Physical Sciences and Mathematics Commons^™

Medical Linear Accelerator Water Tank Measurements Optimization For Commissioning, Tyler R. Laugh

Theses and Dissertations

Beam commissioning is a critical component of linear accelerator (LINAC) implementation, requiring accurate measurement of percent depth dose (PDD), beam profiles, and output factors across a wide range of field sizes. Conventional commissioning workflows often require multiple detectors and can be time-intensive, particularly for small-field dosimetry where measurement accuracy is highly sensitive to detector characteristics. This study evaluated the performance of a plastic scintillation detector (BP-PSD) for full water tank commissioning and its potential to improve efficiency while maintaining dosimetric accuracy.

Beam data were acquired for field sizes ranging from 1 × 1 cm² to 40 × 40 cm² using …

Towards Sample-Efficient Deep Reinforcement Learning, Guang Yang

Theses and Dissertations

Deep reinforcement learning (DRL), combining reinforcement learning and high-performance function approximations such as deep neural networks (DNN), is a powerful approach to solving complex sequential decision-making problems. However, due to the complex solution space of the sequential decision-making problems and the inefficient design of the DRL algorithms, DRL algorithms usually require a prohibitively large number of data samples to train effective strategies. Consequently, it is difficult to apply these DRL algorithms to complex real-world problems that require high costs to collect a large volume of data samples. This dissertation proposes new mechanisms to address this sample inefficiency issue, realizing sample-efficient …

An Integrated Data-Driven Framework For Arctic Shipping: Analyzing Vessel Speed, Environmental And Ecological Factors Through Innovative Statistical Spatio-Temporal Methods, Inverse Optimization And Machine Learning, Mauli Pant

Theses and Dissertations

This dissertation develops an integrated data-driven framework to analyze vessel navigation and ecological risk in the United States Arctic from 2010 to 2019. As environmental change and maritime activity increase in the region, understanding how vessels respond to dynamic conditions and how those responses interact with marine ecosystems has become increasingly important. A central theme of this dissertation is the treatment of vessel speed as both an observed outcome and a decision variable reflecting trade- offs among operational, environmental, and ecological factors. The first chapter develops a predictive framework for vessel speed over ground (SOG) using Gaussian Process Boosting (GPBoost), …

Absolute Quantification And Identification Of Rna From Rna-Lipid Nanoparticles Using High Resolution Mass Spectrometry, Jason C. Funderburk

Theses and Dissertations

RNA therapeutics are a rising drug category with potential use for a range of conditions encompassing infectious diseases to therapies for cancer, diseases, and genetic disorders. RNA-lipid nanoparticles (RNA-LNPs) are the prominent delivery method for these therapeutics approved products include mRNA vaccines and polyneuropathy treatments. The emergency use authorizations and orphan drug status of current RNA-LNP drugs has allowed approval without finalization of the regulatory analytical procedures for quality monitoring. The objective of the study was to develop an LC-MS assay to simultaneously measure identity and concentration of two therapeutically relevant intact RNA constructs extracted from RNA-LNPs to enhance quality …

A Mathematical Frameworks For Singular, Nonlinear Phenomena: Applications To Nematocyst Firing And Inhomogeneous Nls With Coulomb Potential, Abdulrahman Alharbi

Theses and Dissertations

Nematocysts are specialized cellular organelles found in all cnidarians, including corals and jellyfish, as well as in some single-celled protists such as dinoflagellates. These organelles display remarkable diversity in morphology and function, playing roles in prey capture and defense. The firing of a nematocyst is one of the fastest accelerations in nature, yet the underlying physical mechanisms remain not fully understood. In this work, we address key questions: how sufficient force is generated to overcome the fluid boundary layer, whether fluid–structure interaction models can reproduce observed dynamics, and what mechanisms trigger discharge.

