Digital Commons Network^™

Secondary Teachers' Experiences In International Professional Development For Convergence Research In Stem And Tradition, Rachel Sparks White, Kristie S. Gutierrez, James K. Ferri

Teaching & Learning Faculty Publications

Convergence education promotes learning experiences that integrate science, technology, engineering, and mathematics (STEM) to address complex real-world problems. However, secondary teachers often report limited access to professional development (PD) and curricular resources that support transdisciplinary instruction. This exploratory case study examines how four secondary teachers (three chemistry; one engineering) made sense of a transdisciplinary PD model, Convergence Research in STEM and Tradition (CReST), that leverages cultural heritage artifacts (Renaissance frescoes) as boundary objects to connect chemistry, engineering, world history, and technology. Teachers participated in a four-day immersive PD experience in Firenze (Florence) and Pisa, Italy, that included site-based learning, interaction …

'I Want To Learn Biology And Preserve My Culture' Testing The Efficacy Of A Culturally Relevant And Context Specific Approach, Umar Adam, Musa Adekunle Ayanwale, Hafsat Mustapha, Sakibu Olajide Saibu, Tunde Owolabi, Adekunle Ibrahim Oladejo

Teaching & Learning Faculty Publications

Background

The dominance of Western-inspired teaching methods and instructional materials has contributed to cultural erosion in African schools. In response, increasing attention has been directed toward culturally responsive approaches to decolonising science education. One such approach is the Culturo-Techno-Contextual Approach (CTCA), which enables African students to conceptualize science as culturally embedded knowledge, integrated into their everyday experiences and practically relevant to their lived realities.

Purpose

This study examined the potency of the CTCA in enhancing students' achievement in the biology topic of variation, which is widely regarded as conceptually challenging.

Sample

The study involved 105 Senior Secondary II students from …

Pre-Service Elementary Teachers' Science Content Knowledge And Confidence: Teaching Science Methods With Metacognitive Awareness Activities, Demetrice Smith-Mutegi, Erika Wise

Teaching & Learning Faculty Publications

Few elementary teachers are confident in teaching science or in their science knowledge. This study sought to address this concern by investigating the impact of a science methods course on confidence and science knowledge for pre-service elementary teachers (PSETs). In this study, 23 participants enrolled in a teacher education program at a Midwest US university participated in a quasi-experimental pretest-posttest design. The participants engaged in course content, including metacognitive awareness activities, for a full semester and qualitatively described their confidence within the pre-post assessment survey. Results were analyzed using an independent samples t-test, mean content assessment scores, and paired samples …

Artifact Centered Interdisciplinary Instruction Supports Secondary Student Learning In Chemistry History And Engineering, Rachel Sparks White, Kristie S. Gutierrez, James K. Ferri

Teaching & Learning Faculty Publications

Secondary STEM instruction often remains organized in disciplinary silos, limiting opportunities for students to apply Concepts across domains incoherent, authentic contexts. This mixed methods implementation study examined Convergence Research in STEM and Tradition (CReST), an Artifact centered interdisciplinary curriculum model designed to connect chemistry, world history, and engineering through a shared instructional context. In this implementation, Italian frescoes served as a boundary object anchoring a six-day sequence on mural materials, deterioration, and restoration. The sequence integrated targeted concepts in inorganic chemistry (e.g., the lime cycle and thermal processes), renaissance era historical contacts and the engineering design process within tasks related …

Girls Stem Institute: Impacting Black Girls' Self-Efficacy And Interest In Stem Careers, Crystal Morton, Demetrice Smith-Mutegi, Danka Maric, Vidushi Adlakha, Robin Jackson

Teaching & Learning Faculty Publications

Nationally and globally, STEM serves as a gatekeeper to the labor market and higher education opportunities. Many Black girls find themselves locked out of the gate. Historically and contemporarily, Black girls are seldom viewed as capable or worthy of educational investment and face unique and ongoing challenges in K-12 STEM learning spaces due to their gender and racial identities. Research suggests that informal STEM learning spaces can serve as a counter to Black girls’ experiences in K-12 schools and help enhance Black girls’ self-efficacy and STEM career interests. This study explores the impact of a four-week informal STEM program, employing …

Pre-Service Teachers' Perceptions Of Literacy In Stem Classroom Instruction By Implementation Of Project-Based Lessons, Sarah Ferguson, Maryam Enderson, Jamie Colwell

