and organization of landscapes result from the competition between tectonic uplift, fluvial incision, and hillslope diffusion. Understanding how these processes interact under varying external conditions (i.e., extreme climatic events) is critical for predicting landscape dynamics and long-term geomorphic adjustment. Using a physically based landscape evolution model, we first investigate how variations in the fluvial incision coefficient (K) and soil diffusion coefficient (D) mimic different climatic conditions and control the transient and steady-state organization of landscapes. Results indicate that landscapes with the same non-dimensional index (defined as the ratio of the timescales of advective (fluvial) to diffusive (hillslope) processes) …

A Predictive Model Of Adsorption Of Cryogenic Liquids In Aerogel-Based Materials, Julie E. Foroosh

Graduate Thesis and Dissertation post-2024

Commodities that are gaseous at standard temperature and pressure are generally stored in thick-walled pressure vessels, or as cryogenic liquids in vacuum-jacketed tanks. An advantage of cryogenic storage is that more mass can be stored per unit volume. However, cryogenic tanks are often bulky and cannot be made into conformal geometries. Adsorption storage in porous and flexible materials, such as aerogel blankets, is a promising method for storing these commodities. Previous studies have shown that when the commodity is adsorbed from the liquid cryogen, instead of from a gaseous state, the mass adsorbed per unit volume of aerogel blanket is …

Modeling And Predicting The Thermal Behavior Of Si/Cnt Nano Composites Using Pinns, Olamide Osigbemeh

Graduate Thesis and Dissertation post-2024

Silicon/Carbon Nanotube (Si/CNT) composites have shown great performance as Thermal Interface Materials (TIMs) for cooling in advanced electronics. The heat transfer in these materials is highly influenced by the interfacial thermal resistance (ITR) at the interfaces between the CNT filler and the silicon matrix. The ITR poses a critical challenge that hinders the design and performance of the TIM. This research designs a Physics Informed Neural Network (PINN) that predicts the thermal behavior of Si/CNT through a forward and inverse solver since they are known networks in solving complex partial and ordinary differential equations. However, despite the advantages of PINNs, …

Towards Robust Deep Learning: Label Noise Modeling, Algorithms, And Applications, Diego Fernando Linares Gonzalez

Graduate Thesis and Dissertation post-2024

This thesis investigates robust and hybrid deep learning with emphasis on improving accuracy and generalization in challenging real-world settings, including learning under noisy supervision and applications to complex scientific data.

In the first part, we address instance-dependent label noise by using a tensor factorization–based methodology that models the noise transition process as an instance-dependent decomposition. We also utilize low-rank block-term structures to capture class-specific and instance-specific correlations. Moreover, we include a robust loss function and rank regularization to achieve stable optimization during model training. We validated this with experiments on benchmark datasets, like Fashion-MNIST, CIFAR-10, and CIFAR-10N, demonstrating higher accuracy …

A Framework For Machine Learning Surrogate Modeling In Physics-Based Reacting Flow Simulations, Jose O. Bobren-Diaz

Graduate Thesis and Dissertation post-2024

The design of next-generation combustion systems relies on computer simulations that capture the multidimensional and multiscale physics of turbulent reacting flows. These simulations are computationally intensive, requiring trade-offs to produce results within allowable time and cost constraints. These trade-offs often consist of general assumptions and modeling simplifications that approximate complex physical phenomena. Consequently, these models can limit both the accuracy and the regime of applicability to real systems since these approximations are often heuristic and inconsistent with the governing physics. Moreover, in today’s rapid design-to-prototype development phase, making timely and informed design decisions is important. This requires fast and reliable …

Advancing Heterogeneous Collaborative Perception For Autonomous Vehicles, Babak Ebrahimi Soorchaei

Graduate Thesis and Dissertation post-2024

Recent advances in computational and communication technologies have enabled deep neural networks (DNNs) and high‐speed vehicular wireless links, paving the way for cooperative perception and cognition in autonomous systems. By exchanging complementary observations, these methods overcome inherent sensor limitations, such as occlusions and restricted range, and thereby improve the detection of partially hidden targets. However, real‐world deployments face challenges from noisy measurements (e.g., GPS inaccuracies) and heterogeneous perception stacks: vehicles often rely heavily on visual detectors, yet there is no standardization ensuring uniform object‐detector architectures across a fleet.

