Digital Commons Network^™

The Molecular Effects Of Microwave Irradiation Upon Antibiotic-Resistant Bacterial Pathogens, Alistair White

Peninsula Dental School Theses

IntroductionAntimicrobial resistance is a global health crisis due to the emergence and spread of multidrug-resistant bacteria, driven by poor antibiotic stewardship, particularly in low to middle income countries where antibiotics can be bought over the counter. Currently, the molecular diagnosis of infections and genomic DNA extraction are both hindered by several factors including, expense, time, technical expertise, and the requirement of a laboratory-based setting. This makes it difficult to extract genomic material at point-of-care preventing the use of rapid molecular methods for the detection of antibiotic resistant genes. Thus, the gold standard for determining the appropriate antibiotics to treat infections …

Design, Synthesis, And Characterization Of Phosphonate-Functionalized Diimide Ligands For Metal-Organic Framework Construction, Kenya Rosas, Jonathan Uribe

Posters - 2026

• Metal–organic frameworks (MOFs) are solid, porous materials composed of metalions reacted with organic linkers

• MOFs are customizable through variations in their metal ions, organic linkers, and the functional groups of the organic linker all yielding a structure with different properties

• MOFs have a variety of applications, ranging from storage and catalysis to drug delivery

• The synthesis and characterization of four phosphonate-based diimide ligands:
  • N,N´-bis(phosphonomethyl)-pyromellitimide (PPMI).
  • N,N´-bis(phosphonobenzyl)-pyromellitimide (PPMI-Ph).
  • N,N´-bis(phosphonomethyl)-3,3′,4,4′-biphenylenediimide (PBDI).
  • N,N´-bis(phosphonobenzyl)-3,3′,4,4′-biphenylenediimide (PBDI-Ph).

• These ligands were then coordinated with zinc or copper metal ions to make MOFs.

Stability Of Brown Carbon And Lipid Monolayers At The Air-Water Interface, Maaike Snider

Student Scholarship

No abstract provided.

Developing A Serious Game-Based Platform To Support Programming Learning, Noven Indra Prasetya, Nia Saurina, Arief Ardiansyah

Journal of Educational Technology Development and Exchange (JETDE)

Programming learning, especially among informatics students, faces significant challenges related to understanding concepts and syntax, as well as low engagement in programming courses. To address these issues, this research developed DolananCoding, a serious game-based learning platform specifically designed for C++ programming learning. The platform integrates a live coding feature, enabling students to write code directly, along with a leaderboard to motivate them through healthy competition. The immersive concept in DolananCoding is designed to provide an interactive and enjoyable learning experience, aiming to enhance students’ engagement, motivation, and learning outcomes in C++ programming. This research aims to test the effectiveness of …

Large Language Model-Based Automated Item Generation In Stem Assessments: Historical Mapping And A Scoping Review Of Empirical Studies, Moses Oluoke Omopekunola

Journal of Educational Technology Development and Exchange (JETDE)

Educational assessments, from low-stakes classroom tests to high-stakes national examinations, require item pools that are valid, fair, and secure. Automated Item Generation (AIG) aims to efficiently produce large pools of calibrated test items. This paper adopts a two-part design: (1) a brief historical mapping situating LLM-based AIG within the broader AIG trajectory; and (2) a scoping review of empirical studies on LLM-based AIG for STEM assessments, published between January 2022 and January 2026. A structured search of ERIC, Lens and OpenAlex yielded 1,267 records; after deduplication and screening, 7 studies were retained for synthesis. In all studies, LLMs were primarily …

Digital Transformation And Artificial Intelligence In Education In Vietnam: A Bibliometric And Systematic Review, Minh-Anh Thi Nguyen, Van-Quynh Ha

Journal of Educational Technology Development and Exchange (JETDE)

Globally, digital transformation (DT) is reshaping education through data-driven innovation, artificial intelligence (AI), and emerging learning technologies. These transformative forces are influencing not only instructional methods but also governance, equity, and sustainability in education. Within this context, the study provides a comprehensive review of DT and AI in Vietnam’s education sector. Combining bibliometric mapping with a systematic content analysis, it synthesizes recent scholarly developments to reveal key research trends, methodological orientations, and conceptual structures. Five thematic clusters are identified, including digital competence, institutional transformation strategies, AI literacy, and learner engagement, potentially reflecting a shift from institutional readiness toward learner-centered innovation. …

