Digital Commons Network^™

Flow Structure And Mixing Near A Small River Plume Front: Winyah Bay, Sc, Usa, Christopher Papageorgiou, George Voulgaris, Alexander E. Yankovsky, Diane Bennett Fribance

Faculty Publications

This study presents Eulerian data from Winyah Bay, SC, USA collected during the passage of a tidal plume. The data captured the evolution and structure of the plume and include high-resolution velocity and temperature time series, supplemented by temperature – salinity profiles from a MicroCTD profiler. The observations identified a pre-existing plume extending to 4 m, with a water density of 1023.6 kg m⁻³, laying above denser ambient waters. Upon arrival, the newly discharged tidal plume introduced a fresher layer (1020.7 kg m⁻³) extending to 2.6 m, gradually thinning due to radial spreading. The plume's frontal …

Applying Machine Learning Methods To Laser Acceleration Of Protons: Synthetic Data For Exploring The High Repetition Rate Regime, John J. Felice, Ronak Desai, Nathaniel Tamminga, Joseph R. Smith, Alona Kryshchenko, Christopher M. Orban, Michael L. Dexter, Anil K. Patnaik

Faculty Publications

Advances in ultra‐intense laser technology have increased repetition rates and average power for chirped‐pulse laser systems, which offer a promising solution for many applications including energetic proton sources. An important challenge is the need to optimize and control the proton source by varying some of the many degrees of freedom inherent to the laser‐plasma interactions. Machine learning can play an important role in this task, as our work examines. Building on our earlier work in Desai et al. 2024, we generate a large ∼1.5 million data point synthetic data set for proton acceleration using a physics‐informed analytic model that we …

Teaching Machine Learning To Undergraduate Electrical Engineering Students, Gerald L. Fudge, Anika Rimu, William Zorn, July Ringle, Cody Barnet

Faculty Publications

Proficiency in machine learning (ML) and the associated computational math foundations have become critical skills for engineers. Required areas of proficiency include the ability to use available ML tools and the ability to develop new tools to solve engineering problems. Engineers also need to be proficient in using generative artificial intelligence (AI) tools in a variety of contexts, including as an aid to learning, research, writing, and code generation. Using these tools properly requires a solid understanding of the associated computational math foundation. Without this foundation, engineers will struggle with developing new tools and can easily misuse available ML/AI tools, …

Interactions And Community Structure Of Fungi And Prokaryotes In Salt And Brackish Marsh Ecosystems, Madeleine A. Thompso, Xuefeng Peng

Faculty Publications

Microbial communities play a fundamental role in biogeochemical cycling within salt and brackish marsh ecosystems, yet fungal-prokaryotic interactions in these environments remain poorly understood. This study employed metabarcoding of the 16S and 28S rRNA genes to investigate prokaryotic and fungal communities across four locations in sediments and surface waters of the North Inlet salt marsh and Winyah Bay brackish marsh (South Carolina, USA) over four time points from 2020 to 2021. Co-occurrence network analyses were used to identify potential microbial interactions and their ecological implications. Distinct fungal and prokaryotic communities were observed between the two marsh types. From the 16S …

A New Low-Rate Stable Hydrogel Cathode For Aqueous Zn-Ion Batteries, Roya Rajabi, Shichen Sun, Jamil A. Khan, Morgan Stefik, Kevin Huang

Faculty Publications

Aqueous Zn-ion batteries (ZIBs) are attractive candidates for large-scale energy storage owing to the abundance, low cost, and intrinsic safety of Zn metal. However, their practical application is hindered by poor cycle stability, especially at low current densities, due to cathode dissolution and limited electrochemically active sites (EAS). Herein, a hydrogel-based cathode comprising ammonium vanadate, carbon black, and a Zn-ion-conducting carboxymethyl chitosan–acrylamide hydrogel matrix doped with Zn(ClO4)2 is reported. This design establishes a continuous Zn-ion-conducting network, thereby maximizing EAS density throughout the electrode volume. The ZIB with the hydrogel cathode exhibits outstanding cycling stability, with 77% capacity retention after 2000 …

Fuel Adulteration Detection Using Open-Ended Coaxial Line And Circular Waveguide Techniques, Abdulrahman Alofi, Ahmed Shamseldin, Sharif Iqbal M. Sheikh, Hussein Attia