In this dissertation, we first explore the design of a lightweight detector …

Communication-Aware Trajectory Prediction And Tracking For Connected And Autonomous Vehicles, Arash Raftari

Graduate Thesis and Dissertation post-2024

This dissertation presents a unified framework for communication-aware trajectory prediction and vehicle state tracking in connected and autonomous vehicle (CAV) systems. Unlike most deep learning–based forecasting models that assume full observability of surrounding agents, the proposed approach explicitly accounts for limited communication range, packet loss, and bandwidth constraints that characterize real vehicular networks.

A model-based, error-driven information-passing policy is introduced to extend situational awareness beyond one hop. Rather than relaying messages based on distance or freshness, each vehicle evaluates the utility of received content in reducing system-wide position tracking error (PTE). Using compact Gaussian Mixture Model (GMM) representations of motion …

Attention-Based 3d-Convolutional Neural Network Model For Mechanical Property Predictions Using, Daniel E. Traczyk

Graduate Thesis and Dissertation post-2024

Additive manufacturing (AM), while commonly used for rapid prototyping and creating components with complex geometries, has not been widely adopted for critical applications across the aerospace, automotive, defense, energy, and medical industries. This is, in part, due to the challenges of controlling flaws and uncertainty in the mechanical behavior of additively manufactured components. In recent years, there has been an increase in research aimed at predicting the final mechanical properties of additively manufactured components during the printing process. To address these issues, a 3D-CNN model was trained using low-cost in situ visible-light camera data, anomaly classifications, and the chosen process …

Better Models For Mosquito Control: Impacts On Nontarget Insects And Joint Species Distributions Of Mosquitoes, Jacob D. Hart

Graduate Thesis and Dissertation post-2024

Since its inception, mosquito control has been vital for limiting the spread of vector-borne pathogens. Modern control efforts balance the protection of public health and the prevention of environmental harm. We addressed this from two directions: the study of mosquito adulticide effects on nontarget insects, and fine scale distribution modeling of mosquitoes to better target control efforts. We first investigated the effects of ultra-low volume (ULV) pyrethroid applications on nontarget insects in Central Florida and found no statistically clear evidence of reduction in abundance or diversity. We reported a slight and statistically unclear reduction of night-flying Lepidoptera, and effective reduction …

Bridging Physiology, Data Science, And Genomics To Illuminate Photosynthetic Variation In Sunflower, Rebekah E. Davis

Graduate Thesis and Dissertation post-2024

Photosynthesis is a critical metabolic process in plants involving ‘light reactions’ that capture solar energy to produce ATP and NADPH, and ‘dark reactions’ where these molecules power the enzyme Rubisco to synthesize sugars in the Calvin-Benson cycle. These processes serve as the primary energy source for plant growth and reproduction, displaying significant variation across species and environments. In Helianthus, distinct differences in photosynthetic traits are observed between annual and perennial species. Notably, carboxylation rates, photon capture, and maximum photosynthetic rates show strong phylogenetic signal (Pagel’s λ = 0.99) and are significantly higher in annuals. Photosynthesis is modeled here as a …

Lowering The Barrier For Reading Primary Literature In Stem, Sebastian G. Alvarado, Maral Tajerian

Open Educational Resources

Building Bridges of Knowledge (BBK) is a cross‑CUNY initiative that integrates artificial intelligence (AI) and open educational resources to lower the barrier to primary literature and enhance research‑focused pedagogy. Recognizing that scientific papers are often dense and inaccessible to newcomers, we developed a suite of AI‑assisted instructional materials and activities to help students interpret complex data and methodology. A central component is the use of large language models (LLMs) and tools such as Scite.ai to provide citation‑context analytics and personalized research support. These tools allow students to filter literature by supportive, neutral, or contrasting citation statements and to troubleshoot laboratory …