Coding, Machine Learning, And Game Engines In Initial Teacher Training, Sara Redondo-Duarte, José Manuel Sáez-López

Journal of Educational Technology Development and Exchange (JETDE)

This study evaluated the integration of coding, visual block programming, game engines, and machine learning (ML) modeling in initial teacher education. The aim was to examine the feasibility of this instructional approach and its association with pre-service teachers’ computational knowledge and attitudes toward programming and artificial intelligence (AI). A one-group pretest-posttest design was implemented with 129 undergraduate students enrolled in Primary Education degree programs at two public universities in Spain. Knowledge of computational concepts was assessed with a 10-item test analyzed using a paired-samples Student’s t-test, while attitudes toward programming and ML were evaluated with two Likert scales analyzed using …

Investigation Of Dean’S Transformational Leadership Profiles And Digital Transformation Maturity In Guangxi Public Higher Vocational Colleges, Lili Ding, Jamilah Binti Ahmad, Yuqiang Luo

Journal of Educational Technology Development and Exchange (JETDE)

Background: As China builds an innovation-driven economy, vocational education provides essential skilled workers for key industries, helping to elevate vocational colleges into crucial centers for talent development and social mobility. In this context, the dean’s transformational leadership may play an important role in supporting institutional digital transformation.

Research Question: How do deans’ transformational leadership domains predict institutional digital transformation maturity across leadership and pedagogy in vocational education?

Methods: A sequential explanatory mixed-methods design combined a faculty survey with semi-structured interviews. Valid survey responses from 30 faculty members were analyzed using descriptive statistics, correlations, and regression, followed by thematic analysis of …

Recent Progresses In Few-Nucleon Structure And Dynamics In Chiral Effective Field Theory, Laura Elisa Marcucci, Alex Gnech, Michele Viviani

Physics Faculty Publications

The most recent progresses made within the framework of chiral effective field theory for few-nucleon structure and low-energy reactions are here presented. In particular, for the A = 2 sector, the study of muon capture on deuteron is reviewed. Then, the results obtained using the Hyperspherical Harmonics ab-initio method for the 4He monopole form factor and the ³He(n^→ ,p)³H parity-conserving asymmetry are described.

K-Long Facility At Jlab, Moskov Amaryan

Physics Faculty Publications

In this talk I present the outline of K-long Facility (KLF) at JLab [1]. It was approved by PAC48 in 2020 to run for 200 days of beamtime, equally divided between liquid hydrogen and deuterium targets, to measure dozens of hyperon states predicted by CQM and LQCD but not yet established. This facility also will allow to measure Kπ scattering in different channels to observe the so-called ᴷ scalar meson and measure its width and position with unprecedented accuracy. Finally, it will be shown that exotic baryons can be measured at this facility in formation reactions with a two-body final …

Longitudinal Spin Transfer To Λ Hyperons In Semi-Inclusive Deep Inelastic Scattering With The Clas12 Spectrometer, M. Mceneaney, A. Vossen, A. Acar, P. Achenbach, J. S. Alvarado, M. Amaryan, W. R. Armstrong, H. Atac, N. A. Baltzell, L. Barion, M. Bashkanov, M. Battaglieri, F. Benmokhtar, A. Bianconi, A. S. Biselli, M. Bondi, F. Bossù, S. Boiarinov, K. -Th. Brinkmann, W. J. Briscoe, V. D. Burkert, T. Cao, R. Capobianco, D. S. Carman, J. C. Carvajal, A. Celentano, P. Chatagnon, V. Chesnokov, H. Chinchay, G. Ciullo, P. L. Cole, M. Contalbrigo, A. D'Angelo, N. Dashyan, R. De Vita, M. Defurne, S. Diehl, C. Dilks, C. Djalali, R. Dupre, H. Egiyan, M. Ehrhart, A. El Alaoui, L. El Fassi, M. Farooq, S. Fegan, R. E. Ferguson, I. P. Fernando, A. Filippi, C. Fogler, K. Gates, G. Gavalian, D. I. Glazier, R. W. Gothe, Y. Gotra, B. Gualtieri, K. Hafidi, H. Hakobyan, M. Hattawy, T. B. Hayward, D. Heddle, A. Hobart, M. Holtrop, Y. Ilieva, D. G. Ireland, H. S. Jo, S. Joosten, T. Kageya, A. Kim, V. Klimenko, A. Kripko, V. Kubarovsky, L. Lanza, S. Lee, P. Lenisa, D. Marchand, V. Mascagna, D. Matamoros, B. Mckinnon, T. Mineeva, M. Mirazita, V. Mokeev, C. Munoz Camacho, P. Nadel-Turonski, T. Nagorna, K. Neupane, S. Niccolai, G. Niculescu, M. Osipenko, M. Ouillon, P. Pandey, M. Paolone, L. L. Pappalardo, R. Paremuzyan, E. Pasyuk, S. J. Paul, W. Phelps, N. Pilleux, S. Polcher Rafael, L. Polizzi, J. Poudel, Y. Prok, A. Radic, T. Reed, J. Richards, M. Ripani, P. Rossi, A. A. Rusova, C. Salgado, S. Schadmand, A. Schmidt, M. B. C. Scott, E. V. Shirokov, S. Shresstha, E. Sidoretti, D. Sokhan, N. Sparveris, M. Spreafico, I. Strakovsky, S. Strauch, J. A. Tan, R. Tyson, M. Ungaro, P. S. H. Vaishnavi, S. Vallarino, C. Velasquez, L. Venturelli, H. Voskanyan, E. Voutier, D. P. Watts, Y. Wang, U. Weerasinghe, X. Wei, N. Wickramaarachchi, M. H. Wood, L. Xu, Z. Xu, N. Zachariou, Z. W. Zhao, V. Ziegler, M. Zurek

