Physical Sciences and Mathematics Commons^™

Establishing A Conventional Linac-Based Electron Flash Beam, Justin Defrancisco

Theses and Dissertations

Problems: A rediscovered radiotherapy technique, FLASH, involves increasing the dose rate to >40 Gy/s. For a given dose, FLASH is shown to decrease the normal tissue complication probability without affecting the tumor control probability. More preclinical studies are necessary to confirm and enable these desirable properties on humans. Current preclinical investigations have diverse constraints, requiring FLASH apparatuses with flexible parameters and accurate control. However, there are limited FLASH platforms available, the existing ones are costly. Our institution is not in possession of any such infrastructure. Even if available, dosimetry and beam control in FLASH are extremely lacking. There is …

Facile Synthesis Of Gold, Copper, And Silicon Nanostructures By Laser Ablation In Liquid, Nicholas Simpson

Theses and Dissertations

Metal nanoparticle (NP) synthesis is a continually evolving area of research, owing to the widespread application of NPs in fields such as catalysis, drug delivery, and biosensing. Compared to bulk metal material, NPs contain a larger proportion of surface atoms and exhibit unique optical properties, thus making them ideal for applications where maximizing surface interactions is key. Moreover, their compositions can be altered in several different ways, as evidenced by metal alloy, core/shell, supported, and defective NPs. While conventional synthesis methods are capable of producing NPs in large quantities, they often require conditions of high temperature and pressure, strong reducing …

Dosimetric Validation Of An Electron Monte Carlo (Emc) Algorithm For Small Cutouts And Extended Ssd’S: Assessing Emc In Small Field Dosimetry, Anil Basavaaraju

Theses and Dissertations

This thesis validates the accuracy and limitations of Electron Monte Carlo (eMC) simulations in Eclipse treatment planning systems for small-field electron dosimetry and extended source-to-surface distances (SSDs). Systematic measurements were performed using a microDiamond detector across various electron energies and field sizes. The study examines the discrepancies between measured and calculated (eMC) dose distributions, validating challenges due to lateral scatter disequilibrium and extended air gaps. Our findings provide systematic data for eMC dose calculations and demonstrate that eMC performs within clinical tolerances, with the ultimate goal of enhancing the precision and safety of radiation treatments.

Development, Synthesis And Characterization Of Advanced Low- Cobalt And High-Nickel Cathode Materials For Lithium-Ion Batteries, Arjun Patel

Theses and Dissertations

This dissertation focuses on the improvement of structural and electrochemical stability of active materials for lithium-ion batteries. It explores the modification strategies to mitigate the issues related to Ni-rich cathode materials, along with the continuous production of cathode precursors using a slug flow reactor to improve the quality of the product.

The First part of the dissertation examines the effect of substitution of cobalt with aluminum and iron on the electrochemical stability of Ni-rich lithium nickel cobalt manganese oxide (NCM) materials. This study investigates how Al and Fe doping work and how they impact the performance and stability of these …

Investigating The Temperature Effects On Aedes Aegypti And Dengue Virus In Central Argentina: Perspectives From Mathematical Modeling, Morgan H. Jackson

Theses and Dissertations

Dengue virus (DENV) causes over 390 million infections and around 40,000 deaths worldwide each year. DENV is primarily transmitted by the mosquito Aedes aegypti, and both the life cycle of these mosquitoes and DENV transmission are significantly impacted by temperature. In the temperate region of Central Argentina, where dengue outbreaks first began in 2009, outbreaks only occur following new introductions of DENV from other regions. Due to the relationships between temperature and DENV and temperature and Ae. aegypti, the risk of an outbreak changes throughout the year. Here, we develop and analyze mathematical models for both mosquito population dynamics and …

Substrate Preference And Foraging Behavior Of Two Declining Warblers In Appalachian Shrublands, Samantha Fishman

