Digital Commons Network^™

Operational Solutions To Address Radiology Staffing Challenges In Southern California Hospitals, Jacqueline Wood

Walden Dissertations and Doctoral Studies

The problem of interest is the current workforce crisis among the Radiologist Specialty in hospitals in Southern California. There has been a significant decline in the number of Radiologists in the workforce compared to the caseload of imaging studies. The purpose of this integrative review was to evaluate empirical and non-empirical literature for operational solutions to improve recruitment and retention of radiology providers. Literature published between 1999-2024 were evaluated for appropriateness using the Johns Hopkins evidence appraisal tools. The literature involved strategies and identified operational areas of opportunity for improvement with radiologist staffing. Identifying ways to improve job satisfaction, retention …

Fitisemanu V. United States: An Analysis Of The Impact Of The Supreme Court’S Denial Of Certiorari And The Future Citizenship (Or Lack Thereof) For American Samoans, Moore, Michelle

Santa Clara Law Review

Does the Citizenship Clause of the Fourteenth Amendment confer United States citizenship on individuals born in American Samoa? How does one reconcile the competing values of preserving a unique culture and receiving the full protections of the U.S. Constitution? These were the issues brought before the court in Fitisemanu v. U.S.

American Samoa is the only U.S. territory where individuals born there are born as U.S. nationals, rather than U.S. citizens. While being born a U.S. national has certain rights and privileges not provided to noncitizens and nonnationals, the idea of distinguishing between a U.S. citizen and a U.S. national …

Does Bubble Nucleation Occur Heterogeneously In Magmas Feeding Explosive Rhyolite Eruptions? Insights From The Rock Magnetic Properties Of Pumice, Kelly N. Mccartney, Julia E. Hammer, Thomas Shea, Stefanie Brachfeld, Thomas Giacheetti, Bruce F. Houghton

Department of Earth and Environmental Studies Faculty Scholarship and Creative Works

Nanometer-scale titanomagnetite crystals have been detected in nominally aphyric rhyolite pumice, but whether they are numerous enough to impact bubble nucleation in explosive silicic volcanism was unresolved. This study examines sub-micron crystals using rock magnetic techniques, Rhyolite-MELTS modeling, and physical characterization. We analyzed pumice from four eruptions spanning wide ranges in intensity, storage depth, and bubble number density (1016 to 1013 m−3 liquid): 1060 CE Glass Mountain, 1912 CE Novarupta, 232 CE Taupo, and 0.45 Ma Pudahuel. Calculations assuming monospecific assemblages of 10 and 1,000 nm cubic particles yield titanomagnetite number densities of 1021 to 1013 m−3 dense rock equivalent, …

Tracing Priming Effects In Palsa Peat Carbon Dynamics Using A Stable Isotope-Assisted Metabolomics Approach, Christian Ayala-Ortiz, Moira Hough, Elizabeth K. Eder, David W. Hoyt, Rosalie K. Chu, Jason Toyoda, Steven J. Blazewicz, Patrick M. Crill, Ruth Varner, Scott R. Saleska, Virginia I. Rich, Malak M. Tfaily

Michigan Tech Publications

Introduction: Peatlands store up to a third of global soil carbon, and in high latitudes their litter inputs are increasing and changing in composition under climate change. Although litter significantly influences peatland carbon and nutrient dynamics by changing the overall lability of peatland organic matter, the physicochemical mechanisms of this impact—and thus its full scope—remain poorly understood. Methods: We applied multimodal metabolomics (UPLC-HRMS, ¹H NMR) paired with ¹³C Stable Isotope-Assisted Metabolomics (SIAM) to track litter carbon and its potential priming effects on both existing soil organic matter and carbon gas emissions. Through this approach, we achieved molecule-specific tracking …

Examining The Relationship Between Artificial Intelligence And Disability, Luke Abell, Ben Anderson, Andres Arbulu, Cailyn Bowerman, Ethan Brown, Logan Busch, Eliana Chinitz, Shaan Choksi, Charles Clark, Alexander Claure, Meaghan Crites, Amalia Daniell, Stephen Dombrowski, Clara Dotterer, Jordyn Fink, Jake Fox, Akanksha Garg, Ishaan Ghosh, Cristian Gorayeb, Lia Gosselin, Simon Hasik, Alexis Hawkins, Charles Dickerson Iv, Chistopher Johnson, Cj Kulprathipanja, Claire Lindborg, Josh Lorenz, Luke Mcdonald, Daniella Nahnsen, Arwa Neemuchwala, Sage Nichols, Henrique Ortega, Krish Patel, Daniel Paul, Andres Ravelo, Daniel Reyes, Francesca Rovetto, Santiago Sacchini, Juan Santos, Mauricio Saturno, Carter Schaffer, Joey Schlosser, Anna Scroggings, Dylan Smith, Daniel Tejeira, Kenny Teng, Diya Tibrewala, Max Young, Robert Zeltsman, Bradley S. Duerstock, Maxwell Mensah

Undergraduate Coursework

No abstract provided.