Physics Faculty Publications

The polarization of Λ hyperons is preserved in the angular distribution of their decay products. This property allows one to study the spin structure of the Λ. In semi-inclusive deep inelastic scattering where a high energy lepton interacts with a nucleon target and one or more hadrons and the scattered lepton are detected in the final state, the probability for a struck quark to impart the polarization of the lepton to the Λ may be measured. In particular, in electron-proton scattering this quantity may be related to the longitudinal light quark polarization of the Λ. Currently, limited experimental data cannot …

Inverse Problem In The Large Momentum Effective Theory Framework, Herve Dutrieux, Joe Karpie, Christopher J. Monahan, Kostas Orginos, Anatoly Radyushkin, David Richards, Savvas Zafeiropoulos

Physics Faculty Publications

One proposal to compute parton distributions from first principles is the large momentum effective theory (LaMET), which requires the Fourier transform of matrix elements computed nonperturbatively. Lattice quantum chromodynamics (QCD) provides calculations of these matrix elements over a finite range of Fourier harmonics that are often noisy or unreliable in the largest computed harmonics. It has been suggested that enforcing an exponential decay of the missing harmonics helps alleviate this issue. Using nonperturbative data, we show that the uncertainty introduced by this inverse problem in a realistic setup remains significant without very restrictive assumptions, and that the importance of the …

Reaching The Intrinsic Performance Limits Of Superconducting Nanowire Single-Photon Detectors Up To 0.1 Mm Wide, Kristen M. Parzuchowski, Eli Mueller, Bakhrom G. Oripov, Benedikt Hampel, Ravin A. Chowdhury, Sahil R. Patel, Daniel Kuznesof, Emma K. Batson, Ryan Morgenstern, Robert H. Hadfield, Varun B. Verma, Matthew D. Shaw, Jason P. Allmaras, Martina J. Stevens, Alex Gurevich, Adam N. Mccaughan

Physics Faculty Publications

Superconducting nanowire single-photon detectors combine high detection efficiency, low noise, and excellent timing resolution, making them a leading platform for photon-counting applications. However, despite decades of materials and fabrication research, detector performance has never been shown to match theoretical performance expectations. Here, we demonstrate in situ tuning of a detector from its typical, suboptimal operation, to a regime limited only by material quality, allowing the device to reach its intrinsic performance limit. Our approach is based on current-biased superconducting “rails” placed on either side of the detector that redistribute current across its width to achieve peak performance. This technique reduces …

Water Resource Protection Principles And Strategies, Julia Peterson

UNH Cooperative Extension

No abstract provided.

Stormwater Best Management Practices, Kat Kelleher

UNH Cooperative Extension

No abstract provided.

