Digital Commons Network^™

Identifying Red Sponges On Arms Plates By Preprocessing Images Using Histogram Equalization, Barry Ng

Master's Projects

Sponges play a vital role in marine ecosystems, being the only organisms capable of converting dissolved organic matter (DOM) into particulate organic matter (POM). They provide nutrients for coral reefs to thrive in oligotrophic waters. Autonomous reef monitoring structures (ARMS) are used to measure the biodiversity of coral reefs by simulating the complex cavities inside reef structures. Organisms settle on them and scientists can retrieve them after a period of time for analysis. Images are taken of ARMS plates after they are retrieved. Human analysis is unsuitable for the analysis of ARMS plates due to the huge number of images. …

An Evidence-Based Approach To Predicting Pancreatic Ductal Adenocarcinoma, Surya Teja Nalluri

Master's Projects

Pancreatic ductal adenocarcinoma (PDAC) is a complex disease with hidden clinical indicators, so a reliable diagnosis of PDAC requires high precision and sophisticated analysis. Traditional probabilistic methods often rely on making unwarranted assumptions or undesirable approximations about probabilistic estimates, limiting their ability to provide the precision needed for correct diagnosis and treatment planning. In contrast, Dempster–Shafer Theory offers a formal framework for integrating uncertain and potentially conflicting evidence. This makes it well-suited for analyzing incomplete and ambiguous data typically associated with PDAC. By employing an evidential reasoning (ER) model based on Dempster-Shafer Theory, this approach systematically combines and evaluates imperfect …

Image-To-Text Transcription: Analyzing And Describing Visual Content, Zixiao Fan

Master's Projects

Image captioning, which provides a textual understanding of visual content, is the fundamental support for the advancement of Human-A.I. Interaction technology. In the hope of exploring the application of such technology, this project focuses on two specific goals. One is to directly explore the application of the image informationretrieving abilities, and the other is to dive into the specifics of the pipeline and components of image captioning models. As a result, this project presents a working app that exploits the text retrieval functionalities to enable image storage with functions like tagging and transcription. It also supports search functionality with a …

Galora: A Lightweight Graph-Aware Llm Framework For Node Classification On Text-Attributed Graphs, Mayur Choudhary

Master's Projects

With the exponential rise of language models (LMs) and their potential to understand semantic relationships, large LMs are being used across a wide range of applications. Text-attributed graphs (TAGs) are one notable example where LLMs can be combined with Graph Neural Networks (GNNs) to enhance node classification results. TAGs associate textual content with each node and are commonly seen in various domains such as social networks, citation graphs, recommendation systems, etc. Effectively modeling TAGs would enable deeper insights into different aspects of the graph and improve decision-making in relevant domains. We present GaLoRA, a parameter-efficient framework to integrate structural information …

Extraction Of A Knowledge Graph Of Biomedical Relationships, Brian Tran

Master's Projects

Rapid release in biomedical literature poses a challenge in linking information. This thesis aims to extract data from expanding datasets to identify and form meaningful relationships between biomedical entities. Large language models (LLMs) enable us to learn at a rapid pace. Creation of LLms from scratch are impractical. This thesis aims to collect a small dataset, containing biomedical papers, and use it to train large language models (LLMs) to extract entities from the text and learn the relationships between these entities. The experiment will be divided into two stages and utilize EU-ADR and ChemProt dataset. Starting with named entity recognition …

Performance Comparison Of Machine Learning Across Metal, Cuda, And Neuromorphic Frameworks, Ryan Saini

Master's Projects

Machine learning’s computational demands necessitate optimal performance and utilization. This research compares Apple Silicon M3 Pro with MPS, NVIDIA RTX 3070 GPU with CUDA, and neuromorphic computing for machine learning methods. We provide a cross-platform and cross-architecture performance analysis of machine learning methods to identify optimal configurations for training and inference scenarios. On traditional neural networks, Apple Silicon with MPS delivers superior energy efficiency at the cost of longer processing times for training and inference. NVIDIA with CUDA offers faster computation in training and inference at higher energy costs. Convolutional spiking neural networks perform competitively on event-based data, particularly on …

Malware Opcode Embedding And Quality Assessment Of Generative Sample Embeddings, Atishay Jain