Wavefront Sensing Using Nonlocal Metasurfaces, Arturo Martin Jimenez

Graduate Thesis and Dissertation post-2024

Many laser applications require wavefront control to compensate for aberrations caused by system misalignment, component fabrication errors, and propagation through inhomogeneous atmospheres. Aberrations caused by atmospheric turbulence remain particularly challenging to measure due to the temporally fluctuating wavefront distortions and large transverse phase variations in the beam. Conventional wavefront sensors, like the Schack-Hartmann (SHWS) are effective at low turbulence levels, but require relatively bulky optical elements. This thesis investigates the use of metasurface optical elements for use in wavefront sensors both in the low-turbulence limit and in deep turbulence conditions. Such metasurface-based wavefront sensors can achieve the functionality of complex …

Metasurface-Refractive Hybrid Lens Design, Ko-Han Shih

Graduate Thesis and Dissertation post-2024

Refractive optics are widely used in imaging systems while optical aberrations can limit their imaging performance and the typical solution to correct is cascading additional refractive optics with varying materials and shapes. Still, this scheme can result in bulky and costly lenses. Metasurfaces (MSs), with their compactness and ability to locally manipulate wavefronts, offer additional degrees of freedom in aberration correction. Integrating MSs with refractive optics creates MS-refractive hybrid lenses, enabling advanced optical performance while maintaining a compact design. Various methods have been proposed for designing aberration-correcting MSs in hybrid lenses, which often rely on predefined target phase profiles or …

Shoot The Messenger: Why Section 230 Does Not Shield Suggestive Content Delivery, Alexander Heinkele

Cardozo Law Review de•novo

Internet companies have frequently relied on Section 230 of the Communications Decency Act of 1996 to avoid liability for third-party content hosted on their platforms. However, over time, companies began to take advantage of the broad cover of Section 230 in circumstances outside the statute’s original scope. This Note advocates for a more nuanced interpretation of the statute as it applies to suggestive algorithms and offers a proposal for amending Section 230 to better reflect the modern digital landscape.

Principles Of Biomedical Research, Faina Linkov, Matthew C. Kostek

Open Education Materials

HLT 430/530

CC BY-NC-SA

Reframing Recovery: Comparing Music Interventions And Traditional Rehabilitation Methods For Cognitive Function In Traumatic Brain Injury Patients, Laura Velez

Honors Undergraduate Theses

Traumatic brain injury (TBI) affects millions annually, often resulting in long-term cognitive deficits that hinder independence and quality of life. While traditional rehabilitation methods address many physical and functional limitations, cognitive impairments—such as deficits in attention, memory, and executive function—often persist. Emerging evidence highlights music-based interventions as a promising complement to standard therapies, yet comparative research remains limited. This integrative literature review examined the effects of music interventions versus traditional rehabilitation methods on cognitive function in adults with TBI. Using a systematic search of databases including MEDLINE, CINAHL Ultimate, APA PsycInfo, and Music Index, eight peer-reviewed studies were selected for …

Consequences Of Artificial Intelligence In Health Insurance: Lawsuits, Policy, And Ethics, Alyssa N. Roberts

Honors Undergraduate Theses

In recent years, the healthcare system has been burdened by a multitude of obstacles that hinder the ability to provide effective, affordable, and timely care. Among these, one of the most significant challenges is the role that health insurance plays in shaping the quality of care. Health insurance companies are designed to decrease financial strain on patients, but they have introduced inefficiencies through delayed coverage approvals, increased denials, and administrative costs. Artificial intelligence (AI) has started to play an integral role in resolving these issues for the health insurance industry. Through its quick automated claim processing, fraud screening, and reduced …

Development And Pilot Of A Microgreens Project To Integrate Food Security In Grade 8 Science Using Systems Thinking, Iris Mae L. Caidic, C. K. Gonzales

Biology Faculty Publications

Among other countries, the Philippines faces the issue of food security, which must be addressed through education. However, the lack of coherence in teaching science content hinders Filipino junior high school students’ holistic understanding that science lessons are interconnected to food security. This study explores how systems thinking can be utilized to integrate food security into science topics. The pilot of the developed module on a microgreens project was implemented using systems thinking tools to tackle lessons on biodiversity, ecosystems, and the human digestive system within the context of food security. Data from end-of-scaffold check-ins, individual scaffold assessments, group performance …