Theses and Dissertations

Abstract Shrubland and early successional habitats (ESH) support a diversity of wildlife, including many declining songbirds. However, nonnative shrubs increasingly dominate these habitats, potentially altering habitat quality by influencing prey availability. Habitat quality can be assessed by observing foraging behaviors of songbirds and identifying preferred and avoided substrates as well as testing the predictions of optimal foraging theory; specifically that birds will maximize their energy gain relative to the costs. We examined foraging behaviors of Golden-winged Warblers (Vermivora chrysoptera; GWWA) and Chestnut-sided Warblers (Setophaga pensylvanica; CSWA) in ESH on private lands in Virginia with varying amounts …

Optimal Data Splitting Methods, Sujay Mudalgi

Theses and Dissertations

In predictive modeling, effective data splitting is crucial for creating statistically representative training and validation sets. The state-of-the-art data splitting methods are based on minimizing the energy distance between the split subsets. However, there are a number of limitations in the existing methods, which this dissertation aims to address. First, the existing methods were computationally inefficient. Thus, Chapter 2 proposes a method to scale up these approaches for big data. Here, we introduce scalable Twinning (s-Twinning), which significantly improves the execution speed of data splitting without sacrificing accuracy. Second, the existing methods did not consider the predictive relationship in the …

Machine Learning Models Leveraging Patient-Similarity And Clinical Temporality For Disease Prognoses, Ahmad F. Al Musawi

Theses and Dissertations

Electronic Health Records (EHRs) constitute a comprehensive and high-dimensional repository of clinical data, encompassing a wide array of patient-level information such as diagnoses, procedures, medications, laboratory results, and unstructured clinical narratives. These data hold immense potential for advancing predictive modeling in healthcare, including tasks such as disease progression modeling, hospital readmission prediction, and length of stay (LoS) estimation. However, the intrinsic complexity of EHR data—manifested in its heterogeneity, sparsity, and temporal dynamics—poses significant analytical challenges that limit the generalizability and interpretability of conventional machine learning models. Recent methodological advancements in deep learning and graph-based learning, particularly Graph Neural Networks (GNNs), …

Periodic Trends In The Electronic And Magnetic Structure Of Superatomic 3d Transition Metal Chalcogenide Clusters, Gabriel Bohannon

Theses and Dissertations

We have systematically investigated the electronic structure of octahedral transition metal chalcogenide clusters, TM₆S₈(CO)₆, in which the transition metal atoms are from the 3d series in order to identify if periodic properties emerge. We were motivated by the identification of closed electronic shells with electron counts of 96, 100 and 114 in similar clusters from the 4d and 5d transition metal series. Further motivation was the finding of a dual-shell closing in the Fe₆S₈(CN)₆^5- cluster. This cluster is stabilized with a large spin magnetic moment due to the …

An Adaptive Method For Covariate Balancing In Block Randomized Clinical Trials, Ren Rasnick

Theses and Dissertations

Clinical trials are randomized in part to limit allocation bias, but also to ensure comparability between treatment arms for a baseline variable of concern. Comparable with regard to a baseline variable of concern is necessary for the validity of statistical methods. However, comparability is not guaranteed for trials of any size and is even more likely in trials with < 200 total participants. We propose a new method for adapting the allocation of participants in a sequentially allocated two-armed study with a small sample size to better ensure comparability.

The proposed method calculates the expected final imbalance (lack of comparability) based on the current participant values. Unlike several other methods, our method ensures the final desired sample size for each treatment arm, utilizes the expected final imbalance, increases comparability between …

Swarming Segregation: Leveraging Swarm Intelligence And Regionalization As Instruments For School District Desegregation, Jeffrey Wooten

Theses and Dissertations

Even after Brown led to the South briefly having the most diverse schools in the nation, schools throughout the Northeast have remained the most segregated in the nation for decades. While federal jurisprudence has made compelling desegregation pursuant to the Equal Protection Clause more challenging, New Jersey has a particularly favorable landscape to address severe segregation. With a highly diverse, densely populated public enrollment, favorable state constitutional precedent, and a history of successfully compelling desegregation, New Jersey is fertile ground exploring regional desegregation. Scholars, judges, and even plaintiffs in ongoing litigation (Latino Action Network v. N.J.) have called for New …