Master's Projects

Malware is software used to damage and disrupt computer systems with the intent to cause damage to the victim. Malware detection and classification into malware families is a crucial problem for cybersecurity researchers. One of the major bottlenecks in improving these systems is the shortage of good quality labeled malware data, especially for malware families with scarce samples. Researchers have utilized generative models to generate malware data to address this issue. Malware embeddings encode patterns within a malware file, which can be used to detect and classify malware. Recently, encouraging results have been obtained in generating malware embeddings using generative …

Clustering Organ Cell Types, Venkata Satya Swathi Mattaparthi

Master's Projects

The Human Cell Atlas (HCA) created a reference map of all human cells. My project uses the Tabula Sapiens dataset, developed under HCA and based on single-cell RNA sequencing data, to explore cell type and tissue diversity. I performed experiments using the Elbow method and a formula based on dataset observations to determine the number of clusters, then applied k-means clustering on two representative subsets of the All Cells dataset. Clusters were selected for analysis using Shannon’s Diversity Index and Pielou’s Evenness. A novel algorithm based on the cell differentiation tree was used to validate the biological coherence of the …

Framework For Identity Privacy Through Gender Based Skeletonization, Harrison Hwang

Master's Projects

The protection of one’s privacy and sensitive information is becoming increasingly difficult in the modern age full of surveillance and data collection. Through the use of image based object detection machine learning models trained for human and facial recognition, people can be identified and tracked to a terrifyingly accurate degree. On the other hand, the information present in surveillance media can play a key role in security and law enforcement. This presents a problem of how to preserve key information without compromising the privacy of any individuals present in the video. In this research project, Computer Vision techniques and a …

Large Language Models For Bacterial Genomic Analysis, Manvendra Chavan

Master's Projects

Identification of bacterial gene sequences with agricultural applications has the potential to transform agricultural biotechnology. These genes can be used in environmentally friendly pest control strategies. One such use case is identifying genes with potential insecticidal properties. With an increasing number of genomic information and decreasing numbers of available annotated sequences, finding new insecticidal genes has become more challenging.The traditional methods relying on sequence alignment and annotated databases are not effective in detecting functionally relevant genes lacking close homology to known cases. This project investigates the data-driven classification of genes by sequence modeling. This research is focused on learning DNA …

Advanced Knowledge Extraction With Biomedical Data Using Llms, Akshat Krishna

Master's Projects

The rapid growth of biomedical research has led to an overwhelming volume of unstructured textual data in the scientific literature. This has necessitated the development of an automated approach for knowledge extraction and integration. In

this project, we present a comprehensive pipeline for constructing a unified biomed- ical knowledge graph by combining two well-known datasets: CHEMPROT [1],

which captures chemical–protein interactions, and EU-ADR [2], which annotates drug–gene–disease relationships. In order to identify important biomedical entities and interactions from CHEMPROT dataset, we perform Named Entity Recognition (NER) and relation Extraction (RE) using state-of-the-art biomedical models like BioBERT [3], BioGPT [4] and …

Masc Affect: Affective Masculinity In Video Games Research, Joshua Foust

Media Studies - Open-Access Faculty Scholarship

No abstract provided.

Toward A Combine-Style Approach To Predicting Future Esports Success, Nicholas David Bowman, Kevin Sweeney, Aaron R. Seitz, C. Shawn Green

Media Studies - Open-Access Faculty Scholarship

Historically, there has been a great deal of interest in using basicmeasures of individual difference factors to predict future success in traditional sports. For instance, the National Football League (NFL) holds a scouting combine each year prior to the NFL draft during which a host of attributes about players are measured, from basic height and weight, to sprint speed, to jumping capacity, to strength. Even among an already highly selected group of individuals (i.e., individuals skilled enough to even be considered for the NFL), such measures have been seen to have some degree of utility in predicting future performance. The …

Structure/Property/Processing Of A 3d Printed Self-Healing Polymer Blend Based On A Thermoplastic Healing Agent, Vincent Mei

Theses

Self-healing polymers can regain mechanical performance following damage, offering increased material durability and sustainability. This thesis establishes structure/property/processing relationships for a 3D printable extrinsically self-healing polymer based on a UV polymerizable thermosetting resin system blended with a low temperature thermoplastic healing agent. This work serves as the first example of a vat polymerization 3D printed soft, low Tg low-melt thermoplastic extrinsically self-healing polymer blend. This development enables high resolution fabrication of complex geometries with self-healing functionality. This strategy of imbuing self-healing properties onto vat polymerization resins will enable functionality in many application spaces including aerospace, biomedical, soft robotics, coatings, and …