Our research investigates models based on osmotic pressure …

Lost In The Language: Data Breaches And The Strategic Fog Of Risk Disclosures, Ling Tuo, Shipeng Han

Accounting Faculty Publications

This study examines whether firms strategically adjust the readability of Item 1A (“Risk Factors”) disclosures following data breaches. Using U.S. firm-year observations from 2006 to 2023, we find that data breaches are associated with a significant decline in Item 1A readability. This decline is not accompanied by a meaningful increase in informational content; instead, post-breach disclosures exhibit higher syntactic complexity, more positive tone, and lower textual similarity to prior and industry peers' filings, consistent with strategic obfuscation rather than transparent reporting. The readability decline is amplified among firms facing higher litigation risk but attenuated among firms with stronger reputations for …

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 …

Near Real-Time Adaptive Isotropic And Anisotropic Image-To-Mesh Conversion For Cerebral Aneurysm Simulations, Kevin Garner, Chander Sadasivan, Nikos Chrisochoides

Computer Science Faculty Publications

This paper presents two performance optimization techniques for a mesh adaptation method that is designed to help streamline the discretization of complex vascular geometries within the numerical modeling process. This method is integrated into a pipeline with an image-to-mesh conversion tool to generate adaptive anisotropic meshes from segmented medical images. The pipeline is shown to satisfy quality, fidelity, smoothness, and robustness requirements while providing near real-time performance for medical image-to-mesh conversion. Tested with two brain aneurysm cases and utilizing up to 96 CPU cores within a single, multicore node on Purdue University’s Anvil supercomputer, the parallel adaptive anisotropic meshing method …

Adaptive Self-Attention For Enhanced Segmentation Of Adult Gliomas In Multi-Modal Mri, Evan P. Savaria, Jiangwen Sun

Computer Science Faculty Publications

Every year there are an estimated 80,000–90,000 new glioma cases, highlighting the need for reliable imaging-based decision support. Although deep learning has improved tumor sub-region segmentation, many state-of-the-art models fail to fully capture complementary information across T1, T1Gd, T2, and FLAIR MRI modalities and often operate as “black boxes,” limiting physician trust when precise delineation is critical for surgical planning, radiation targeting, and treatment monitoring. To address these limitations, we propose AIMS, an Adaptive Integrated Multi-Modal Segmentation framework that maintains modality-specific feature streams and employs adaptive self-attention within a hierarchical CNN-Transformer architecture to prioritize and fuse multi-modal MRI features. We …

Sage: Spatially Aware Gene Selection And Dual-View Embedding Fusion For Domain Identification In Spatial Transcriptomics, Yi He, Yunpei Xu, Liqing Ding, Hong-Dong Li, Yaohang Li, Shaokai Wang

Computer Science Faculty Publications

Despite enabling high-resolution mapping of gene expression within tissues, spatial transcriptomics (ST) still faces challenges in accurately segmenting spatial domains due to complex tissue architecture and limitations of current methods. Most approaches rely on local spatial priors, lack gene-level interpretability, and fall short in capturing structure-discriminative genes or long-range functional relationships, limiting their ability to resolve biologically meaningful architectures. We present Spatially Aware Gene selection and dual-view Embedding fusion (SAGE), a unified and reproducible framework for domain identification in spatial transcriptomics that combines topic-driven gene selection with dual-view embedding fusion to address these gaps. SAGE integrates non-negative matrix factorization (NMF)-based …

Micro-Behavioral Analysis Of Online Shopping Patterns For Blind Users, Yash Prakash, Akshay Kolgar Nayak, Nithiya Venkatraman, Sampath Jayarathna, Hae-Na Lee, Vikas Ashok

Computer Science Faculty Publications

While online shopping platforms provide convenience and autonomy to blind users, their non-visual interactions remain underexplored at a micro-behavioral level. Existing studies have primarily emphasized accessibility and usability challenges but have overlooked how fine-grained, screen reader-driven keystroke-level behaviors reflect users’ cognitive strategies. In this paper, we present the findings of a longitudinal study with 25 blind participants to examine their micro-behavioral patterns, using keyboard activity and screen reader logs on both familiar and unfamiliar e-commerce websites. We complemented this study with semi-structured interviews to contextualize the uncovered micro-behavioral patterns. Our results revealed patterns in how blind users draw upon cognitive …