Faculty Publications

Fuel adulteration, the practice of blending lower-quality substances like kerosene with gasoline, poses significant challenges for ensuring fuel quality and safety. This study investigates two electromagnetic-based methods for detecting such adulteration: the open-ended coaxial line method and the circular waveguide method. The results reveal that the open-ended coaxial line technique is particularly effective in detecting kerosene contamination, identifying levels as low as 10% by measuring changes in the dielectric constant of the gasoline-kerosene mixture. Operating in the frequency range of 0.25–1.25 GHz, this method captures subtle changes in permittivity. In contrast, the circular waveguide method evaluates contamination by monitoring shifts …

Deep Learning Based Contactless Fingerprint Identification, Mohammad Alsmirat, M. Moneb Khaled, Aghyad A.L. Sayadi

Faculty Publications

Biometric authentication systems, particularly contactless fingerprint methods, offer enhanced security and convenience across various domains like access control, law enforcement, and finance. Despite these advantages, contactless systems face significant challenges related to image quality, finger orientation, and environmental factors. To address this, our paper presents the first extensive deep learning-based study on contactless fingerprint recognition using a large dataset of 2,143 images from 175 individuals. Our proposed approach integrates state-of-the-art preprocessing techniques with deep learning models to boost identification performance. After studying various transfer learning models, we achieved a high accuracy of 93.5%. We also conducted two further studies on …

Joint Neutrino Oscillation Analysis From The T2k And Nova Experiments, S. Abubakar, M. A. Acero, B. Acharya, P. Adamson, N. Anfimov, A. Antoshkin, E. Arrieta-Diaz, L. Asquith, A. Aurisano, D. Azevedo, A. Back, N. Balashov, P. Baldi, A. Bambah, E. F. Barenboim, A. Bashyal, A. Basti, K. Bromberg, N. Buchanan, Roberto Petti

Faculty Publications

The landmark discovery that neutrinos have mass and can change type (or flavour) as they propagate—a process called neutrino oscillation^1,2,3,4,5,6—has opened up a rich array of theoretical and experimental questions being actively pursued today. Neutrino oscillation remains the most powerful experimental tool for addressing many of these questions, including whether neutrinos violate charge-parity (CP) symmetry, which has possible connections to the unexplained preponderance of matter over antimatter in the Universe^7,8,9,10,11. Oscillation measurements also probe the mass-squared differences between the different neutrino mass states (Δm²), whether there are two light states and a heavier …

Home Range Size And Habitat Usage Of Hatchling And Juvenile Wood Turtles (Glyptemys Insculpta) In Iowa, Jeffrey W. Tamplin, Joshua G. Otten, Samuel W. Berg, Nadia E. Patel, Jacob B. Tipton, Justine M.R. Radunzel

Faculty Publications

The Wood Turtle (Glyptemys insculpta) is an endangered species in the state of Iowa and a species of conservation concern across their entire range. The Iowa population is characterized by high levels of adult and egg predation, displays little or no annual recruitment, and harbors an extremely low number of juveniles (7.3%). Home range and habitat usage studies of hatchling and juvenile Wood Turtles are limited to a few studies, and only one study of juveniles exists from the state of Iowa. Over a 10 yr period, we conducted a radiotelemetry study in Iowa on seven juvenile wood turtles for …

Prioritizing Data Quality In Machine Learning For Thermophysical Property Prediction: A Case Study On Normal Boiling Points Of Organic Compounds, Frank T. Mtetwa, Neil F. Giles, W. Vincent Wilding, Thomas A. Knotts Iv

Faculty Publications

Accurate prediction of the normal boiling point (NBP) of organic compounds is essential for the design and optimization of chemical processes. This is especially true for modern practice since experimental measurements of many compounds of contemporary importance are difficult to perform due to cost, purity, and stability issues. Traditional methods, such as group contribution (GC) and quantitative structure–property relationship (QSPR) approaches, are limited by inflexibility and diminishing accuracy in these situations. Machine learning (ML) approaches have shown promise but are plagued by two weaknesses for the nonspecialist: ease of use (especially compared to GC and QSPR) and proven accuracy for …

