Digital Commons Network^™

Association Of Glp-1 Receptor Agonists And Hepatocellular Carcinoma Incidence And Hepatic Decompensation In Patients With Type 2 Diabetes, Lindsey Wang, Nathan A. Berger, David C. Kaelber, Rong Xu

Faculty Scholarship

Background & aims: Hepatocellular carcinoma (HCC) is a leading cause of cancer death. HCC is preventable with about 70% of HCC attributable to modifiable risk factors. Glucagon-like peptide-1 receptor agonists (GLP-1RAs), Food and Drug Administration–approved medications for treating type 2 diabetes mellitus (T2DM), have pleiotropic effects on counteracting risk factors for HCC. Here we evaluate the association of GLP-1RAs with incident HCC risk in a real-world population. Methods: This retrospective cohort included 1,890,020 patients with a diagnosis of T2DM who were prescribed GLP-1RAs or other non-GLP-1RA anti-diabetes medications and had no prior diagnosis of HCC. Incident (first-time) diagnosis of HCC …

Bridging Disciplines With Ai-Powered Coding: Empowering Non-Stem Students To Build Advanced Apis In The Humanities, Daniel Plate, James Hutson

Faculty Scholarship

The integration of AI-powered coding assistants, such as Cursor AI, GitHub Copilot, and Replit’s Ghostwriter AI, represents a transformative shift in programming education, particularly for non-STEM students. These tools democratize coding by enabling natural language code generation, intelligent error correction, and context-aware assistance within familiar coding environments. This article explores how these technologies empower educators across disciplines to introduce basic and advanced coding concepts to humanities students, a demographic traditionally underserved in programming education. By leveraging AI, instructors can teach non-STEM students the foundational principles of coding and guide them through the development of sophisticated projects, such as building APIs …

Diffuse Interface Modeling Of Non-Isothermal Stokes-Darcy Flow With Immersed Transmissibility Conditions, Hyoung Suk Suh

Faculty Scholarship

The coupling between free and porous medium flows has received significant attention since it plays an important role in a wide range of problems from fluid-soil interactions to biofluid dynamics. However, modeling this coupled process remains a difficult task as it often involves a domain decomposition algorithm in conjunction with a special treatment at the interface. The problem can become more challenging under non-isothermal conditions because it requires the iterative procedure at every time step to simultaneously meet the transient mass continuity, force equilibrium, and energy balance for the entire system. This article presents a diffuse interface framework for modeling …

Carbon-Sulfur Bond Elongation As The Promoting Reaction Coordinate In The Efficient Sub-Nanosecond Intersystem Crossing In Thianaphthene Derivatives, Cameron Griffith, Erqian Mao, Sean J. Hoehn, Sarah E. Krul, Carlos E. Crespo-Hernández

Faculty Scholarship

Thiophene derivatives have become integral to OLEDs, photovoltaics, and photodynamic therapy research. A deeper understanding of their excited state dynamics and electronic relaxation mechanisms is expected to provide important physical insights of direct relevance for these applications. In this study, thianaphthene (TN), 2-methylbenzothiophene (2MBT), and 3-methylbenzothiophene (3MBT) are investigated using femtosecond broadband transient absorption and steady-state spectroscopy techniques along with time-dependent density functional calculations in cyclohexane and acetonitrile. The photophysical properties and electronic relaxation mechanisms of these derivatives are elucidated. Small fluorescence quantum yields ranging from 0.4 to 1.1% are measured. It is demonstrated that excitation of TN at 290 …

A Magnetic-Directed Micro-Particle With Near-Ir Light Triggered Guest-Release Property, Jin Ge, Xiang Cheng, Lihan Rong

Faculty Scholarship

In this study, guest carriers, composed of magnetic-directed hydrogel particles with near-infrared (NIR)-light-triggered guest-release properties, are prepared via the one-pot method. The gel particles contain photostable NIR-responsive polypyrrole nanoparticles with temperature-sensitive poly(N-isopropyl acrylamide), the combination of which can induce the accelerated release of encapsulated drugs by 337% upon 5 minutes of NIR light irradiation based on the release profile of NRMG-1 at 10–15 min. Additionally, with magnetic iron oxide particles crosslinking the polyvinyl alcohol frames, these gel particles can form a physical barrier that decreases the release rate by 20% under an external magnetic field over 24 h. The magnetic …

3d Mr Fingerprinting For Dynamic Contrast-Enhanced Imaging Of Whole Mouse Brain, Yuran Zhu, Yuning Gu, Walter Zhao, Jiahao Lu, Junqing Zhu, Christina J. Macaskill, Andrew Dupuis, Mark A. Griswold, Dan Ma, Chris A. Flask, Xin Yu