Cryogenic Sloshing In Aircraft Fuel Tanks, Stuart Colville

School of Engineering, Computing and Mathematics Theses

Liquid sloshing in next-generation sub-cooled liquid hydrogen aircraft fuel tanks can induce rapid ullage pressure and temperature drops, potentially causing cavitation in cryogenic pumping systems and compromising fuel delivery systems. Sloshing events may be initiated during taxiing, take-off, landing, and turbulence, with large accelerations producing highly non-linear liquid motion and wave-breaking conditions. Understanding such phenomena is essential for cryogenic hydrogen fuel tank design and certification for next generation aircraft.A systematic experimental methodology was employed to investigate both the wave kinematic and thermodynamic aspects of sloshing. Initial water–air experiments in a simplified horizontal circular tank were conducted to validate measurement techniques, …

Untrained Position-Encoded Multilayer Perceptron Network For Structured Illumination Microscopy Reconstruction, Sahil Sharma, Leonidas Zimianitis, Krishnendu Samanta, Balpreet Singh Ahluwalia, Joby Joseph, Dushan N. Wadduwage

Computer Science Faculty Publications

Structured Illumination Microscopy (SIM) enables super-resolution imaging by encoding high-frequency spatial information through patterned light. While traditional Fourier-based reconstruction methods are prone to artifacts under suboptimal conditions, recent deep learning approaches often require large training datasets and lack adaptability across different imaging setups. In this work, we present Position Encoded Multi-Layer Perceptron (PEM) network that leverages implicit neural representations (INRs) and SIM forward-model-driven modeling to reconstruct super-resolved images without any training data. PEM-SIM represents each spatial coordinate as a combination of sinusoidal functions across multiple frequencies, enabling rich encoding of fine spatial detail. A forward model grounded in SIM image …

Application Paths Of Semantic Modeling In Financial Fraud Detection And Risk Identification, Victor P. Gauthier, Daniel S. Wu

Computer Science Faculty Publications

Financial fraud and risk pose significant threats to economic stability and individual well-being. Traditional detection methods often struggle to keep pace with increasingly sophisticated fraudulent schemes. Semantic modeling, which focuses on understanding the meaning and relationships within data, offers a promising avenue for enhancing fraud detection and risk identification. This review paper explores the application paths of semantic modeling in this domain. We begin with a historical overview of fraud detection techniques, highlighting the limitations of traditional approaches. Subsequently, we delve into core themes, including knowledge graph-based fraud detection and semantic rule-based inference for risk assessment. We then compare and …

Integration Of Hybrid Quantum-Neuromorphic Ai With Cloud, Edge, And High-Performance Computing Environments, Arun B. Prasad, Ajay Prasad, Dineshkumar Rajendran, Anurag Tiwari, T. Akilan, Islombek Khushvaktov

Computer Science Faculty Publications

The convergence of quantum computing, neuromorphic learning, and distributed cloud infrastructures has occurred very rapidly, and intelligent systems are now providing new opportunities, but the challenge of instability, complexity of orchestration, and noise sensitivity remains in the way of practical integration. The proposed work is based on a hybrid quantum and neuromorphic architecture, which is the integration of event-based neuromorphic adaptation and quantum-assisted global optimization, orchestrated by cloud-HPC. The architecture presents the thermodynamically regularized learning and resourceful task scheduling to the probabilistic search and the continuous local adaptation. Experimental evaluation across financial modeling, medical imaging, and physical system prediction shows …

Cognitive Prosthetic: An Ai-Enabled Multimodal System For Episodic Recall In Knowledge Work, Lawrence Obiuwevwi, Krzystof J. Rechowicz, Vikas Ashok, Sachin Shetty, Sampath Jayarathna

Computer Science Faculty Publications

Modern knowledge workplaces increasingly strain human episodic memory as individuals navigate fragmented attention, overlapping meetings, and multimodal information streams. Existing workplace tools provide partial support through note-taking or analytics but rarely integrate cognitive, physiological, and attentional context into retrievable memory representations. This paper presents the Cognitive Prosthetic Multimodal System (CPMS)—an AI-enabled proof-of-concept designed to support episodic recall in knowledge work through structured episodic capture and natural language retrieval. CPMS synchronizes speech transcripts, physiological signals, and gaze behavior into temporally aligned, JSON-based episodic records processed locally for privacy. Beyond data logging, the system includes a web-based retrieval interface that allows users …

Exploring Marshall–Olkin Models Through Bibliometric And Topic Modeling Approaches Uses Latent Dirichlet Allocation (1981-2025): A Study Based On Scopus Data, Humberto Llinás, Brian Llinás, Carlos López, Daniela Nuñez