Decomposing Trends In The Gender Gap For Highly Educated Workers, Joseph G. Altonji, John Eric Humphries, Yagmur Yuksel, Ling Zhong

Cowles Foundation Discussion Papers

This paper examines the gender gap in log earnings among full-time, college-educated workers born between 1931 and 1984. Using data from the National Survey of College Graduates and other sources, we decompose the gender earnings gap across birth cohorts into three components: (i) gender differences in the relative returns to undergraduate and graduate fields, (ii) gender-specific trends in undergraduate field, graduate degree attainment, and graduate field, and (iii) a cohortspecific “residual component” that shifts the gender gap uniformly across all college graduates. We have three main findings. First, when holding the relative returns to fields constant, changes in fields of …

Statistical Analysis Of Multi-Endpoint Phenotypic Screening Increases Sensitivity Of Planarian Neurotoxicity Testing, Danielle Ireland, L. J. Word, Eva-Maria S. Collins

Biology Faculty Works

There is an urgent need for high-throughput screening (HTS) models to replace, refine, and/or reduce (“3Rs”) vertebrate toxicity testing. Replacing in vivo animal studies is challenging for neurotoxicity and developmental neurotoxicity (DNT), where the functional relevancy of adverse outcomes needs to be assessed on the whole organism. We previously screened the NTP 87-compound library (NTP87), consisting of known and suspected developmental neurotoxicants, and showed that planarian HTS can identify known (developmental) neurotoxicants. Because analysis methods can impact screening results, and our original analysis used the lowest-observed-effect-level (LOEL) only, we hypothesized that the use of state-of-the-art statistical analysis would increase the …

August 21, 2025, James Madison University

The Breeze, 2020-

The Breeze is the student newspaper of James Madison University in Harrisonburg, Virginia.

Prefrontal Cortex Astrocytes Modulate Distinct Neuronal Populations To Control Anxiety-Like Behavior, Eunyoung Kim, Hairuo Du, Yanqi Tan, Brandon L Brown, Yaowen Chang, Blanca Díaz-Castro, Jonathan V Sweedler, Xinzhu Yu

Faculty, Staff and Student Publications

Accumulating evidence has supported diverse regulatory functions of astrocytes in different neural circuits as well as various aspects of complex behaviors. However, little is known about how astrocytes regulate different neuronal subpopulations that are linked to specific behavioral aspects within a single brain region. Here, we show that astrocytes in the medial prefrontal cortex (mPFC) encode anxiogenic environmental cues in freely behaving mice. Silencing mPFC astrocyte Ca

Large-Scale Flood Detection And Mapping In The Yangtze River Basin (2016–2021) Using Convolutional Neural Networks With Sentinel-1 Sar Images, Xuan Wu, Zhijie Zhang, Wanchang Zhang, Bangsheng An, Zhenghao Li, Rui Li, Qunli Chen

Environment and Society Faculty Publications

Synthetic Aperture Radar (SAR) technology offers unparalleled advantages by delivering high-quality images under all-weather conditions, enabling effective flood monitoring. This capability provides massive remote sensing data for flood mapping, while recent rapid advances in deep learning (DL) offer methodologies for large-scale flood mapping. However, the full potential of deep learning in large-scale flood monitoring utilizing remote sensing data remains largely untapped, necessitating further exploration of both data and methodologies. This paper presents an innovative approach that harnesses convolutional neural networks (CNNs) with Sentinel-1 SAR images for large-scale inundation detection and dynamic flood monitoring in the Yangtze River Basin (YRB). An …

Full-Stack Quantum Computing, Benjamin C A Morrison

Physics & Astronomy ETDs

Quantum computing is a promising tool for solving computational problems in several areas, including the simulation of physical and chemical systems. The design of practical quantum computing systems for these applications is a daunting task, requiring collaborative work by interdisciplinary teams considering different abstractions of the same systems. This dissertation presents work on the development of those abstractions, and on components that bridge multiple layers of abstraction. I first provide an introduction to quantum circuit model computation, error correction with stabilizer codes, and physics simulation algorithms. In joint work with the QSCOUT software team, I then describe the development of …