Design And Analysis Of Modern Quantum Neural Network Architectures For Intelligent Systems, Lakshmi Chandrakanth Kasireddy, Prabhakara Rao Kapula, Dineshkumar Rajendran, Neha Bharani, Srikanth Pulipeti, Islombek Khushvaktov

Computer Science Faculty Publications

Quantum neural networks (QNNs) offer a principled pathway for integrating quantum computation with machine learning through superposition- and entanglement-based representations. This chapter proposes an architecture-aware design and evaluation framework for modern QNNs, emphasizing robustness and system feasibility alongside predictive performance. Multiple architectures variational QNNs, quantum convolutional neural networks, tensor-network hybrids, and fully quantum models—are assessed under a unified protocol. Experimental analysis shows that the proposed architecture-search–guided QNN achieves 91.8% classification accuracy and an F1-score of 0.914, outperforming fixed-template variational QNNs by approximately 5.6 percentage points. Under depolarizing noise with probability p = 0.10, the proposed model retains 85.3% accuracy, whereas …

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 …

Quantum Machine Learning Models: Principles, Frameworks, And Computational Challenges, K. A. Jayabalaji, S. Venkata Anand, Dineshkumar Rajendran, Prasanta Chatterjee Biswas, Sardor Omonov, Rubaid Ashfaq

Computer Science Faculty Publications

Quantum machine learning (QML) has become an optimistic avenue of harnessing quantum computation in data-driven modeling, especially of issues with high dimensionality and complicated correlations. Current methods are generally based on fixed or over-parameterized quantum circuits, and hence restricted to scalability as well as unproductive optimization in real-world hardware. This chapter introduces a hybrid quantum-classical learning system that is adaptive and provides principled quantum data encoding, architecture-conscious variational circuit design and resource-optimal optimization. The technique is based on the concepts of quantum architecture search and subspace-preserving transformations to trade expressiveness with trainability, and discretize the quantum model into a classical …

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 …

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 …

Voxvista: Enhancing Screen Reading Experience For Online User Comments, Yash Prakash, Akshay Kolgar Nayak, Mohammed Shoaib Alyaan, Sampath Jayarathna, Hae-Na Lee, Vikas Ashok

Computer Science Faculty Publications

Online discussions have become integral to how people exchange ideas, form opinions, and participate in collective deliberation. While sighted users can comfortably engage with online discussions, blind users who are dependent on screen readers are forced to listen to long threads narrated in a single, monotonic voice that lacks prosodic variation, rhythm, or emotion. This robotic auditory experience not only deteriorates the user engagement with the content but also increases cognitive strain, by making it difficult to remain attentive and discern meaning beyond literal words. In an interview study, most blind participants reported that monotonous narration hindered their ability to …

Modeling Joint Visual Attention In Naturalistic Dyadic Interactions, Kuushini Thennakoon, Yasasi Abeysinghe, Bhanuka Mahanama, Vikas Ashok, Sampath Jayarathna

Computer Science Faculty Publications

Joint visual attention (JVA) provides important insight into how individuals coordinate attention during social interaction. Egocentric eye tracking enables the study of JVA in natural, multi-user settings. This work presents a multi-stage framework to identify and analyze JVA using egocentric video and gaze data. The approach consists of three steps: spatiotemporal tube-based visual similarity, gaze-guided object detection, and attention pattern analysis using the ambient–focal coefficient K. Results show that object-focused collaborative activities exhibit high JVA, with object detection capturing higher joint attention than visual similarity, whereas conversation-based or independent activities show lower and more fragmented joint attention. Analysis of K …

The Privacy Paradox Of Llms: User Perceptions And The Reality Of Pii Leakage, Shuai Cheng, Haitao Xu, Shu Meng, Shuai Hao, Chuan Yue, Zhao Li