Computer Science Faculty Publications

The Marshall–Olkin family of distributions has gained increasing attention in fields such as reliability engineering, survival analysis, financial risk modeling, and actuarial science because of its flexibility in modeling dependence among events and its wide range of extensions. Despite its growing relevance, a systematic understanding of how research on Marshall–Olkin models has evolved over time is still limited. This study addresses this gap by combining bibliometric techniques with topic modeling to analyze the structure and evolution of the scientific literature on Marshall–Olkin models. The analysis includes all 266 peer-reviewed publications on Marshall–Olkin models indexed in Scopus between 1981 and 2025. …

Stochastic Fractional-Order Memristive Fuzzy Bam Neural Networks With Time Delays And Leakage Term For Finite-Time Stability Analysis, J. Kumar, M. Syed Ali, Sumaya Sanober, Mohammad Yarish, Abeer M. Alotaibi, Tarek F. Ibrahim

Computer Science Faculty Publications

In this study, a finite-time stability analysis with time delays and a leakage term is conducted on stochastic fractional-order memristive fuzzy BAM neural networks. FOMFBAMNNs are developed using set-valued map theories as well as differential inclusion. We obtained several significant adequate criteria of uniform stability in the mean square of such networks by using analytical methods and inequality approaches, such as Cauchy–Schwarz inequality and Burkholder–Davis–Gundy inequality. In addition to examining two different fractional-order derivatives between the U-layer and V-layer synchronously with fractional order, the existence, uniqueness, and stability of its equilibrium point are also shown ½ ≤ α ≤ 1. …

Towards Supporting Real-Time Estimation Of Vehicle Fuel Consumption And Co2 Emissions In Smart City Applications, Abrar Alali, Stephan Olariu

Computer Science Faculty Publications

This paper evaluates a simplified physics-based energy demand model designed to estimate vehicle fuel consumption and CO₂ emissions—a critical tool for sustainable transportation planning and smart city applications. Unlike data-driven regression models that lack generalizability for user-defined conditions or complex physics-based approaches that rely on extensive, often proprietary data, the simplified model is distinguished by its minimal parameter requirements, depending primarily on a single, overarching powertrain efficiency value. A key contribution is the comprehensive empirical evaluation of the simplified model against official Environmental Protection Agency (EPA) test data across multiple driving cycles and vehicle types, providing a rigorous validation previously …

Contextual Scaffolding And Self-Efficacy: Supporting Computer Skill Development Among Blind Learners In India, Akshay Kolgar Nayak, Yash Prakash, Sampath Jayarathna, Hae-Na Lee, Vikas Ashok

Computer Science Faculty Publications

Inclusive computer literacy education efforts, broadening the participation of blind or visually impaired (BVI) individuals, have gained traction in recent years. Existing literature investigating these efforts primarily draws evidence from affluent Global North contexts, where accessibility resources and legal frameworks are relatively more mature. Little is known about the in-situ teaching and learning challenges faced by trainers and BVI students, respectively, in resource-constrained, multicultural Global South countries like India. To address this knowledge gap, we conducted a four-month contextual inquiry at two computer training centers catering to 94 BVI students in India. We notably observed a rigid, experience-driven training environment …

Open Scholarly Information Systems: Status Quo, Challenges, Opportunities, Hannah Bast, Guillaume Cabanac, Paolo Manghi, Jian Wu, Marcel R. Ackermann

Computer Science Faculty Publications

Over the past 30 years, a rich ecosystem of scholarly information systems has developed that openly provide their services to the scientific community. These systems include aggregators of bibliographic metadata (e.g., DBLP, OpenCitations, OpenAIRE Graph, OpenAlex, ORKG, Semantic Scholar, CiteSeerX, and CORE); publication, data, and software repositories (e.g., Arxiv.org, Figshare, Zenodo, Software Heritage, and Dataverse); and PID authorities (e.g., ORCID, ROR, Crossref, and DataCite). This interdisciplinary Dagstuhl Seminar "Open Scholarly Information Systems: Status Quo, Challenges, Opportunities" (25381) was the first of its kind to bring together practitioners from this ecosystem, as well as researchers investigating related questions or relying on …