Teaching & Learning Faculty Publications

This study examines how project-based learning (PBL) supports pre-service STEM teachers (PSTs) in understanding and applying disciplinary literacy in classroom instruction. Conducted in a semester-long PBL course at a mid-sized public university, the research explores PSTs’ shifting perceptions of literacy in STEM and the challenges and successes they experience. Using qualitative methods grounded in student voice and case study frameworks, data were collected through focus groups, reflections, and lesson plan analyses. Findings show PSTs initially held narrow views of literacy but developed more nuanced understandings through PBL. Key successes included increased confidence and use of inquiry-based strategies; challenges involved time …

Supporting Teacher Candidates' Conceptions Of Equitable Mathematics Teaching: Using Exposure And Experience In Elementary Mathematics Methods Courses, Amanda Mohammad Mirzaei, Ethan P. Smith

Teaching & Learning Faculty Publications

Elementary mathematics methods courses may provide a lever to help support teacher candidates in their trajectory to becoming equitable mathematics teachers. To support the rising generation of mathematics teachers, mathematics teacher educators (MTEs) must critically analyze approaches to equity within and across teacher preparation programs. This study investigates an elementary mathematics course centering equitable mathematics teaching and learning (EMT), rather than isolating or using it as a supplementary aspect. We consider a framework of the teacher learning trajectory (Turner et al., 2012) where teacher candidates learn initial practices, make connections, and then incorporate these practices into their teaching. We extend …

Generative Ai-Driven Optimization In Flexible And Reconfigurable Manufacturing Systems, Salah Hammedi, Hicham Chaoui, Lotfi Nabli

Electrical & Computer Engineering Faculty Publications

Flexible and Reconfigurable Manufacturing Systems (FRMSs) are essential for coping with variability in modern production environments; however, efficient scheduling and rapid reconfiguration remain challenging. This paper presents a hybrid optimization framework that integrates Colored Petri Net (CPN) modeling with Generative Artificial Intelligence (GenAI) to enhance scheduling performance and system adaptability. The CPN formalism ensures verifiable modeling of system dynamics, while a transformer-based generative model produces candidate scheduling and reconfiguration strategies. Simulation experiments were conducted under static, dynamic, and adaptive scenarios, including machine breakdowns and dynamic job arrivals. Performance was evaluated using makespan, mean flow time, machine utilization, and reconfiguration latency. …

The Principle Of Tolerance And Logical Pluralism, Teresa Kouri Kissel

Philosophy Faculty Publications

Logical pluralism is the view that there is more than one correct logic. This is in contrast with logical monism, which holds that only one is, and logical nihilism, which holds that none are. Monists are typically folks who defend a single logic, like Michael Dummett (2000) or Alan Ross Anderson and Nuel D. Belnap (1975), as correct for guiding deductive reasoning.

Mg-Spair: Multi-Grade Sparse-Guided Implicit Representation For Training-Data-Free Image Restoration, Jianmin Liao, Lei Huang, Ronglong Fang, Ashley Prater-Bennette, Lixin Shen, Yuesheng Xu

Mathematics & Statistics Faculty Publications

MG-SpaIR is a training-data-free framework for restoring a clean image from a single observation corrupted by a mixture of blur, downsampling, noise, and missing pixels. Building on implicit neural representations (INRs), we introduce a multi-grade residual hierarchy that progressively refines the reconstruction from low to high spatial frequencies across grades, improving representational fidelity and mitigating spectral limitations. To stabilize reconstruction optimization and suppress INR-induced artifacts, we further propose an explicit sparse proximal regularization (e.g., ℓ0 type) applied directly in the high-resolution image domain, which discourages spurious high-frequency patterns while preserving sharp structures. The resulting optimization is solved efficiently via a …

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 …

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 …

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 …

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 …

Variational Autoencoder Inverse Mapper For Extraction Of Compton Form Factors: Benchmarks And Conditional Learning, Douglas Adams, Md Fayaz Bin Hossen, Joshua Bautista, Gia-Wei Chern, Simonetta Liuti, Marie Boër, Marija Čuić, Michael Engelhardt, Gary R. Goldstein, Huey-Wen Lin, Yaohang Li