Cost-Effective Strategies For Feral Swine Control: Exploring Trapping Equipment Cooperatives, Phil Kenkel, Riza Radmehr, Rodney Holcomb, Mckenzie Boyce

Human–Wildlife Interactions

Feral swine (Sus scrofa) in the United States have become a growing concern, with a population of ≥6 million and causing annual damages of ≥$1.5 billion USD. Because of their high reproductive capacity, effective control methods are crucial to reducing or slowing their population growth. While remotely monitored and triggered traps have proven to be an effective control strategy, the cost of sophisticated trapping equipment, which can cost ≥$7,000 USD, is often cost prohibitive for many landowners. Despite the pressing need to address the challenge of making effective feral swine control methods more affordable, there are few studies …

Bird Strikes During Climb And Approach: A Need For Innovative Management Strategies, Richard A. Dolbeer, Michael J. Begier

Human–Wildlife Interactions

Wildlife Hazard Management Plans at civil airports in the United States cannot directly address threats posed by birds moving through aircraft climb and approach paths unrelated to nearby bird-attractant habitats such as landfills. As an iconic example, an Airbus 320 departing LaGuardia Airport (New York, USA) on January 15, 2009, struck a flock of migratory Canada geese (Branta canadensis) 8 km out at 884 m above ground level (AGL) that resulted in a water landing now known as the “Miracle on the Hudson.” We documented 1,841 and 458 strikes from 2009 to 2023 involving large (≥1.8 kg) and …

Habitat Loss And Its Behavioral Effects On A Female Gray Hawk In The Lower Rio Grande Valley Of Texas, Usa, Michael T. Stewart, Ashley M. Tanner, Brian A. Millsap, William S. Clark

Human–Wildlife Interactions

Habitat loss is a major threat to wildlife worldwide. While habitat loss is commonly associated with the fragmentation, degradation, or destruction of large tracts of suitable space, habitat loss in urban environments can be much more incremental and nuanced. We tracked an adult female gray hawk (Buteo plagiatus) in the Lower Rio Grande Valley of Texas, USA, before and after 70% of the trees (7.6 ha) were cleared from the core of the nesting territory. While the hawk’s overall activity budget between within-patch and exploratory movements was unchanged, the hawk undertook substantially longer exploratory movements after the trees …

The Macroalgal Composition Differs Among Depths And Zones In Cuban Mesophotic Coral Reef Ecosystems, Beatriz Martínez-Daranas, M. Dennis Hanisak, Patricia M. González-Sánchez, Stephanie Farrington, John K. Reed

Faculty Scholarship

Previously the algal community of Cuban mesophotic coral reef ecosystems (MCEs) has not been characterized quantitatively. The objective of this study was to explore the distribution of macroalgae around Cuba and their depth profiles within the mesophotic zone (30−150 m). Data on the algal community were obtained in 2017 during 43 ROV (remotely operated vehicle) dives around Cuba’s shelf. Scientists specializing in algal taxonomy watched the live ROV video on board the ship and recorded algal presence every ~5—10 min in a database. Algae were identified to the lowest possible taxa, and algal presence/absence was analyzed in 20 m depth …

Butyrate Attenuates High-Fat Diet Induced Glomerulopathy Through Gpr43-Sirt3 Pathway, Ying Shi, Lin Xing, Ruoyi Zheng, Xin Luo, Fangzhi Yue, Xingwei Xiang, Anqi Qiu, Junyan Xie, Ryan D. Russell, Dongmei Zhang

Health & Human Performance Faculty Publications

The incidence of obesity related glomerulopathy (ORG) is rising worldwide with very limited treatment methods. Paralleled with the gut-kidney axis theory, beneficial effects of butyrate, one of short-chain fatty acids produced by gut microbiota, on metabolism and certain kidney diseases have gained growing attention. However, the effects of butyrate on ORG and its underlying mechanism are largely unexplored. In this study, a mice model of ORG was established with high-fat diet (HFD) feeding for 16 weeks, and sodium butyrate treatment was initiated at the 8th week. Podocytes injury, oxidative stress, and mitochondria function were evaluated in mice kidney and validated …