Unraveling The Reactivity Of Nitrate Ester Explosives: Insights From Femtosecond Time-Resolved Mass Spectrometry And Computational Chemistry, Erica Britt

Theses and Dissertations

This dissertation uncovers the ultrafast molecular mechanisms underlying the decomposition of nitrate ester explosives—nitroglycerin (NG), ethylene glycol dinitrate (EGDN), and amyl nitrate—through the integration of femtosecond time-resolved mass spectrometry and advanced computational chemistry. Although these compounds play fundamental roles in both military and civilian contexts, the precise molecular dynamics governing their pronounced reactivity and impact sensitivity have remained inadequately characterized. Our research establishes that low O–NO2 bond dissociation energies are the primary driver of ultrafast fragmentation and high reactivity in all three esters. The distinctive molecular architectures of these esters dictate their explosive properties: NG’s tri-nitrate configuration leads to exceptional …

Relationship Extraction Using Retrieval Augmented Generation For Biomedical Dataset, Jannat .

Theses and Dissertations

With the increasing number of structured and unstructured data, obtaining reliable information effectively has become crucial. In the biomedical domain, extracting information from the scientific papers is crucial in order to stay up-to-date with accurate information, given the increased pace by which new research studies are published. This work focuses on identifying relationships between entities that are extracted from the abstracts and titles of biomedical research papers. In this work, we developed a Retrieval Augmented Generation (RAG) based system to automatically identify relations between biomedical entities. We evaluate multiple open source Large Language Models (LLMs) and the number of examples …

Heterogeneous Clustering Of Multiomics Data For Breast Cancer Subgroup Classification And Detection, Joseph Pateras, Musaddiq Lodi, Pratip Rana, Preetam Ghosh

Computer Science Faculty Publications

The rapid growth of diverse -omics datasets has made multiomics data integration crucial in cancer research. This study adapts the expectation–maximization routine for the joint latent variable modeling of multiomics patient profiles. By combining this approach with traditional biological feature selection methods, this study optimizes latent distribution, enabling efficient patient clustering from well-studied cancer types with reduced computational expense. The proposed optimization subroutines enhance survival analysis and improve runtime performance. This article presents a framework for distinguishing cancer subtypes and identifying potential biomarkers for breast cancer. Key insights into individual subtype expression and function were obtained through differentially expressed gene …

Gramseq-Dta: A Grammar-Based Drug-Target Affinity Prediction Approach Fusing Gene Expression Information, Kasul Debnath, Pratip Rana, Preetam Ghosh

Computer Science Faculty Publications

Drug–target affinity (DTA) prediction is a critical aspect of drug discovery. The meaningful representation of drugs and targets is crucial for accurate prediction. Using 1D string-based representations for drugs and targets is a common approach that has demonstrated good results in drug–target affinity prediction. However, these approach lacks information on the relative position of the atoms and bonds. To address this limitation, graph-based representations have been used to some extent. However, solely considering the structural aspect of drugs and targets may be insufficient for accurate DTA prediction. Integrating the functional aspect of these drugs at the genetic level can enhance …

Prompt-Based Learning For Few-Shot Class-Incremental Learning, Jicheng Yuan, Hang Chen, Songsong Tian, Wenfa Li, Lusi Li, Enhao Ning, Yugui Zhang