Faculty Scholarship

Purpose: Quantitative MRI enables direct quantification of contrast agent concentrations in contrast-enhanced scans. However, the lengthy scan times required by conventional methods are inadequate for tracking contrast agent transport dynamically in mouse brain. We developed a 3D MR fingerprinting (MRF) method for simultaneous T1 and T2 mapping across the whole mouse brain with 4.3-min temporal resolution. Method: We designed a 3D MRF sequence with variable acquisition segment lengths and magnetization preparations on a 9.4T preclinical MRI scanner. Model-based reconstruction approaches were employed to improve the accuracy and speed of MRF acquisition. The method's accuracy for T1 and T2 measurements was …

Scheduling Meetings: Are The Odds In Your Favor?, Harsh Mathur

Faculty Scholarship

Abstract: Polling all the participants to find a time when everyone is available is the ubiquitous method of scheduling meetings nowadays. We examine the probability of a poll with m participants and ℓ possible meeting times succeeding, where each participant rejects r of the ℓ options. For large ℓ and fixed r/ℓ, we can carry out a saddle-point expansion and obtain analytical results for the probability of success. Despite the thermodynamic limit of large ℓ, the ‘microcanonical’ version of the problem where each participant rejects exactly r possible meeting times, and the ‘canonical’ version where each participant has a probability …

Association Between Residential Distance To Airport And Blood Lead Levels In Children Under 6 Living In North Carolina, 1992–2015, Abdul-Nasah Soale

Faculty Scholarship

Even with progress in lead poisoning prevention, research has consistently shown there is no safe blood lead level (BLL) for children.1 Behavioral and cognitive deficits are associated with lead exposure, even at low levels.2 Unfortunately, many piston-engine aircraft are still fueled by leaded aviation gas (avgas). The US Environmental Protection Agency (EPA) estimates that 5.2 million people live within 500m of an airport where avgas is used.3 Our past research in six North Carolina (NC) counties indicates that children living within 500m of airports where avgas is used have ∼4% higher BLLs than children who lived beyond 2,000m (reference group); …

Uncertainty Quantification In Machine Learning Models Via Gaussian Process Regression: A Comparative Study, Ayorinde E. Olatunde, Weiqi Yue, Pawan K. Tripathi, Roger H. French, Anirban Mondal

Faculty Scholarship

As the use of Machine learning models in science and engineering continues to increase, there is an increasing need for quantifying the uncertainties inherent in the predictions of these models. The more complex a model is, the more the uncertainties in its predictions increase. Amongst the plethora of methodologies used in quantifying uncertainties lies Gaussian Process Regression (GPR). GPR surmounts some of the popular shortfalls of other state-of-the-art methodologies. Although GPR has some quick wins in its application for uncertainty quantification, it is plagued with some shortfalls, such as scalability issues when the feature space increases as well as an …

Contemplating Existence: Ai And The Meaning Of Life, Emily Barnes, James Hutson

Faculty Scholarship

This article explores the intersection of artificial intelligence (AI) with existential philosophy, examining how AI technologies influence human conceptualizations of purpose and meaning. Despite rapid advancements in AI, the domain's implications for existential thought remain underexplored. By integrating interdisciplinary perspectives from psychology, philosophy, and AI ethics, this study elucidates how AI can shape, challenge, or enhance our understanding of life's purpose. It investigates theoretical frameworks and practical implementations of AI engaging in existential questions, analyzing both the capabilities and limitations of AI systems such as ChatGPT in simulating human existential thought. The ethical implications of AI's role in existential inquiries …

Questionable Arbitrator Habits, Robert H. Smit

Faculty Scholarship

International arbitrators do some things in conducting arbitrations, that tend to go without saying, but that I struggle to accept without question. I speak not of major procedural missteps that run afoul of principles of due process or of procedural “best practices” codified in soft law instruments like the IBA Rules on the Taking of Evidence. Rather, I speak of some of the smaller, uncodified procedural behaviors — procedural habits, I call them — that international arbitrators adopt within the confines, and between the interstices, of those codified due process and soft law “best practice” parameters. I speak of widely …

The Limits Of Immigrant Resilience, Huyen Pham, Natalie C. Cook, Ernesto Amaral, Raymond Robertson, Suojin Wang

Faculty Scholarship

Economists have identified important adaptations that immigrant workers have made to weather economic crises. During times of economic contraction, immigrant workers have moved across industries or geographical locations, downshifted to part-time work, and accepted lower wages to stay employed. Evidence from the Great Recession (2007–2009) shows the benefits of that economic resilience: immigrant workers were more likely than native-born workers to remain continuously employed, to have shorter periods of unemployment when they lost their jobs, and to regain jobs more quickly in the recovery period. Of course, these adaptations had significant personal costs for immigrant workers and their families, but …