Exploring Automatic And Reflective Nudges For Smishing, Morgan E. Edwards

Psychology Theses & Dissertations

Phishing has been a common problem in the world of cybersecurity for over a decade, leading to significant monetary and privacy losses. Phishing attacks commonly occur via email, but there has been a rise in a new form of phishing known as SMiShing. This attack vector occurs similarly to email phishing, except the malicious message is received via SMS text. While ways to mitigate phishing via email have been widely studied, there is a lack of research on SMiShing and ways to mitigate this problem from a human-centered approach. Interventions such as digital nudges have been successfully applied to …

Investigation Of Dynamic Adsorption And Desorption Of Polymer Nanogel In Porous Media Through Microfluidics, Junchen Liu, Fuqiao Bai, Abdulaziz A. Almakimi, Mingzhen Wei, Xiaoming He, Ibnelwaleed A. Hussein, Baojun Bai

Mathematics and Statistics Faculty Research & Creative Works

Understanding the transport and retention of elastic nanogel and microgel particles in porous media has been a significant research subject for decades, essential to the application of enhanced oil recovery (EOR). However, a lack of dynamic adsorption and desorption studies, in which the kinetics in porous media are seldom investigated, hinders the design and application of polymer nanogel in underground porous media. In this work, we visualized and quantified the transport and dynamic adsorption of polymer nanogel in 3D glass micromodels that were manufactured by packing glass beads in capillaries. Calibrating the linearity of fluorescence intensity to concentration, we calculated …

Depletion Of Adipose Stroma-Like Cancer-Associated Fibroblasts Potentiates Pancreatic Cancer Immunotherapy, Joseph Rupert, Alexes Daquinag, Yongmei Yu, Yulin Dai, Zhongming Zhao, Mikhail G Kolonin

Faculty, Staff and Student Publications

This study shows that populations of CAFs have distinct effects on pancreatic cancer progression and shows that depletion of CAFs expressing adipose markers potentiates tumor/metastasis suppression effects of immune checkpoint blockade.

Examining Educational And Career Transition Points Among A Diverse, Virtual Mentoring Network, Erika L Thompson, Toufeeq Ahmed Syed, Zainab Latif, Katie Stinson, Damaris Javier, Gabrielle Saleh, Jamboor K Vishwanatha

Faculty, Staff and Student Publications

Given the differences in trajectory for under-represented minorities in biomedical careers, we sought to explore how a virtual mentoring program, the National Research Mentoring Network (NRMN), and its platform (MyNRMN), may facilitate transitions in the science, technology, engineering, mathematics, and medicine (STEMM) pipeline. The purpose of this study was to describe how the size of an MyNRMN member’s mentoring network and level of engagement correlate with academic and career transitions. We examined MyNRMN platform user data from March 2020 to May 2021 (n = 2993). Logistic regression estimated the odds of a career or academic transition related to NRMN …

Key Considerations For Combination Therapy In Alzheimer’S Clinical Trials: Perspectives From An Expert Advisory Board Convened By The Alzheimer’S Drug Discovery Foundation, Jeffrey Cummings, Michael Gold, Mark Mintun, Michael Irizarry, Andrew Von Eschenbach, Suzanne Hendrix, Donald Berry, Cristina Sampaio, Kaycee Sink, Jaren Landen, Miia Kivipelto, Michael Grundman, Steven E Arnold, Allan Green, Katherine Partrick, Laura Nisenbaum, Aaron Burstein, Howard Fillit

Faculty, Staff and Student Publications

There is growing consensus in the Alzheimer's community that combination therapy will be needed to maximize therapeutic benefits through the course of the disease. However, combination therapy raises complex questions and decisions for study sponsors, from preclinical research through clinical trial design to regulatory, statistical, and operational considerations. In January 2024, the Alzheimer's Drug Discovery Foundation convened an expert advisory board to discuss the key considerations in each of these areas. Experts agreed on the need to prioritize a combination therapy approach that encompasses a wide range of targets associated with aging and the underlying biology of Alzheimer's disease. Progress …

A Change In Climate: Inclusion And Menopause Experience At Work, Janie D. Stuart

Industrial-Organizational Psychology Dissertations

Inclusion within workplace environments is increasingly important as workforce diversity continues to expand. Despite anti-discrimination laws, women and other underrepresented groups often face exclusion in the workplace, which negatively affects their professional growth, health, and well-being. This research focused on how menopause, a natural biological transition, compounds workplace exclusion for women. Specifically, the study investigated the relationship between Menopause Experience and two workplace outcomes, Organizational Commitment and Turnover Intention, and assessed the moderating effects of Inclusion in organizations, teams, and non-gendered workplace cultures. There were strong positive relationships between all three dimensions of Inclusion with both Organizational Commitment and Turnover …