Computer Science Faculty Publications

Few-Shot Class-Incremental Learning (FSCIL) aims to enable deep neural networks to incrementally learn new tasks from a limited number of labeled samples, while retaining knowledge of previously learned tasks, mimicking the way humans learn. In this paper, we introduce a novel approach called Prompt Learning for FSCIL (PL-FSCIL), which leverages the power of prompts alongside a pre-trained Vision Transformer (ViT) model to effectively tackle the challenges of FSCIL. Our approach explores the feasibility of directly applying visual prompts in FSCIL, using a simplified model architecture. PL-FSCIL integrates two key prompts: the Domain Prompt and the FSCIL Prompt. Both are tensors …

Vaim-Cff: A Variational Autoencoder Inverse Mapper Solution To Compton Form Factor Extraction From Deeply Virtual Compton Scattering, Manal Almaeen, Tareq Alghamdi, Brandon Kriesten, Douglas Adams, Yaohang Li, Huey-Wen Lin, Simonetta Liuti

Computer Science Faculty Publications

We develop a new methodology for extracting Compton form factors (CFFs) from deeply virtual exclusive reactions such as the unpolarized DVCS cross section using a specialized inverse problem solver, a variational autoencoder inverse mapper (VAIM). The VAIM-CFF framework not only allows us access to a fitted solution set possibly containing multiple solutions in the extraction of all 8 CFFs from a single cross section measurement, but also accesses the lost information contained in the forward mapping from CFFs to cross section. We investigate various assumptions and their effects on the predicted CFFs such as cross section organization, number of extracted …

An Analytical Review Of Preprocessing Techniques In Bengali Natural Language Processing, Sovon Chakraborty, Protiva Das, Shakib Mahmud Dipto, Md Aktaruzzaman Pramanik, Jannatun Noor

Computer Science Faculty Publications

Research in Bengali Natural Language Processing (BNLP) is rapidly expanding. Despite being one of the most widely spoken languages in the world, BNLP research remains insufficient, particularly in Bengali speech recognition. The languages rich morphology, agglutinative structure, and diverse dialects make text and speech processing especially challenging. However, these challenges can be addressed with effective preprocessing techniques. Various organizations in Bangladesh and West Bengal are integrating Natural Language Processing (NLP) into their services, but without a thorough understanding of preprocessing, these implementations remain incomplete. Applying proper preprocessing techniques to the Bengali language will serve as a foundation for developing robust …

Not Here, Go There: Analyzing Redirection Patterns On The Web, Kritika Garg, Sawood Alam, Dietrich Ayala, Michele C. Weigle, Michael L. Nelson

Computer Science Faculty Publications

URI redirections are integral to web management, supporting structural changes, SEO optimization, and security. However, their complexities affect usability, SEO performance, and digital preservation. This study analyzed 11 million unique redirecting URIs, following redirections up to 10 hops per URI, to uncover patterns and implications of redirection practices. Our findings revealed that 50% of the URIs terminated successfully, while 50% resulted in errors, including 0.06% exceeding 10 hops. Canonical redirects, such as HTTP to HTTPS transitions, were prevalent, reflecting adherence to SEO best practices. Non-canonical redirects, often involving domain or path changes, highlighted significant web migrations, rebranding, and security risks. …

Github Repository Complexity Leads To Diminished Web Archive Availability, David Calano, Michael Nelson, Michele Weigle

Computer Science Faculty Publications

Software is often developed using versioned controlled software, such as Git, and hosted on centralized Web hosts, such as GitHub and GitLab. These Web hosted software repositories are made available to users in the form of traditional HTML Web pages for each source file and directory, as well as a presentational home page and various descriptive pages. We examined more than 12,000 Web hosted Git repository project home pages, primarily from GitHub, to measure how well their presentational components are preserved in the Internet Archive, as well as the source trees of the collected GitHub repositories to assess the extent …

The Invisible Influencer In Information Infrastructure, Herbert Van De Sompel, Michael L. Nelson