Molecular Identification Of Acinetobacter Baumannii Isolated From Clinical Samples In Iraq By Using Multiplex Pcr, Yaseen Ismael Mamoori, Ibrahim Abdulla Ahmed, May Ridha Jaafer, Ayat Muneam Ali, Sabreen Ali Hadi

Baghdad Science Journal

Acinetobacter baumannii is one of the most clinically significant pathogens that cause severe nosocomial infections. Rapid identification of this bacterial species is essential so that proper treatment can be provided and outbreaks can be controlled. In this study, a multiplex PCR was used to identify A. baumannii. Twenty clinical isolates of A. baumannii and three each of Gram+ve (Staphylococcus aureus, Bacillus licheniformis, Micrococcus yunnanensis) and Gram−ve (Proteus mirabilis, Pseudomonas aeruginosa, Klebsiella pneumoniae) bacterial isolates were subjected to singleplex and multiplex PCR reactions using three sets of species-specific primers (P-Ab-ITS-F, P-Ab-ITS-R; …

Autonomous Driving Of A Guided Emergency Service Robot With Route Tracking Control, Saif Talib Abbas Albasrawi, Lamyaa Sabeeh Ashour Al-Lami

Baghdad Science Journal

This work presents a method for controlling the travel routes that tracked robots use to get around their environments. The algorithm utilized in model-predictive control could be implemented in a vehicle to make the vehicle capable of driving itself if an emergency arises. It is anticipated that soon, collaboration between edge clouds will become more commonly used to connect and control various pieces of rescue equipment. The Linear Model-Predictive Control algorithm, also known as LMPC, is an excellent choice for controlling the path that the rescue robots will take because it makes efficient use of the limited computational resources available …

Re: Butte Priority Soils Operable Unit (Bpsou) Draft Final Insufficiently Reclaimed Sites Field Sampling Plan (Fsp): Bres No. 26 – Cripple Dump, Mike Mcanulty

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Re: Butte Priority Soils Operable Unit (Bpsou) Draft Final Insufficiently Reclaimed Sites Field Sampling And Investigation Plan (Fsp): Bres No. 177 – North Alice Culvert, Mike Mcanulty

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Tort Liability For Failure To Age Gate: A Promising Regulatory Response To Digital Public Health Hazards, Matthew B. Lawrence, Brett Frischmann, Avi Sholkoff

Faculty Publications

Tort liability for failure to “age gate” is a promising legal response to the public health hazards of AI, social media, sports gambling, and other digital spaces. Tort liability for failure to “age gate” hinges liability for harms to minors on an app’s failure to take reasonable steps to prevent minors from gaining access or otherwise to apply appropriate governance rules, such as privacy-protective default settings or ensuring genuine parental consent. While no one legal response is a panacea, tort liability for failure to age gate carries several distinctive advantages that make it a particularly promising option at this stage …

Organizational Climate And Decision Aid Sustainability In Lupus Care: Mixed Methods Study, Aizhan Karabukayeva, Larry R Hearld, Nathan W Carroll, Reena Kelly, Jasvinder A Singh

Faculty, Staff and Students Publications

Background: Digital decision aids (DAs) are increasingly used in health care to support shared decision-making and promote patient engagement. In the context of systemic lupus erythematosus (SLE), a complex autoimmune disease characterized by diverse symptoms and uncertain prognoses, DAs offer guidance to patients in navigating treatment risks and benefits. Although numerous studies have examined the initial implementation of evidence-based tools, there is limited evidence on the organizational factors that influence their long-term sustainability in clinical practice. This gap is particularly salient for digital interventions, where integration into routine workflows and ongoing use require alignment with clinic readiness and culture. This …

Tense And Agreement Measures In Young African American English-Speaking Children, Kwaku Nyantakyi

LSU Doctoral Dissertations

The Tense Marker Total (TMT) and the Tense and Agreement Productivity (TAP) score are two ways of measuring children’s tense and agreement systems, and studies have shown that both measures track developmental growth across different ages in General American English (GAE)-speaking children. This dissertation examined the feasibility and validity of using these measures for children learning African American English (AAE).