Intrinsic Defects (Vacancies And Antisites) In Neutron Irradiated Cdsip2 Crystals, Timothy D. Gustafson, Elizabeth M. Scherrer, Nancy C. Giles, Kevin T. Zawilski, Peter G. Schunemann, Jonathan E. Slagle, Kent L. Averett, Larry E. Halliburton

Faculty Publications

Cadmium silicon phosphide (CdSiP₂) is a nonlinear optical material widely used in optical parametric oscillators. Intrinsic defects (vacancies and antisites) are responsible for unwanted broad optical absorption bands in these crystals that degrade the performance of the devices. In the present work, optical absorption and electron paramagnetic resonance (EPR) spectra are acquired (at room temperature and 12 K, respectively) from a neutron-irradiated CdSiP₂ crystal. After the irradiation, the crystal is highly absorbing from the band edge near 600 nm to beyond 1.3 μm because of overlapping defect-related absorption bands. Heating to 550 °C removes nearly all the …

Using Serial Open Circuit Potentiometry To Study Ce And Gd Deposition In Molten Licl-Kcl Eutectic, Ranon G. Fuller, Guy L. Fredrickson, Devin Rappleye

Faculty Publications

The ability to electrochemically separate individual species in a pyrochemical reprocessing salt relies on first being able to detect a separation in their reaction potentials. This requires the ability to measure reaction potentials with extreme precision. In this work, we introduce a novel technique called serial open circuit potentiometry (SOCP) and demonstrate its effectiveness at precisely measuring metal deposition reaction potentials. SOCP has several advantages over traditional open circuit potentiometry, including its abilities to quantify the error of measured equilibrium potentials by averaging many individual OCP measurements, determine overpotential deposition nucleation potentials to instrument precision, and simultaneously determine the nucleation …

Moving Beyond Traditional Legal Scholarship Impact Rankings, John Cannan

Faculty Publications

Legal academia is likely stuck with law faculty scholarly impact rankings—that is, using citation counts to judge a law faculty’s reputation and influence. But why can’t we produce better, more useful studies? Current rankings rely on outdated methods. Moreover, these rankings—touted by their creators as objective—suffer from long-standing flaws. They are also grounded in limited understandings of legal citations as sources of information. This paper calls on law librarians to advance legal citation research by adopting methodologies from other information sciences and developing legal citation taxonomies and theories.

Pathlength, Altitude And Angle Of Incidence Dependence Of Remote Water Raman Scattering, Whitney E. Schuler, Paige K. Williams, Zechariah B. Kitzhaber, Caitlyn M. English, Tammi L. Richardson, Nikos Vitzilaios, Michael L. Myrick

Faculty Publications

A small remote Raman sensor was used to measure the Raman scattering signal from clear, still water as a function of water depth (12 cm and 396 cm depth), sensor distance above the water surface (20–300 cm), and angle of incidence (0–80°) to the normal of the water surface. Under thick- and thin-sample conditions, the signal depends on either the inverse, or the inverse square, of sensor distance from the water surface, respectively. A model is derived that fits data for different sensor distances, water depths, and angles of incidence. Fits to the measured data are consistent with the known …

Detecting Polar Ring Galaxies Via Deep Learning, Fawad Kirmani, Anathavishnu S. Unnii, Varsha P. Kulkarni, Kyle Lackey, John R. Rose

Faculty Publications

Polar ring galaxies (PRGs) are peculiar galaxies that show a ring of stars, gas, and dust oriented roughly over the poles of the central ‘host’ galaxy (i.e. roughly orthogonal to the disc of the host galaxy). The formation models for these rings involve mergers or tidal interactions of the host galaxy with another galaxy. Although the identified PRGs look different from each other, they all have a ring that is not in the same plane as the disc of the host galaxy. Unlike in galaxies such as our Milky Way, where stars form in spiral arms, the rings exemplify an …

Modeling The Effects Of Temperature And Resource Quality On The Outcome Of Competition Between Aedes Aegypti And Aedes Albopictus And The Resulting Risk Of Vector-Borne Disease, Emma Beck, Lauren Beuerle, Patt Martin, Regan Stambaugh, Rebeca De Jesús Crespo, Michael A. Robert, Suzanne L. Robertson