Computer Science Faculty Publications

The UPS Prototype was a proof-of-concept web portal built in preparation for the Universal Preprint Service Meeting held in October 1999 in Santa Fe, New Mexico. The portal provided search functionality for a set of metadata records that had been aggregated from a range of repositories that hosted preprints, working papers, and technical reports. Every search result was overlaid with a dynamically generated menu, called an SFX-menu, that provided a selection of value-adding links for the described scholarly work. The meeting eventually led to the Open Archives Initiative and its Protocol for Metadata Harvesting (OAI-PMH), which remains widely used in …

Uncertainty-Aware Deep Learning Framework For Forecasting Coastal Water Level In Virginia Beach, Md Mahmudul Hasan, Malachi Schram, Sridhar Katragadda, Diana Mcspadden, Alisa N. Udomvisawakul, Heather Richter, Frank Liu

Computer Science Faculty Publications

Coastal areas like Virginia Beach, USA, are increasingly vulnerable to flooding. To mitigate the impact of flooding, it is crucial for the City of Virginia Beach to have reliable 72-hour-ahead (3 days) forecasts of water levels at key gauge locations. To support this effort, several sensors have been installed throughout the city to monitor water levels and other environmental parameters such as wind speed, precipitation, and atmospheric pressure. Leveraging sensor data from one of these locations, we developed an uncertainty-aware deep learning model to forecast water levels. We employed deep quantile regression (DQR) to quantify variability in the predictions and …

Human Perception Of Ai Capabilities At Classifying Perturbed Roadway Signs, Katherine R. Garcia, Jing Chen, Yanru Xiao, Scott Mishler, Cong Wang, Bin Hu

Computer Science Faculty Publications

Artificial Intelligence (AI) is crucial to numerous functions required for driving automation systems, including the computer vision techniques used to detect the roadway environment and make real-time decisions. However, the images used as inputs to the AI system may be maliciously perturbed, or manipulated, causing the AI system to make an incorrect classification. In this study, we examined humans’ perception of the AI’s computer vision capability of classifying various road sign images, including the original images, images with two different types of malicious attacks, and images that are scrambled randomly at the pixel level. Our results showed that participants rated …

A Real-Time Approach To Capture Ambient And Focal Attention In Visual Search, Gavindya Jayawardena, Yasith Jayawardana, Yasasi Abeysinghe, Bhanuka Mahanama, Sampath Jayarathna, Jacek Gwizdka

Computer Science Faculty Publications

During visual search, individuals’ attention shifts between ambient and focal states in response to task demands and stimuli. The ambient/focal coefficient K is a statistically validated measure of these states, computed offline from fixation duration and saccade amplitude data. While current methods compute K offline, real-time computation could enable applications such as monitoring user attention, creating attention-adaptive user interfaces, and optimizing graphics rendering. However, real-time computation of K requires stable estimates for the parameters of fixation duration and saccade amplitude distributions. Since these distributions are heavy-tailed, the real-time estimates exhibit high variance and slow convergence. To overcome this, we propose …

Multi-Modal Mri Based Segmentation Of Brain Metastases Using Adaptive Self-Attention, Evan Savaria

Computer Science Faculty Publications

Brain metastases (BMs) are the most common adult central nervous system malignancy, affecting 20–40% of cancer patients. Accurate segmentation of metastatic lesions in multi-modal MRI is essential for treatment planning and prognosis however, manual delineation is time consuming and prone to variability. Traditional deep learning models such as U-Net, have improved segmentation accuracy but capture limited long-range dependencies and struggle with variations in metastasis size, shape, and distribution. This study introduces the Adaptive Integrated Multi-modal Segmentation (AIMS) model, an adaptive self-attention framework within a hybrid U-Net and Transformer architecture to enhance BM segmentation by leveraging multi-modal MRI integration. The proposed …

Optiselect And Enshap: Integrating Machine Learning And Game Theory For Ischemic Stroke Prediction, Pritam Chakraborty, Anjan Bandyopadhyay, Sricheta Parul, Sujata Swain, Partha Sarathy Banerjee, Tapas Si, Hong Qin, Saurav Mallik