The Representation Of Racially And Ethnically Diverse Students Identified With Giftedness In Third Grade In Florida, Ioanna Katsavria

Graduate Thesis and Dissertation post-2024

This study examines the proportional representation of Black, Hispanic, White, and Asian third-grade gifted students across urban and rural school districts in the U.S. state of Florida. It incorporates secondary quantitative datasets for the Fall 2023 and Spring 2024 school terms from the Florida Department of Education (FLDOE, n.d.), collected in the Fall 2024, along with qualitative data gathered in Spring 2025 from the websites of sixty-seven county-based school districts. Significant disparities among all groups of racially and ethnically diverse students were exposed through the quantitative data analysis. Notably, Black, Hispanic, and Asian students, especially in rural areas, were shown …

Enhancing The Accuracy Of Image Classification For Degenerative Brain Diseases With Cnn Ensemble Models Using Mel-Spectrograms, Sang-Ha Sung, Michael Pokojovy, Do-Young Kang, Woo-Yong Bae, Yeon-Jae Hong, Sangjin Kim

Mathematics & Statistics Faculty Publications

Alzheimer’s disease (AD) and Parkinson’s disease (PD) are prevalent neurodegenerative disorders among the elderly, leading to cognitive decline and motor impairments. As the population ages, the prevalence of these neurodegenerative disorders is increasing, providing motivation for active research in this area. However, most studies are conducted using brain imaging, with relatively few studies utilizing voice data. Using voice data offers advantages in accessibility compared to brain imaging analysis. This study introduces a novel ensemble-based classification model that utilizes Mel spectrograms and Convolutional Neural Networks (CNNs) to distinguish between healthy individuals (NM), AD, and PD patients. A total of 700 voice …

A Time-Domain Boundary Integral Equation For Moving Acoustic Sources In Uniform Flow And Its Solution By An Advanced Time Propagation Approach, Fang Q. Hu, Douglas M. Nark

Mathematics & Statistics Faculty Publications

This paper presents a time-domain boundary integral equation (TDBIE) formulation for predicting acoustic scattering from moving sources in a uniform mean flow. This work is motivated by the increasing need for accurate aeroacoustic modeling of modern aircraft configurations, including VTOL and eVTOL systems with rotating components. A key challenge in time-domain scattering simulations with moving sources is the determination of retarded time for a given observer time, which involves solving an implicit equation at each time step. This can be computationally costly, particularly for numerical solution of the TDBIE where every surface element on the scattering body acts as an …

Covariate Selection For Rna-Seq Differential Expression Analysis With Hidden Factor Adjustment, Farzana Noorzahan, Hyeongseon Jeon, Yet Nguyen

Mathematics & Statistics Faculty Publications

In RNA-seq data analysis, a primary objective is the identification of differentially expressed genes, which are genes that exhibit varying expression levels across different conditions of interest. It is widely known that hidden factors, such as batch effects, can substantially influence the differential expression analysis. Furthermore, apart from the primary factor of interest and unforeseen artifacts, an RNA-seq experiment typically contains multiple measured covariates, some of which may significantly affect gene expression levels, while others may not. Existing methods either address the covariate selection or the unknown artifacts separately. In this study, we investigate two integrated strategies, FSR_sva and SVAall_FSR, …

Developing, Designing, And Evaluating Knightly Codes: A System Of Resources Supporting Motivation Using Automated Feedback, Kyle S. Dencker

Graduate Thesis and Dissertation post-2024

Providing meaningful practice and feedback is a critical component of supporting students' learning, but it is also labor-intensive and time-consuming for instructors. This study evaluated the effectiveness of Knightly Codes, a supplemental system designed to provide automated, tiered feedback to students in an introductory, college-level Computer Science course designed to help them develop strategies to tackle real-world coding problems and maintain or improve their interest and motivation. Key design features include increasingly specific guidance as students submit multiple incorrect solutions, gamification elements including progress checks, badges, and personal and global statistics, and reflection surveys to promote self-regulated learning. A total …