Finding The Signal In The Noise: An Exploratory Study On Assessing The Effectiveness Of Ai And Accessibility Forums For Blind Users' Support Needs, Satwik Ram Kodandaram, Jiawei Zhou, Xiaojun Bi, Iv Ramakrishnan, Vikas Ashok

Computer Science Faculty Publications

Accessibility forums and, more recently, generative AI tools have become vital resources for blind users seeking solutions to computer-interaction issues and learning about new assistive technologies, screen reader features, tutorials, and software updates. Understanding user experiences with these resources is essential for identifying and addressing persistent support gaps. Towards this, we interviewed 14 blind users who regularly engage with forums and GenAI tools. Findings revealed that forums often overwhelm users with multiple overlapping topics, redundant or irrelevant content, and fragmented responses that must be mentally pieced together, increasing cognitive load. GenAI tools, while offering more direct assistance, introduce new barriers …

Explainable Convolutional Neural Network Model Provides An Alternative Genome-Wide Association Perspective On Mutations In Sars-Cov-2, Parisa C. Hatami, Richard Annan, Luis Miranda, Jane L. Gorman, Mengjun Xie, Letu Qingge, Hong Qin

Computer Science Faculty Publications

Identifying informative genomic features in SARS-CoV-2 can help clarify patterns of viral evolution. In this study, we developed an explainable convolutional neural network (CNN) model to classify SARS-CoV-2 genomic sequences into the WHO-designated Variants of Concern (VOCs), Alpha, Beta, Gamma, Delta, and Omicron. Using a balanced dataset of genomes, the classification CNN achieved 99.96% accuracy on the held-out test set. To interpret the model’s predictions, we applied SHapley Additive exPlanations (SHAP) to estimate the contribution of each nucleotide position to VOC-label prediction and compared aggregated attributions with a chi-square GWAS baseline applied to the same categorical labels. SHAP prioritized several …

A Comparative Analysis Of Explainable Ai (Xai) Techniques For Transparent And Reliable Image Classification, Sovon Chakraborty, Shakib Mahmud Dipto, Kevin R. Pilkiewicz, Michael L. Mayo, Pratip Rana

Computer Science Faculty Publications

Evaluating the trustworthiness of black-box machine learning models remains a significant methodological challenge. Their lack of transparency and interpretability limits applicability, because stakeholders often seek transparency before trusting the results of black-box machine learning models. Explainable AI (XAI) methods provide for human-understandable justifications and informed decision-making of these black-box architectures. Therefore, it is imperative to select the proper XAI model tailored to specific tasks. In this research, we focus on examining four XAI techniques: PEEK, LRP, GRAD-CAM, and LIME to understand how they perform against each other for image classification tasks. We evaluate the performance, robustness, generalizability, noise stability, and …

Guidelines For Automatic Grading Of Student Essays Using Large Language Models, Diwakar Yalpi, Sruta Keerti Kasula, Ravi Mukkamala

Computer Science Faculty Publications

Automated essay evaluation using large language models (LLMs) has emerged as a promising approach to support scalable and consistent educational assessment. However, the effectiveness of LLM-based grading varies significantly across evaluation dimensions and is highly influenced by prompt design and model selection. In this study, we evaluate five state-of-the-art LLMs across five rubric-based categories: Relevance to Question, Reasoning and Critical Thinking, Evidence and Examples, Organization, and Clarity and Writing Quality. We systematically investigate the impact of three prompting strategies, including rubric-only prompting, exemplar-based prompting (with and without rubric guidance)(Original and Refined prompt designs) incorporating structured instructions. Additionally, a prompt ablation …

Toward An Event-Level Analysis Of Hadron Structure Using Differential Programming, Kevin Braga, Markus Diefenthaler, Steven Goldenberg, Daniel Lersch, Yaohang Li, Jian-Wei Qiu, Kishansingh Rajput, Felix Ringer, Nobuo Sato, Malachi Schram

Computer Science Faculty Publications

Reconstructing the internal properties of hadrons in terms of fundamental quark and gluon degrees of freedom is a central goal in nuclear and particle physics. This effort lies at the core of major experimental programs, such as the Jefferson Lab 12 GeV program and the upcoming Electron-Ion Collider. A primary challenge is the inherent inverse problem: converting large-scale observational data from collision events into the fundamental quantum correlation functions (QCFs) that characterize the microscopic structure of hadronic systems within the theory of QCD. Recent advances in scientific computing and machine learning have opened new avenues for addressing this challenge using …