Computer Science Faculty Publications

Large language models (LLMs) are increasingly deployed, yet they introduce significant privacy risks by disclosing personally identifiable information (PII) during interactions. Although prior work has demonstrated the feasibility of extracting PII from LLMs, no comprehensive study has evaluated the actual extent of PII leakage across mainstream LLMs or investigated user perceptions, literacy, and behavioral responses to these risks. To address these gaps, we conduct a large-scale evaluation of PII leakage in popular LLMs, demonstrating that attackers can extract email addresses and phone numbers with high success rates. Through a mixed-methods study involving 20 interviews and 204 survey participants, we identify …

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. …

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. …

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 …

Maxgrnet: A Multi-Axis Vision Transformer With Improved Generalization For Eye Disease Classification Using Explainable Ai With Insertion-Deletion Operations On Fundus Images, Md Mehedi Hasan Santo, Fuyad Hasan Bhoyan, Fuad Ibne Jashim Farhad, Fahmid Al Farid, Sovon Chakraborty, Md Humaion Kabir Mehedi, Jia Uddin, Hezerul Bin Abdul Karim

Computer Science Faculty Publications

Eye diseases, including diabetic retinopathy (DR), glaucoma, and cataracts, represent a major global health concern and can lead to severe visual impairment or blindness if not identified in a timely manner. This study proposes a novel eye disease classification framework based on a multi-axis vision transformer (MaxViT) applied to color fundus images with Explainable Artificial Intelligence (XAI) techniques to enhance model transparency. The proposed architecture integrates transformer-based attention mechanisms with Global Response Normalization (GRN)-based multi-layer perceptron (MLP) layers to capture complex spatial and contextual relationships within fundus images effectively. The model was evaluated on a publicly available eye disease classification …

Exploring Large Language Models For Trustworthy Use: Insights From Research And Development, Sandeep Kalari, Sahithi Padidela, Vikas Ashok, Ravi Mukkamala

Computer Science Faculty Publications

Large Language Models (LLMs) are increasingly being adopted in a wide variety of domains, including sensitive domains such as healthcare and finance. However, persistent challenges such as unreliable data sources, privacy breaches, and hallucinated output continue to hinder their usage. We have experimented with several strategies to address these challenges. First, we developed BlockQwen, a blockchain-augmented framework that integrates decentralized trust validation, role-specific access control, and verifiable audit trails into the Qwen 2.5 LLM workflow. Second, we developed PrivAware, a multilayered privacy-enforcement framework, using a fine-tuned Flan-T5 model with self-attention masking, to safeguard data while maintaining high utility. Both systems …

The Shms 11 Gev/C Spectrometer In Hall C At Jefferson Lab, S. Ali, A. Ahmidouch, G. R. Ambrose, A. Asaturyan, C. Ayerbe Gayoso, J. Benesch, V. Berdnikov, H. Bhatt, D. Bhetuwal, D. Biswas, P. Brindza, M. Bukhari, M. Burton, R. Carlini, M. Carmignotto, M. E. Christy, C. Cotton, J. Crafts, D. Day, S. Danagoulian, A. Dittmann, D. H. Dongwi, B. Duran, D. Dutta, R. Ent, H. Fenker, M. Fowler, D. Gaskell, A. Hamdi, N. Heinrich, W. Henry, N. Hlavin, T. Horn, G. M. Huber, Y. Ilieva, J. Jarrell, S. Jia, M. K. Jones, M. Junaid, M. L. Kabir, N. Kalantarians, A. Karki, S. J.D. Kay, C. E. Keppel, V. Kumar, S. Lassiter, W. B. Li, D. Mack, S. Malace, J. Mcmahan, A. Mkrtchyan, H. Mkrtchyan, P. Monaghan, C. Morean, P. Nadel-Turonski, G. Niculescu, M. I. Niculescu, A. Nadeeshani, E. Pooser, A. Ramos, J. Reinhold, B. Sawatzky, H. Szumila-Vance, V. Tadevosyan, R. L. Trotta, A. Usman, C. Yero, M. Yurov, S. Zhamkochyan, S. A. Wood, J. Zhang