Computer Science Faculty Publications

Deeply virtual exclusive scattering processes (DVES) serve as precise probes of nucleon quark and gluon distributions in coordinate space. These distributions are derived from generalized parton distributions (GPDs) via Fourier transform relative to proton momentum transfer. QCD factorization theorems enable DVES to be parameterized by Compton form factors (CFFs), which are convolutions of GPDs with perturbatively calculable kernels. Accurate extraction of CFFs from DVCS, benefiting from interference with the Bethe–Heitler (BH) process and a simpler final state structure, is essential for inferring GPDs. This paper focuses on extracting CFFs from DVCS data using a variational autoencoder inverse mapper (VAIM) and …

Lost In Instructions: Study Of Blind Users' Experiences With Diy Manuals And Ai-Rewritten Instructions For Assembly, Operation, And Troubleshooting Of Tangible Products, Monalika Padma Reddy, Aruna Balasubramanian, Jiawei Zhou, Xiaojun Bi, Iv Ramakrishnan, Vikas Ashok

Computer Science Faculty Publications

AI tools like ChatGPT and Be-My-AI are increasingly being used by blind individuals. Although prior work has explored their use in some Do-It-Yourself (DIY) tasks by blind individuals, little is known about how they use these tools and the available product-manual resources to assemble, operate, and troubleshoot physical/tangible products – tasks requiring spatial reasoning, structural understanding, and precise execution. We address this knowledge gap via an interview study and a usability study with blind participants, investigating how they leverage AI tools and product manuals for DIY tasks with physical products. Findings show that manuals are essential resources, but product-manual instructions …

Susceptibility To High-Fidelity Misinformation: An Eye-Tracking Analysis, Yasasi Abeysinghe, Gavindya Jayawardena, Enkelejda Kasneci, Sampath Jayarathna

Computer Science Faculty Publications

With the rise of online misinformation and AI-generated text, understanding human perception of news truthfulness is critical. In this study, we examine visual attention and cognitive processing using eye-tracking measures as individuals read fake and real news articles sharing nearly identical structure and imagery, differing only in subtle textual changes. Using the public FakeNewsPerception dataset, we analyze advanced gaze measures, including scanpaths, AOI transitions, and luminance-corrected pupil measures, beyond basic gaze features, in relation to news truthfulness and perceived believability. Results show that, given the high fidelity of the fake news, readers exhibited comparable visual scanning patterns, attention allocation across …

Attf-Gnn: An Attention-Based Multi-Omics Graph Neural Network With Modality Learning For Disease Subtyping, Sovon Chakraborty, Eleni Adam, Terry Stilwell, Harold Riethman, Desh Ranjan, Pratip Rana

Computer Science Faculty Publications

We propose AttF-GNN, an attention-based graph fusion strategy for diseases classification and subtyping. In multiomics analysis, not all types of molecular data are equally relevant for disease subtyping and considering all modalities equally may obscure discriminative signals and limit the effectiveness of predictive models by overlooking modality-specific contributions. Therefore, we design an attention-based multimodal GraphSAGE framework that can automatically emphasize the modalities providing the most relevant information for classification. At first, we have constructed three graphs using mRNA, RNA-seq and DNA methylation modalities, and train each omics with individual GraphSAGE encoders. Next, a unified intersection graph is formed using an …

Larval Crowding In Culex Pipiens Generates Cumulative Cascading Effects Under Changing Environmental Conditions, Sengul Talay, Zafer Sakaci, Michele C. Weigle, Bugrahan Regaip Kilinc, Jenah Parman, Holly Gaff, Deniz Sirin, Bulent Alten, Dennis Bente, Sirri Kar

Computer Science Faculty Publications

Density-dependent population regulation is a fundamental driver in the population dynamics of living systems, ensuring a balanced and sustainable maintenance in nature. It has been suggested that the adverse developmental and morphological effect of high larval density in container mosquitoes represents a significant limiting factor for population density, and the need to investigate this possible importance within the framework of field-based principles has been emphasized. This semi-field study, conducted under the natural thermal regime and using a natural population in Turkish Thrace, aimed to examine the effects of non-food-limiting larval density (crowding effect) in the mosquito species, Culex pipiens ( …

Distributed Semi-Speculative Parallel Anisotropic Mesh Adaptation, Kevin Garner, Polykarpos Thomadakis, Nikos Chrisochoides