A Framework For The Foundation Of The Philosophy Of Artificial Intelligence, Emily Barnes, James Hutson

Faculty Scholarship

In recent years, the rapid advancement of artificial intelligence (AI) technology has sparked profound questions about the nature of machine intelligence and the possibility of AI consciousness. As AI systems become increasingly sophisticated, examining their philosophical foundations has become imperative. This article investigates the intricate relationship between AI and existential thought, aiming to establish a comprehensive framework for understanding AI's philosophical underpinnings. The historical development of AI, from symbolic AI to contemporary machine learning paradigms, highlights the increasing complexity and sophistication of AI systems, prompting significant philosophical debates about machine consciousness. Theoretical models such as the Independent Core Observer Model …

Materials Data Science Using Cradle: A Distributed, Data-Centric Approach, Thomas G. Ciardi, Arafath Nihar, Rounak Chawla, Olatunde Akanbi, Pawan K. Tripathi, Yinghui Wu, Vipin Chaudhary, Roger H. French

Faculty Scholarship

There is a paradigm shift towards data-centric AI, where model efficacy relies on quality, unified data. The common research analytics and data lifecycle environment (CRADLE™) is an infrastructure and framework that supports a data-centric paradigm and materials data science at scale through heterogeneous data management, elastic scaling, and accessible interfaces. We demonstrate CRADLE’s capabilities through five materials science studies: phase identification in X-ray diffraction, defect segmentation in X-ray computed tomography, polymer crystallization analysis in atomic force microscopy, feature extraction from additive manufacturing, and geospatial data fusion. CRADLE catalyzes scalable, reproducible insights to transform how data is captured, stored, and analyzed. …

Bridging The Gap: Ai And The Hidden Structure Of Consciousness, Emily Barnes, James Hutson

Faculty Scholarship

The quest to develop Artificial Intelligence (AI) systems that possess human-like consciousness necessitates a deep dive into both theoretical and practical aspects underpinning this ambitious goal. This article builds on initial philosophical explorations of AI consciousness by examining the intricate and often hidden structures that may facilitate conscious experiences in AI. Drawing from concepts in cognitive science and neuroscience, the article elucidates how AI systems can be designed to replicate the structural and functional aspects of human consciousness. The discussion includes the Hierarchy of Spatial Belongings proposed by Forti (2024), frameworks like the Integrated Information Theory (IIT), and models linking …

Epilogue: Uc Law Journal – Rice Symposium, Ming H. Chen

Faculty Scholarship

The theme for this symposium, “We the People: Citizenship, Race, and Equality,” grew out of my concern for unequal citizenship for immigrants and non-white marginalized persons in their enduring quest to obtain fuller citizenship and belonging. More specifically, the theme furthers a dialogue sparked by my past writing on the structural limits on equal citizenship. In Pursuing Citizenship, I argued that citizenship entails both substantive and formal citizenship and that people cannot fully belong without formal citizenship. Uncovering the importance of formal citizenship led to some important policy reforms, including a reduction in the naturalization backlog under the Biden …

Beyond Automation: Ai As A Catalyst For New Job Creation In Software Development, Jill Willard, James Hutson

Faculty Scholarship

As artificial intelligence (AI) continues to evolve, its impact on software development and programming is profound, drawing parallels to the shift from assembler to object-oriented programming. This article explores how AI is reshaping the landscape of software jobs, creating new opportunities rather than diminishing them. By simplifying complex tasks and lowering barriers to coding, AI is expanding the technology "pie," introducing new use cases, and enhancing efficiency. The transition from monolithic services to microservices has reduced risks and accelerated deployment processes, and AI is poised to further this evolution by managing the complexities of service interactions through advanced orchestration layers. …

Materials Data Science Using Cradle: A Distributed, Data-Centric Approach, Thomas G. Ciardi, Arafath Nihar, Rounak Chawla, Olatunde Akanbi, Pawan K. Tripathi, Yinghui Wu, Vipin Chaudhary, Roger H. French

Faculty Scholarship

There is a paradigm shift towards data-centric AI, where model efficacy relies on quality, unified data. The common research analytics and data lifecycle environment (CRADLE™) is an infrastructure and framework that supports a data-centric paradigm and materials data science at scale through heterogeneous data management, elastic scaling, and accessible interfaces. We demonstrate CRADLE’s capabilities through five materials science studies: phase identification in X-ray diffraction, defect segmentation in X-ray computed tomography, polymer crystallization analysis in atomic force microscopy, feature extraction from additive manufacturing, and geospatial data fusion. CRADLE catalyzes scalable, reproducible insights to transform how data is captured, stored, and analyzed.