Ethics Vs.. Regulation: Converging Frameworks For Trustworthy Human-Centered Ai In Biomedical Research, Tayo Obafemi-Ajayi, Tiffani J. Bright, Emily F. Wong, Donald Wunsch, Joan Peckham, Jason H. Moore

Electrical and Computer Engineering Faculty Research & Creative Works

The accelerating impact of AI in biomedical research is driving significant advances in precision medicine. As these systems increasingly shape health outcomes, the imperative to develop trustworthy, reliable, and ethically grounded AI becomes more pressing, particularly in addressing concerns related to data integrity, patient safety, and equitable outcomes. While the potential of AI to transform biomedical research is clear, its responsible integration depends on more than technological capability. Ensuring that these systems are aligned with societal values requires a dual commitment: the operationalization of ethical principles throughout the AI life cycle and the establishment of robust regulatory mechanisms. Ethics provides …

Honey-Reram Enabled Sustainable Edge Ai System For Iot Applications, Jinhui Wang, Feng Zhao, Mohammad Rafeeq Khan, Md Mehedi Hasan Tanim, Zoe Templin, Harshvardhan Uppaluru

Electrical and Computer Engineering Faculty Research & Creative Works

This paper is toward a promising solution to address the environmental sustainability challenge in computing by building brain-inspired and green non-Von Neumann systems with Resistive Random-Access Memory (ReRAM) made from natural organic materials, honey, for energy-efficient operation, renewable material resources, sustainable device manufacturing, and environmentally-friendly disposal. In this paper, honey-ReRAM and its arrays are firstly manufactured and tested. The resistance modulation mechanism of honey-ReRAM is analyzed and investigated. Then a Computing-in-Memory (CIM) architecture based on honey-ReRAM for edge AI and IoT applications is proposed and evaluated. The experimental results indicate that the proposed edge AI systems with the VGG8 and …

Spin Mechanisms In Gd-Doped Gan Implanted With Oxygen Carbon At Room Temperature, Vishal Saravade, Amirhossein Ghods, Chuanle Zhou, Ian Ferguson

Electrical and Computer Engineering Faculty Research & Creative Works

Gadolinium-doped gallium nitride implanted with oxygen and carbon show carrier-mediated spin mechanisms at room temperature. As-grown Gd-doped GaN grown by metal-organic chemical vapor deposition using a tris(cyclopentadienyl) gadolinium precursor shows Ordinary Hall Effect and no ferromagnetism at room temperature. Upon O or C implantation in Gd-doped GaN, Anomalous Hall Effect that is indicative of carrier-mediated spin and ferromagnetism is observed. A good crystal quality is maintained even after implantation. O and C favor interstitial sites and occupy deep-level acceptor-type states in Gd-doped GaN. Room-temperature spin and ferromagnetism that is induced by gadolinium in Gd-doped GaN is activated by O and …

Sift Feature-Based Relative Altitude Estimation Enhanced With Siamese Network, Shirin Nasr-Esfahani, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

In GPS-denied environments or when GPS signals are unreliable or unavailable, alternative methods of accurate localization with coordinate generation become critical. To address localization, the scale-invariant feature transform (SIFT) algorithm, along with its numerous adaptations, is extensively utilized in computer vision and remote sensing for matching image features to identify objects and perform localization. This article presents a novel approach for estimating the relative altitude of unmanned aerial vehicles (UAVs) using SIFT features' scale (size), omitting the need for additional data like camera intrinsic parameters, as well as extensive image datasets are also required for training. Furthermore, the approach enhances …

An Extensive Analysis Of Match-Tracking Methods For Artmap, Niklas M. Melton, Leonardo Enzo Brito Da Silva, Donald C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

This paper identifies and studies five match-tracking (MT) methods in the adaptive resonance theory (ART) literature and conducts a detailed comparative analysis of these in ARTMAP applications. We focus on model performance for each MT method with respect to time and space efficiency as well as classification accuracy. Experimental results indicate that one MT variant, used in ARTMAP applications for the first time in this work, provides significant improvements in computational efficiency: depending on the ARTMAP variant, it was able to achieve up to one order of magnitude reduction in both time and space requirements, albeit with a compromise in …