Computer Science Faculty Publications

This paper presents a distributed memory method for anisotropic mesh adaptation that is designed to avoid the use of collective communication and global synchronization techniques. In the presented method, meshing functionality is separated from performance aspects by utilizing a separate entity for each - a multicore cc-NUMA-based (shared memory) mesh generation software and a parallel runtime system that is designed to help applications leverage the concurrency offered by emerging high-performance computing (HPC) architectures. First, an initial mesh is decomposed and its interface elements (subdomain boundaries) are adapted on a single multicore node (shared memory). Subdomains are then distributed among the …

Attention-Based Multi-Omics Fusion For Drug Synergy Prediction, Kusal Debnath, Pratip Rana, Preetam Ghosh

Computer Science Faculty Publications

Drug combination therapy in disease management gained popularity in the last few decades. Computational modeling of such combinations is an active area of research in the drug discovery domain. While earlier approaches solely emphasized on the structural features of participating drugs for designing synergistic models, they lack other crucial factors directly linked with drug administration - omics expressions. As differential omics expression is a downstream consequence of the administered drug combinations, utilizing such expressions while designing synergistic models promises robust and dynamic modeling. In this work, we propose SynergyLM that fuses multi-omics features with drug embeddings to build an omics-aware …

Professional Identity Exploration And Commitment Development By Stem Undergraduates From Underrepresented Groups During Near-Peer Mentoring, Joseph A. Brobst, Joanna K. Garner, David Hartenstine, Perry Fizzano

Center for Educational Partnerships Publications

This study examines near-peer mentoring as a context for professional identity development by undergraduates from groups that are underrepresented in STEM higher education. Mentees were students in a computer science/mathematics‑focused scholarship program, with a two-tiered near-peer mentoring component that paired 1st-2nd‑year undergraduate mentees with 3^rd-4^th year mentors and 3^rd-4^th year mentees with early‑career professional mentors. We analyzed mentoring conversation summaries from three mentor–mentee pairs, spanning one academic year. Mentoring relationships provided safe spaces for students’ professional identity exploration and the development of commitments toward career paths and life goals. Across all cases, conversation summaries …

Advancing Food Equity Through Explainable Ai (Xai): Identifying Place-Based Factors And Conditions Of Food Security, Leslie Hoglund, Hyoshin Park

Health Behavior, Policy & Management Faculty Publications

Food behaviors, food security, and their association with socioeconomic factors constitute a critical area of study with implications for public health, economic stability, and social equity. Understanding these relationships are essential for developing effective policies and interventions that promote sustainable, healthy food systems and greater food equity. This paper employs explainable artificial intelligence (XAI) methods to identify key features influencing household food behaviors. The insights gained from the XAI analysis are further utilized in conjunction with inverse reinforcement learning (IRL) to examine expert behaviors related to eating habits satisfaction. The XAI results reveal that household health conditions, spending patterns, and …

Machine Learning: Thematic Feature Grouping, And The Magnificent Seven: A Forecasting Analysis, Mirarmia Jalali, Mohammad Najand, Andrew Cohen

Finance Faculty Publications

This study examines the predictability of monthly excess returns for the “Magnificent Seven” U.S. technology firms using machine learning and economically motivated thematic feature grouping. Framed as a focused study of the most systemically consequential equity panel in modern markets—seven firms representing over 30% of the S&P 500—the analysis confronts a small-N, large-P environment where economically structured dimensionality reduction is essential. Using 154 firm-level characteristics categorized into 13 economic themes, we evaluate linear, penalized, tree-based, and neural network models in a small-N, large-P setting. Unrestricted models suffer substantial overfitting and fail to outperform the historical average benchmark out-of-sample. In contrast, …

Natural Resource Rents And Energy Poverty Nexus In Next Eleven Economies, Muhammad Salah Uddin, Ayub Ali, Zobayer Ahmed, Md Nasir Uddin Sikdar, Ahsan Habib, Abdullah Elah Al-Mahde

Finance Faculty Publications

Energy poverty (EP) remains a persistent global challenge with important implications for economic development, public health, and social welfare. While natural resources, particularly oil and gas, are often viewed as key sources of energy access, their effectiveness in mitigating EP remains underexplored in emerging economies. This study examines the relationship between natural resource rents (NRR), specifically oil rents (OR) and natural gas rents (NGR), and energy poverty (EP) in the Next Eleven (N-11) countries from 2000 to 2020. The primary objective is to assess how NRR influences EP at various levels of poverty using the Method of Moments Quantile Regression …

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 …