Experimental Investigation Of Heat Transfer And Pressure Drop In Copper Manifold Microchannel Heat Sinks, Yue Qiu, Huigang Wang, Jennifer Carter, James Mcguffin-Cawley, Chirag R. Kharangate

Faculty Scholarship

Over the past decade, various two-phase cooling solutions have been implemented to dissipate power in high heat flux electronic components. The manifold microchannel heat sink is an efficient thermal management solution that reduces heat resistance while also limiting the pumping power. However, there is limited work available on small form factor manifold microchannels with traditional heat sinking materials like copper. In this study, an experimental investigation is conducted to evaluate the heat transfer and pressure drop performance of copper manifold microchannel heat sinks using an environmentally friendly refrigerant, R1233zd(E), as the working fluid. A microchannel plate and a manifold plate, …

Machine Learning Boiling Prediction: From Autonomous Vision Of Flow Visualization Data To Performance Parameter Theoretical Modeling, Cho Ning Huang, Issam Mudawar, Chirag R. Kharangate

Faculty Scholarship

Flow boiling is a highly efficient configuration for meeting the high heat dissipation demands of thermal management systems. However, the complex physics of two-phase flow has hindered its broader application, especially in terms of quantifying visual information. Recent advancements in machine learning vision tools have revolutionized the analysis of phase change phenomena by enabling the digitalization of physically meaningful features such as void fraction, vapor-liquid interfacial behaviors, and liquid-solid wall wetting front areas en masse. In this study, we systematically investigate two-phase models that compute void fractions, heat transfer coefficients, and critical heat flux using live bubble data streams under …

L-Pbf High-Throughput Data Pipeline Approach For Multi-Modal Integration, Kristen J. Hernandez, Thomas G. Ciardi, Rachel Yamamoto, Mingjian Lu, Arafath Nihar, Jayvic Cristian Jimenez, Pawan K. Tripathi, John J. Lewandowski, Roger H. French, Laura S. Bruckman

Faculty Scholarship

Metal-based additive manufacturing requires active monitoring solutions for assessing part quality. Multiple sensors and data streams, however, generate large heterogeneous data sets that are impractical for manual assessment and characterization. In this work, an automated pipeline is developed that enables feature extraction from high-speed camera video and multi-modal data analysis. The framework removes the need for manual assessment through the utilization of deep learning techniques and training models in a weakly supervised paradigm. We demonstrate this pipeline’s capability over 700,000 high-speed camera frames. The pipeline successfully extracts melt pool and spatter geometries and links them to corresponding pyrometry, radiography, and …

Single-Fly Genome Assemblies Fill Major Phylogenomic Gaps Across The Drosophilidae Tree Of Life, K. Nicole Crown, Sarah E. Diamond, Ryan A. Martin

Faculty Scholarship

AU Long-read: Pleaseconfirmthatallheadinglevelsarerepresentedcorrectly sequencing is driving rapid progress in genome assembly : across all major groups of life, including species of the family Drosophilidae, a longtime model system for genetics, genomics, and evolution. We previously developed a cost-effective hybrid Oxford Nanopore (ONT) long-read and Illumina short-read sequencing approach and used it to assemble 101 drosophilid genomes from laboratory cultures, greatly increasing the number of genome assemblies for this taxonomic group. The next major challenge is to address the laboratory culture bias in taxon sampling by sequencing genomes of species that cannot easily be reared in the lab. Here, we build …

A Qualitative Study On The Integration Of Artificial Intelligence In Cultural Heritage Conservation, Kholoud Ghaith, James Hutson

Faculty Scholarship

The widespread adoption of generative artificial intelligence (GAI) technologies heralds an era of expanding possibilities in the domain of cultural heritage conservation. This paradigm shift is marked by a confluence of innovative methodologies, including digital twin mapping, digital archiving, and enhanced preservation strategies, aimed at safeguarding the vestiges of our shared past. The application of AI within this field represents a frontier where technology and tradition intersect, offering new vistas for the preservation of historical structures and artifacts that are at risk of deterioration or oblivion. This article endeavors to elucidate the perspectives of professionals within the conservation domain on …

Experimental Investigations On The Flow Boiling And Heat Transfer Characteristics During Chilldown Process In A Closed Loop Chilldown Test Section, Jayachandran K. Narayanan, Jason W. Hartwig, Mohammad Kassemi, Chirag R. Kharangate