Physics Faculty Publications

The Super High Momentum Spectrometer (SHMS) has been built for Hall C at the Thomas Jefferson National Accelerator Facility (Jefferson Lab). With a momentum capability reaching 11 GeV/c, the SHMS provides measurements of charged particles produced in electron-scattering experiments using the maximum available beam energy from the upgraded Jefferson Lab accelerator. The SHMS is an ion-optics magnetic spectrometer comprised of a series of new superconducting magnets which transport charged particles through an array of triggering, tracking, and particle-identification detectors that measure momentum, energy, angle and position in order to allow kinematic reconstruction of the events back to their origin at …

High-Energy Η(′)Π Photoproduction And The Nature Of Exotic Waves, Glòria Montaña, Vincent Mathieu, Vanamali Shastry, Łukasz Bibrzycki, César Fernández-Ramírez, Nadine Hammoud, Robert J. Perry, Alessandro Pilloni, Arkaitz Rodas, Wyatt A. Smith, Adam P. Szczepaniak, Daniel Winney

Physics Faculty Publications

The observation of hybrid mesons in photoproduction experiments can provide essential insight into the inner workings of quantum chromodynamics in the strong coupling regime. In particular, the study of final η^(′)π states is of great interest due to the presence of the lowest lying hybrid candidate with manifestly exotic quantum numbers, the π₁ (1600). In this work, a double-vector exchange model with Reggeized ρ and ω trajectories is developed to describe the photoproduction of η^(′)π in the high-mass region. Results are presented for the differential cross sections and forward-backward asymmetries in the energy region of interest …

Probing Hard/Soft Factorization Via Beam-Spin Asymmetry In Exclusive Pion Electroproduction From The Proton, A. C. Postuma, G. M. Huber, D. J. Gaskell, N. Heinrich, T. Horn, M. Junaid, S. J. D. Kay, V. Kumar, P. Markowitz, J. Roche, R. Trotta, A. Usman, B. -G Yu, T. K. Choi, K. -J. Hong, S. Ali, R. Ambrose, D. Androic, W. Armstrong, A. Bandari, V. Berdnikov, H. Bhatt, D. Bhetuwal, D. Biswas, M. Boer, P. Bosted, E. Brash, A. Camsonne, J. -P. Chen, J. Chen, M. Chen, M. E. Christy, S. Covrig, M. M. Dalton, W. Deconinck, M. Diefenthaler, B. Duran, D. Dutta, M. Elaasar, R. Ent, H. Fenker, E. Fuchey, D. Hamilton, J. -O Hansen, F. Hauenstein, S. Jia, M. K. Jones, S. Joosten, M. L. Kabir, A. Karki, C. Keppel, E. Kinney, N. Lashley-Colthirst, W. B. Li, D. Mack, S. Malace, M. Mccaughan, Z. E. Meziani, R. Michaels, R. Montgomery, M. Muhoza, C. Muñoz Camacho, G. Niculescu, Z. Papandreou, S. Park, E. Pooser, M. Rehfuss, B. Sawatzky, G. R. Smith, H. Szumila-Vance, A. Teymurazyan, H. Voskanyan, B. Wojtsekhowski, S. A. Wood, Z. Ye, C. Yero, J. Zhang, X. Zheng

Physics Faculty Publications

Deep exclusive meson production (DEMP) reactions, such as p(e⃗ , e'π⁺)n, provide opportunities to study the three-dimensional structure of the nucleon through differential cross section and beam-and target spin asymmetry measurements. This work aims to probe the onset of the hard/soft factorization regime through the exclusive p (e⃗, e'π⁺)n reaction, as measured in the KaonLT experiment at Jefferson Lab Hall C. A 10.6 GeV Longitudinally polarized electron beam was incident on an unpolarized liquid hydrogen target, and the scattered electron and produced meson were detected in two magnetic focusing spectrometers, enabling precision cross section measurements. The cross section ratio σ …

Dynamic Modeling Of The Earth's Trapped Proton Environment, Xiaojing Xu, Steve R. Blattnig, Francis F. Badavi, Martha S. Clowdsley, Edward J. Semones