Faculty Publications

The community composition of vectors and hosts plays a critical role in determining risk of vector-borne disease transmission. Aedes aegypti and Aedes albopictus, two mosquito species that both transmit the viruses that cause dengue, chikungunya, and Zika, share habitat requirements and compete for resources at the larval stage. Ae. albopictus is generally considered a better competitor under many conditions, while Ae. aegypti is able to tolerate higher temperatures and is generally a more competent vector for many pathogens. We develop a stage-structured ordinary differential equation model that incorporates competition between the juvenile stages of two mosquito populations. We incorporate experimental …

Introducing A Markov Chain-Based Time Calibration Procedure For Multi-Channel Particle Detectors: Application To The Superfgd And Tof Detectors Of The T2k Experiment, S. Abe, H. Alarakia-Charles, I. Alekseev, C. Alt, T. Arai, T. Arihara, S. Arimoto, A. M. Artikov, Y. Awataguchi, N. Babu, V. Baranov, G. Barr, D. Barrow, L. Bartoszek, L. Bernardi, L. Berns, S. Bhattacharjee, A. V. Boikov, A. Blanchet, A. Blondel, A. Bonnemaison, S. Bordoni, M. H. Bui, T. H. Bui, F. Cadoux, S. Cap, A. Cauchois, J. Chakrani, P. S. Chong, A. Chvirova, P. Collard, M. Danilov, C. Davis

Faculty Publications

Inter-channel mis-synchronisation can be a limiting factor to the time resolution of high performance timing detectors with multiple readout channels and independent electronics units. In these systems, time calibration methods employed must be able to efficiently correct for minimal mis-synchronisation between channels and achieve the best detector performance. We present an iterative time calibration method based on Markov Chains, suitable for detector systems with multiple readout channels. Starting from correlated hit pairs alone, and without requiring an external reference time measurement, the method solves for fixed per-channel offsets, with precision limited only by the intrinsic single-channel resolution. A mathematical proof …

On Tight (K,ℓ)-Stable Graphs, Xiaonan Liu, Zi Xia Song, Zhiyu Wang

Faculty Publications

For integers (Formula presented.), a graph (Formula presented.) is (Formula presented.) -stable if (Formula presented.) for every (Formula presented.) with (Formula presented.). A recent result of Dong and Wu states that every (Formula presented.) -stable graph (Formula presented.) satisfies (Formula presented.). A (Formula presented.) -stable graph (Formula presented.) is tight if (Formula presented.); and (Formula presented.) -tight for some integer (Formula presented.) if (Formula presented.). In this paper, we first prove that for all (Formula presented.), the only tight (Formula presented.) -stable graphs are (Formula presented.) and (Formula presented.), answering a question of Dong and Luo. We then prove that …

Adipose Tissue Estrogen Receptor-Alpha Overexpression Ameliorates High-Fat Diet–Induced Adipose Tissue Inflammation, Marion C. Hope, Christian A. Unger, M. Chase Kettering, Cassidy E. Socia, Ahmed K. Aladhami, Barton C. Rice, Darya S. Niamira, Ben P. Wiznitzer, Diego Altomare Ph.D., William E. Cotham, Reilly T. Enos

Faculty Publications

Context

We created an innovative mouse model that enables inducible overexpression of estrogen receptor-alpha (ERα), specifically in adipose tissue (Adipo-ERα↑). Objective

We aimed to investigate how elevated Adipo-ERα↑ influences the development of high-fat diet (HFD)-induced obesity in both male and female mice. Methods

Male and female Adipo-ERα↑ mice and littermate controls were fed a low-fat diet (LFD) or HFD for 13 weeks. Adipo-ERα↑ was induced at the initiation of dietary treatment. Body morphology and composition, hepatic lipid accumulation, glucose tolerance, fasting insulin concentrations, and adipose tissue mRNA profiling were assessed. Liquid chromatography–mass spectrometry was used to determine circulating and adipose …

Pentanary And Hexanary Alkali Lanthanide Germanates Grown From Lanthanide Rich Flux Growth Reactions, Gregory Morrison, K. Pilar Zamorano, Virginia G. Jones, Ethan N. Adams, Hans Conrad Zur Loye

Faculty Publications

Single crystals of five compounds of a new structure type, the K3Nd52Ge12O100F7 type, were grown from lanthanide rich reactions using a KF/KCl flux: K3Ln52Ge12O100F7 (Ln = Nd, Eu), K3Ln52Ge12O100+δF7-δ (Ln = Pr, Tb), and K3Ca0.872Nd51.128Ge12O99.128F7.872. The five compounds crystallize in the cubic space group I-43d with lattice parameter 22.0350(2) Å for K3Nd52Ge12O100F7. The structure consists of a 3D checkerboard of LnX8, LnX7, LnX6, and GeO4 polyhedra, where X = O/F, and can be conceptually derived from the fluorite structure. K3Eu52Ge12O100F7 exhibits the fluorescence typical of Eu³⁺.