Faculty Scholarship

In this study, closed loop chilldown experiments are performed on a 0.009525 m outer diameter SS-316 tube of length 0.6 m and thickness 0.001651 m in a horizontal flow configuration with PF-5060 as the working fluid. The challenges associated with the development of a closed loop chilldown test section in comparison with conventional open loop cryogenic chilldown experiments are discussed and the methods to overcome this are presented. The test section tube is heated to around 246–249 °C to perform the chilldown tests and the film, transition and nucleate boiling regimes along with the single phase liquid convective regimes are …

The History Of Diapers And Their Environmental Impact, Cansu Tokat, Nicholas C. Rickman, Cynthia F. Bearer

Faculty Scholarship

Impact: This article examines diaper practices around the world throughout history. This article reviews the innovation of the modern diaper and the environmental effects of disposable diapers.

Improving Performance Of Tpu By Controlled Crosslinking Of Soft Segments, Lucivan P. Barros Jr., Lucio R. De Souza, Rasoul Rahimzadeh, Ica Manas-Zloczower

Faculty Scholarship

Thermoplastic polyurethanes (TPUs) are a family of thermoplastic elastomers with great properties such as high elongation and excellent chemical and abrasion resistance, which are processable by conventional melting methods. Nevertheless, TPUs lose mechanical properties and thermal stability at higher temperatures. In this work, we designed and synthesized a new TPU with limited crosslinking of the soft segments in order to improve its performance at high temperatures while preserving processability. Additionally, the new TPU maintains its transparency. With the incorporation of 10% trifunctional polyol, the T was increased by 7°C, the storage modulus at room temperature (25°C) was improved by 412 …

An Advanced Light Scattering Imaging Model For Total Internal Reflection Microscopy Considering A Stratified Medium, Christopher L. Wirth

Faculty Scholarship

An advanced light scattering model for Total Internal Reflection Microscopy (TIRM) is presented. The model considers the specific TIRM geometry and deals with the scattering by an axisymmetric particle of arbitrary orientation placed in a stratified medium and the imaging of the scattered field. The scattered field is computed by truncating the scattered and internal field expansions and by using spherical and plane wave expansions for the free-space dyadic Green's function. While the first expansion is valid outside a sphere enclosing the particle, the second one is valid outside the tangent planes bounding the particle from above and below. We …

Enhancing Adult Learner Success In Higher Education Through Decision Tree Models: A Machine Learning Approach, Emily Barnes, James Hutson, Karriem Perry

Faculty Scholarship

This article explores the use of machine learning, specifically Classification and Regression Trees (CART), to address the unique challenges faced by adult learners in higher education. These learners confront socio-cultural, economic, and institutional hurdles, such as stereotypes, financial constraints, and systemic inefficiencies. The study utilizes decision tree models to evaluate their effectiveness in predicting graduation outcomes, which helps in formulating tailored educational strategies. The research analyzed a comprehensive dataset spanning the academic years 2013–2014 to 2021–2022, evaluating the predictive accuracy of CART models using precision, recall, and F1 score. Findings indicate that attendance, age, and Pell Grant eligibility are key …

The Hidden Monetary State, Gabriel Rauterberg, Joshua Younger

Faculty Scholarship

Money is a motley. While the state enjoys a monopoly on issuing new physical currency, a variety of instruments serve money-like roles in the financial system. The commercial banking system significantly augments the money supply through issuing deposits. Alongside it, a shadow banking system has also developed, offering a range of deposit substitutes.

This Article seeks to cast new light on the U.S. financial system by exploring how, over the course of the twentieth century, federal policymakers engaged in a series of distinct and largely uncoordinated monetary experiments. As we show through historical case studies, federal authorities designed, promoted, and …

Investigation Of Fluid Flow During Flow Boiling Inside A Horizontal Rectangular Channel With Single-Sided Heating Using Particle Image Velocimetry, Chinmay Shingote, Farshad Barghi Golezani, Chirag R. Kharangate

Faculty Scholarship

Subcooled flow boiling is a highly efficient cooling systems for thermal management systems. This study explores the intricate dynamics of subcooled flow boiling within a horizontal channel, investigating the impact of vapor generation on liquid-phase velocity using Particle Image Velocimetry (PIV) and advanced image processing techniques. Four mass flow rates ranging from 5–20 g/s with subcooled inlet conditions are investigated in a rectangular channel with single-sided heating. Three regions of interest along the heated channel are investigated for instantaneous PIV analysis. The PIV system captures detailed velocity profiles, illustrating the impact of varying mass flow rates and heat flux levels …