The data were cross-sectional archival language samples from 38 AAE-speaking children who ranged in age from three to five years (11 at 3 years, 12 at 4 years, 15 at 5 years). The language samples were coded and scored for …

Deep Learning For The Early Detection Of Invasive Ductal Carcinoma In Histopathological Images: Convolutional Neural Network Approach With Transfer Learning, Naga Shreya Chilumukuru, Pragya Priyadarshini, Yawo Ezunkpe

Faculty Research, Scholarly, and Creative Activity

Background: Invasive ductal carcinoma (IDC) is considered the most common form of breast cancer, accounting for a significant percentage of mortality worldwide. Therefore, its early detection is vital to further improve patients’ outcomes and survival rates. However, conventional diagnostic methods in the form of manual histopathological examinations are time-consuming, subjective, and prone to errors. Therefore, there is an urgent need to develop automated solutions for accurate IDC detection in histopathology images to assist pathologists in clinical decision-making. Objective: We aim to develop and validate a convolutional neural network (CNN) model for early detection of IDC by analyzing histopathological images. The …

Evaluating Snapshotnir For Tissue Oxygenation Measurement Across Skin Types After Mastectomy, Saif Badran, Sara Saffari, William R Moritz, Gary B Skolnick, Amanda M Westman, Mitchell A Pet, Justin M Sacks

2020-Current year OA Pubs

Accurate monitoring of mastectomy skin flap (MSF) perfusion is critical, especially in patients with darker skin pigmentation at higher risk of misdiagnosed tissue ischemia. Near-infrared spectroscopy (NIRS) devices, such as SnapshotNIR, offer real-time tissue oxygen saturation measurements (StO

Scorpions Of The Horn Of Africa (Arachnida: Scorpiones). Part Xliii. Lanzatus Fuscipalpis Sp. N. From Ethiopia (Buthidae), František Kovařík, Graeme Lowe

Euscorpius

The rare Horn of Africa buthid genus Lanzatus Kovařík, 2001, known only from Somalia and Somaliland, is recorded for the first time from Ethiopia. We describe a new species Lanzatus fuscipalpis sp. n. fully illustrated with color photographs showing its morphology, habitus, hemispermatophore, and collection sites.

Real-Time Feedback On User-Written Generative Artificial Intelligence Model Prompts, Gulmohar Khan, Ramprasad Sedouram, Karthik Srinivas

Defensive Publications Series

A common user interface for consumer use of generative artificial intelligence (genAI) tools is a chat interface, where a user can input their request as a text prompt within an input field and where the genAI model generates a response that answers the prompt. When users are insufficiently skilled at prompting, they may require multiple interactions with the genAI tool before a satisfactory result is generated. There are no mechanisms that guide the users to provide a prompt that uses good prompting strategies to express their intent. Also, user-provided prompts may include errors, gaps, or other issues that impact the …

Sensemaking Opportunities For Students Experiencing Difficulty: A Mixed Methods Study, Rachel Juergensen, Delinda Van Garderen

Journal of Science Education for Students with Disabilities

All students deserve to be engaged in high-quality science instruction that moves beyond memorization and recall of facts and offers opportunities for sensemaking. Science achievement is alarmingly low signifying there is a large percentage of students experiencing difficulty in science. The low science achievement may be symptomatic of gaps in opportunities for sensemaking within the science classroom. Using an explanatory sequential mixed methods design, the current study explored the relationship between middle school science teacher beliefs and opportunities students experiencing difficulty in science had to participate in sensemaking discussions. Integrated findings from the study suggest that teachers’ beliefs influenced the …

Tax Rule Validation Using Large Language Models, Sandeep Chandna, Misha Narinsky

Defensive Publications Series

The process of mapping products to jurisdiction-specific tax categories and validating rates against evolving legislation can involve complex, manual effort. A system is described that may leverage Large Language Models (LLMs) to assist with these tasks. The system can be configured to perform functions such as interpreting natural-language text from tax laws to classify products from an internal catalog. Additionally, the system can source and ingest public tax data to compare a company's internal tax rule configurations against external information sources. By automating the cross-referencing of internal product data with external legal and rate information, such a system can facilitate …

The Innovative Technique For Obtaining The Solutions Of Jeffery Hamel Nano-Fluids Flow Problem, Haedir Abd Alrazak Namoos, Abeer Majeed Jasim

Iraqi Journal for Computer Science and Mathematics

The study of heat transfer in nanofluid flows is increasingly important in many engineering, medical, and industrial applications. These fluids offer enhanced thermal cooling properties compared to conventional fluids. The research problem lies in the challenges of solving the Jeffrey-Hammel flow model for nanofluids, which includes coupled nonlinear differential equations that describe the thermal and hydrodynamic behavior of this type of flow, taking into account the influence of multiple factors such as the type, size, and concentration of nanoparticles. This research aims to propose a new hybrid analytical technique that combines the Laplace transform and the q-homotopy analysis technique with …