Better Digital Contracts With Prosocial Friction-In-Design, Brett Frischmann, Moshe Y. Vardi

Faculty Publications

Contract law is supposed to enable people to reach genuine agreements and cooperate. If this ideal was ever a reality, the rise of mass market contracts and boil­erplate rendered it pure fiction. Modern consumer contracts are incomprehensible to most people. No one reads them anyway.

Digital contracting involves design features that amplify traditional boilerplate harms and create others. For example, digital contracting is too cheap; low marginal costs lead to overexpansion in scale and scope. To make matters worse, the loss of autonomy from repeat engagement with digital contracting systems is pernicious. People become increasingly predictable and programmable as digital …

Record Number Of Students Enter Top Stem Phd Programs, Anthony Bosman

Faculty Publications

No abstract provided.

Glucodensity Functional Profiles Outperform Traditional Continuous Glucose Monitoring Metrics, Marcos Matabuena, Rahul Ghosal Ph.D., Javier Enrique Aguilar, Ayya Keshet, Robert Wagner, Carmen Fernández Merino, Juan Sánchez Castro, Vadim Zipunnikov, Jukka-Pekka Onnela, Francisco Gude

Faculty Publications

Continuous glucose monitoring (CGM) data have revolutionized the management of type 1 diabetes, particularly when integrated with insulin pumps to mitigate clinical events such as hypoglycemia. Recently, there has been growing interest in utilizing CGM devices in clinical studies involving healthy and diabetic populations. However, efficiently exploiting the high temporal resolution of CGM profiles remains a significant challenge. Numerous indices—such as time–in–range metrics and glucose variability measures–have been proposed, but evidence suggests these metrics overlook critical aspects of dynamic glucose homeostasis. As an alternative method, this paper explores the clinical value of glucodensity metrics in capturing glucose dynamics—specifically the speed …

Polymorphism Crystal Structure Prediction With Adaptive Space Group Diversity Control, Sadman Saadeed Omee, Lai Wei, Jianjun Hu

Faculty Publications

Crystalline materials can form different structural arrangements (i.e., polymorphs) with the same chemical composition, exhibiting distinct physical properties depending on how they are synthesized or the conditions under which they operate. For example, carbon can exist as graphite (soft, conductive) or diamond (hard, insulating). Computational methods that can predict these polymorphs are vital in materials science, which help understand stability relationships, guide synthesis efforts, and discover new materials with desired properties without extensive trial-and-error experimentation. However, effective crystal structure prediction (CSP) algorithms for inorganic polymorph structures remain limited. ParetoCSP2 is proposed, a multi-objective genetic algorithm for polymorphism CSP that incorporates …

Effects Of Climate Change On River And Groundwater Nutrient Inputs To The Coastal Ocean, Christina M. Richardson, Bernhard Peucker-Ehrenbrink, Shea Wyatt, Annie Bourbonnais, Vanessa Hatje, Claudia Frey, Tina Sanders, Diana E. Varela, Adina Paytan

Faculty Publications

Rivers and groundwater are major sources of nutrients to the global coastal ocean. Climate change is expected to impact nutrient fluxes from river basins and coastal aquifers through alterations to both hydrological and nutrient cycling processes. In this Review, we identify and summarize how climate change impacts, such as changes in precipitation, increased cryosphere melt, and sea level rise, will affect water discharge and nutrient concentrations in rivers and coastal groundwater, which ultimately control nutrient inputs to the coastal ocean. We also document key limitations in the current understanding of climate-related changes to nutrient fluxes, especially in coastal groundwater basins. …

Ncti Is The New Gilti, And States Should Still Conform, David Gamage, Darien Shanske