Physics Faculty Publications

Context: Reliable prediction of space radiation exposure is critical for safeguarding spacecraft systems and ensuring astronaut health during missions. Accurate radiation risk assessment for space mission requires advanced models of the Earth’s trapped proton environment. These models must reflect temporal variations driven by geomagnetic field evolution and solar cycle modulation. Existing static models, such as AP8 and IRENE-AP9, are not designed to fully capture these evolving conditions. Aims: This paper presents a dynamic modeling method for the prediction of trapped proton fluxes, which incorporate time-dependent variations due to geomagnetic field evolution and solar cycle fluctuations. Methods: The …

Measuring Short-Range Correlations And Quasi-Elastic Cross Sections In A(E,E') At X > 1 And Modest Q², Y. P. Zhang, Z. H. Ye, D. Nguyen, P. Aguilera, Z. Ahmed, H. Albataineh, K. Allada, B. Anderson, D. Anez, K. Aniol, J. Annand, J. Arrington, T. Averett, H. Baghdasaryan, X. Bai, A. Beck, S. Beck, V. Bellini, F. Benmokhtar, A. Camsonne, C. Chen, J. -P. Chen, K. Chiraptpimol, E. Cisbani, S. Covid Dusa, M. M. Dalton, D. Day, W. Deconinck, M. Defurne, D. Flay, N. Fomin, M. Friend, E. Fuchey, F. Garibaldi, D. Gaskell, S. Gilad, R. Gilman, S. Glamazdin, C. Gu, P. Guèye, C. Hanretty, J. -O Hansen, M. Hashemi Shabestari, D. W. Higinbotham, M. Huang, S. Iqbal, G. Jin, N. Kalantarians, H. Kang, A. Kelleher, I. Korover, J. Lerose, J. Leckey, S. Li, R. Lindgren, E. Long, J. Mammei, P. Markowitz, D. Meekins, R. Michaels, M. Mihovilovič, N. Muangma, C. Munoz Camacho, B. E. Norum, Nuruzzaman, K. Pan, S. Phillips, E. Piasetzky, I. Pomerantz, M. Posik, V. Punjabi, X. Qian, Y. Qiang, X. Qiu, P. E. Reimer, A. Rakhman, S. Riordan, G. Ron, O. Rondon-Aramayo, L. Selvy, A. Shahinyan, R. Shneor, S. Širca, K. Slifer, N. Sparveris, R. Subedi, V. Sulkosky, D. Wang, J. W. Watson, L. B. Weinstein, B. Wojtsekhowski, S. A. Wood, I. Yaron, J. Zhang, Y. W. Zhang, B. Zhao, X. Zheng, P. Zhu, R. Zielinski

Physics Faculty Publications

We present results from the Jefferson Lab E08-014 experiment, investigating short-range correlations (SRC) through measurements of inclusive quasi-elastic scattering from ²H, ³He, ⁴He, ¹²C, ⁴⁰Ca, and ⁴⁸Ca. The kinematics were selected to isolate scattering from SRCs, yielding a plateau in the A/²H cross-section ratios due to the universal two-body structure of the 2N-SRCs in light and heavy nuclei. We observe approximate plateaus in the A/²H ratios and provide the first extractions of the A/²H ratio for ⁴⁰Ca and ⁴⁸Ca. We also examine the A/³He ratio, aiming to identify three-nucleon SRCs (3N-SRCs). Following the approach for isolating 2N-SRCs, searching for 3N-SRC …

Reply To "Comment On "Qcd Factorization With Multihadron Fragmentation Functions"", T. C. Rogers, T. Rainaldi, M. Radici, A. Courtoy

Physics Faculty Publications

We respond to comments [D. Pitonyak, C. Cocuzza, A. Metz, A. Prokudin, and N. Sato, preceding Comment, Phys. Rev. D 113, 038901 (2026)] about our article, [Phys. Rev. D 111, 056001 (2025)]. We stand by our conclusions and defend them against the criticisms.