Optimized Hybrid Watermarking: Dual-Scheme Strategies For Enhanced Robustness, Ooi Jessie, Liew Siau Chuin, Syifak Izhar Bt Hisham, Khor Hui Liang, Khoo Bee Ee, Jasni Mohamad Zain

Iraqi Journal for Computer Science and Mathematics

Digital watermarking is crucial in content identification and copyright protection, particularly multimedia and medical imaging. This paper introduces two novel hybrid watermarking methods, Entropy-Guided Singular Embedding (EGSE) and Entropy-Guided Hybrid Embedding (EGHE), that improve upon existing techniques by integrating entropy-based adaptive block selection with Particle Swarm Optimization (PSO) for dynamic embedding strength determination. Unlike traditional methods, which rely on fixed embedding regions or manual parameter tuning, the proposed approaches automatically identify high-entropy regions to embed watermark signals, ensuring stronger resistance to distortion while maintaining image quality. EGSE employs Integer Wavelet Transform (IWT) and Singular Value Decomposition (SVD), whereas EGHE enhances …

Retracted: Efficient Multi-User Computation Offloading And Reducing Latency In Mobile-Edge Computing For Iot Applications, Sarmad T. Abdul-Samad, Osamah Al-Hwaidi, Ali Abd Al-Rasool Muslem

Iraqi Journal for Computer Science and Mathematics

Mobile Edge Computing (MEC) is an inventive paradigm for computing that has the potential to notably diminish latency and energy consumption by transferring computationally demanding jobs to edge clouds near intelligent mobile users. This investigation aims to reduce offloading and latency between multiple users and edge computing in the context of Internet of Things (IoT) applications in the fifth generation (5G) by utilizing an optimization algorithm called the Bald Eagle Search Optimization Algorithm. Although employing deep learning methods might increase time consumption and computational complexity, an edge computing system enables devices to transfer their demanding jobs to edge servers, decreasing …

Retracted: Iot Flow Parameters Classification Based On Machine Learning Techniques, El-Sayed M. El-Kenawy, Marwa M. Eid, Ban Salman Shukur, Amel Ali Alhussan, Doaa Sami Khafaga

Iraqi Journal for Computer Science and Mathematics

In recent years, there has been a highly remarkable convergence of artificial intelligence (AI) and the Internet of Things (IoT), which has made rapid progress in smart city initiatives by developing smart devices for such cities. Since these devices are increasingly diversified, they require a resilient communication network to demonstrate high performance in managing consistent traffic flows. A machine learning model intended for identifying network parameters from diverse devices, in addition to proposing modifications meant for network performance enhancement, is developed in this study. In relation to packet data as a network traffic parameter, employing gateway devices can facilitate its …

Retracted: Metaguard: A Federated Learning Approach To Hybrid Xgboost And Meta-Learning Models For Proactive Cyber Threat Hunting, Shatha H. Jafer Al-Khalisy, Ghada Al-Kateb

Iraqi Journal for Computer Science and Mathematics

In an increasingly interconnected world, cybersecurity threats have become more sophisticated, necessitating advanced, scalable, and privacy-preserving solutions. MetaGuard emerges as a novel framework that integrates federated learning with hybrid machine learning models, specifically XGBoost and meta-learning, to enhance proactive cyber threat detection. This framework offers a robust, distributed approach to cybersecurity, ensuring high detection accuracy while preserving user privacy through the implementation of differential privacy and homomorphic encryption. MetaGuard leverages distributed nodes to collaboratively train a global model, enabling rapid adaptation to new threats without the need for centralized data aggregation. Experimental evaluations using the CYBER-2024 dataset demonstrate that MetaGuard …

Ineffective Parenting, Alexis Rian

Master of Arts in Human Services

Ineffective parenting can lead to long-term, severe, and lasting consequences for children. Children depend heavily on their primary caregivers for safety and to have their basic needs met. However, not all parents demonstrate positive parenting styles, whether due to a lack of knowledge, limited capacity, or simply a lack of interest. Parenting styles fall into four categories: authoritarian, authoritative, permissive, and uninvolved. The type of parenting style a caregiver adopts can significantly influence the attachment style a child has with them. Four attachment styles that children have are insecure avoidant attachment, secure attachment, insecure resistant attachment, and disorganized attachment. Children …