Faculty Publications

This essay continues our series on corporate profit shifting by analyzing how states should respond to the federal One Big Beautiful Bill Act, which transformed GILTI into the new Net Controlled Foreign Corporation Tested Income (NCTI) regime. We argue that states should conform to this new federal provision as a reasonable and legally sound approach to combatting the persistent profit shifting that erodes state tax bases. The 60 percent inclusion rate for NCTI effectively serves as a practical, "rough justice" estimate of income that was economically generated domestically but improperly shifted offshore — an estimate supported by empirical research. We …

Effect Of Vitamin D Supplementation During Pregnancy And Lactation On The Development Of Infants Born To Tanzanian Women Living With Hiv: A Secondary Analysis Of A Randomised Controlled Trial, Temiwunmi Shobanke, Alfa Muhihi, Nandita Perumal Phd, Nzovu Ulenga, Fadhlun M. Alwy Al-Beity, Christopher P. Duggan, Wafaie W. Fawzi, Karim P. Manji, Christopher R. Sudfeld

Faculty Publications

Background Infants born to pregnant women living with HIV (WLHIV) are at greater risk for morbidity and mortality and may also have poorer developmental outcomes as compared with infants who are not exposed to HIV. Nutrition interventions in pregnancy may affect developmental outcomes.

Objectives This study evaluated the effect of maternal vitamin D supplementation on infant development outcomes.

Design We conducted a secondary analysis of a randomised, triple-blind, placebo-controlled trial of maternal vitamin D supplementation from June 2015 to October 2019.

Setting Antenatal care clinics in Dar es Salaam, Tanzania.

Participants Pregnant WLHIV and their offspring.

Interventions Daily 3000 IU …

Microarchitectural Malware Detection Via Translation Lookaside Buffer (Tlb) Events, Cristian Agredo, Daniel F. Koranek, Christine M. Schubert Kabban, Jose R. Gutierrez Del Arroyo, Scott R. Graham

Faculty Publications

Prior work has shown that Translation Lookaside Buffer (TLB) data contains valuable behavioral information. Many existing methodologies rely on timing features or focus solely on workload classification. In this study, we propose a novel approach to malware classification using only TLB-related Hardware Performance Counters (HPCs), explicitly excluding any dependence on timing features such as task execution duration or memory access timing. Our methodology evaluates whether TLB data alone, without any timing information, can effectively distinguish between malicious and benign programs. We test this across three classification scenarios: (1) A binary classification problem involving distinguishing malicious from benign tasks, (2) a …

Resolution Of Physics And Deep Learning-Based Protein Engineering Filters: A Case Study With A Lipase For Industrial Substrate Hydrolysis, Spencer Gardiner, Peter Dollinger, Filip Kovacic, Jörge Pietruszka, Daniel Ess, Karl-Erich Jaeger, Gunnar F. Schröder, Dennis Della Corte

Faculty Publications

Computational enzyme design remains a powerful yet imperfect tool for optimizing biocatalysts, especially when targeting non-natural substrates. Using design tools we investigated Pseudomonas aeruginosa LipA, a lipase with a flexible lid domain crucial for substrate binding and turnover, aiming to enhance its hydrolysis of the industrially relevant substrate Roche ester. We generated an initial set of single-point mutations based on structural proximity to the active site and evaluated their effects using a computational pipeline integrating molecular dynamics (MD) simulations, density functional theory (DFT) calculations, and ensemble-based energy scoring. While we identified several active variants, attempts to rank them by activity …

Wave Intensity Analysis With Exercise Identifies Impairments In Pulmonary Hypertension, Christopher G. Lechuga, Farhan Raza, Mitchel J. Colebank, Claudia E. Korcarz, Jens C. Eickhoff, Naomi C. Chesler

Faculty Publications

Wave intensity analysis provides a novel approach to understanding the dynamic interactions between the right ventricle and pulmonary vasculature, particularly in pulmonary hypertension, a condition characterized by elevated pulmonary arterial pressures and vascular remodeling. This prospective study used wave intensity analysis to evaluate right ventricular and pulmonary vascular mechanics in 22 participants with pulmonary hypertension (including precapillary, isolated postcapillary, and combined pre/postcapillary pulmonary hypertension), and three without pulmonary hypertension. Forward and backward compression and decompression waves were quantified at rest and during incremental exercise (25, 50, and 75 W). Relationships between metrics of wave intensity analysis, hemodynamics, right ventricular function, …