Computer Science Faculty Publications

Stroke analysis using game theory and machine learning techniques. The study investigates the use of the Shapley value in predictive ischemic brain stroke analysis. Initially, preference algorithms identify the most important features in various machine learning models, including logistic regression, K-nearest neighbor, decision tree, support vector machine (linear kernel), support vector machine ( RBF kernel), neural networks, etc. For each sample, the top 3, 4, and 5 features are evaluated and selected to evaluate their performance. The Shapley value method was used to rank the models using their best four features based on their predictive capabilities. As a result, better-performing …

Ai For Nuclear Physics: The Exclaim Project, S. Liuti, D. Adams, M. Boër, G. W. Chern, M. Cuic, M. Engelhardt, G. R. Goldstein, B. Kriesten, Y. Li, H. W. Lin, M. Sievert, D. Sivers

Computer Science Faculty Publications

An overview of the recent activity of the newly funded EXCLusives with AI and Machine learning (EXCLAIM) collaboration is presented. The main goal of the collaboration is to develop a framework to implement AI and machine learning techniques in problems emerging from the phenomenology of high energy exclusive scattering processes from nucleons and nuclei, maximizing the information that can be extracted from various sets of experimental data, while implementing theoretical constraints from lattice QCD. A specific perspective embraced by EXCLAIM is to use the methods of theoretical physics to understand the working of ML, beyond its standardized applications to physics …

Insights In Adaptation: Examining Self-Reflection Strategies Of Job Seekers With Visual Impairments In India, Akshay Kolgar Nayak, Yash Prakash, Sampath Jayarathna, Hae-Na Lee, Vikas Ashok

Computer Science Faculty Publications

Significant changes in the digital employment landscape, driven by rapid technological advancements and the COVID-19 pandemic, have introduced new opportunities for blind and visually impaired (BVI) individuals in developing countries like India. However, a significant portion of the BVI population in India remains unemployed despite extensive accessibility advancements and job search interventions. Therefore, we conducted semi-structured interviews with 20 BVI persons who were either pursuing or recently sought employment in the digital industry. Our findings reveal that despite gaining digital literacy and extensive training, BVI individuals struggle to meet industry requirements for fulfilling job openings. While they engage in self-reflection …

A Survey On Deep Learning For Drug-Target Binding Prediction: Models, Benchmarks, Evaluation, And Case Studies, Kusal Debnath, Pratip Rana, Preetam Ghosh

Computer Science Faculty Publications

Conventional drug discovery is expensive, time-consuming, and prone to failure. Artificial intelligence has become a potent substitute over the last decade, providing strong answers to challenging biological issues in this field. Among these difficulties, drug-target binding (DTB) is a key component of drug discovery techniques. In this context, drug-target affinity and drug–target interaction are complementary and essential frameworks that work together to improve our comprehension of DTB dynamics. In this work, we thoroughly analyze the most recent deep learning models, popular benchmark datasets, and assessment metrics for DTB prediction. We look at the paradigm shift in the development of drug …

A Bibliographic And Topic Modeling Analysis Of The P-Adic Theory Literature Using Latent Dirichlet Allocation, Humberto Llinás, Ismael Gutiérrez, Anselmo Torresblanca, Javier De La Hoz, Brian Llinás

Computer Science Faculty Publications

P-adic analysis, introduced by Kurt Hensel in the early 20th century, has developed into a fundamental area of mathematical research with broad applications in number theory, algebraic geometry, and mathematical physics. This study aims to examine the thematic evolution and scholarly impact of p-adic research through a comprehensive topic modeling and bibliometric analysis. Using classical bibliometric techniques (e.g., performance analysis, co-authorship, and co-citation networks) combined with Latent Dirichlet Allocation (LDA), we analyzed 7388 peer-reviewed documents published between 1965 and 2024. The computational workflow was conducted using R (version 4.4.1) and VOSviewer (version 1.6.20), which enabled the identification of 20 distinct …