Digital Commons Network^™

Association Of Nucleoside Reverse Transcriptase Inhibitor Use With Reduced Risk Of Alzheimer's Disease Risk, Joseph Magagnoli Ph.D., Ms, Meenakshi Ambati, Tammy H. Cummings, Joseph Nguyen, Claire C. Thomas, Vidya L. Ambati, S. Scott Sutton, Bradley D. Gelfand, Jayakrishna Ambati

Faculty Publications

INTRODUCTION

Inflammasome activation is implicated in Alzheimer's disease (AD). We previously demonstrated that nucleoside reverse transcriptase inhibitors (NRTIs), drugs approved to treat human immunodeficiency virus (HIV) and hepatitis B, also inhibit inflammasome activation.

METHODS

We evaluated the association between NRTI exposure and subsequent development of AD in the United States Veterans Health Administration over a 24-year period and in the MarketScan database over a 14-year period using propensity score-matched multivariate Cox hazards regression and Kaplan–Meier analyses.

RESULTS

We report that in humans, NRTI exposure was associated with a significantly lower incidence of AD in two of the largest health insurance …

Graphics Processing Unit-Enabled Path Planning Based On Global Evolutionary Dynamic Programming And Local Genetic Algorithm Optimization, Junlin Ou, Ge Song, Yi Wang

Faculty Publications

This paper presents a novel path planning method for real-time robotic path planning in a dynamic environment involving moving obstacles. It combines on a holistic platform a global approach to rapidly generate initial paths of prominent diversity and a heuristic approach to enable local path refinement for enhanced computational efficiency, exploration, and robustness. The global approach innovates a formulation that treats a path planning problem with a visibility graph as a Markov decision process and decomposes the process into many subproblems. A new evolutionary dynamic programming approach (EDP) is proposed to solve these subproblems in an iterative manner using …

Global Sporadic-E Prediction And Climatology Using Deep Learning, J. A. Ellis, Daniel J. Emmons, M. B. Cohen

Faculty Publications

Sporadic-E (E_s) is an ionospheric phenomenon defined by strong layers of plasma which may interfere with radio wave propagation. In this work, we develop deep learning models to improve the understanding of E_s, including the presence, intensity and height of the layers. We developed three separate models. The first, building off earlier work in (J. A. Ellis et al., 2024, link in AFIT Scholar, 10.1029/2023sw003669), includes only the main features from radio occultation (RO) measurements. The second adds to that time, date, location, geomagnetic and solar indices, solar winds, x-ray flux, weather and lightning. A …

Compensating Regulatory Losers, Todd S. Aagaard

Faculty Publications

Every change in government regulation creates benefits and burdens that fall unequally on the population. This unequal distribution raises the question of whether fairness requires compensating those who bear regulatory costs—what is known as the “compensation problem.” The extensive body of scholarship addressing the compensation problem tends to focus only on the burdens of regulatory compliance and ignores the burdens of injuries left unaddressed by regulation. The effect of this differential treatment is to treat past regulatory windfalls as permanent entitlements and to omit from the analysis equally deserving regulatory losers who have suffered the consequences of unregulated harms. This …

Towards Human Modeling For Human-Robot Collaboration And Digital Twins In Industrial Environments: Research Status, Prospects, And Challenges, Guoyi Xia, Zied Gharairi, Thorsten Wuest, Karl Hribernik, Aaron Heuermann, Furui Liu, Hui Liu, Klaus-Dieter Thoben

Faculty Publications

Human-Robot Collaboration (HRC) and Digital Twins (DT) have significantly advanced industrial development and digital transformation. Human representations and models are essential in Industry 5.0, where human-centric is one of the key features. Despite the growing interest in human models for HRC and DT, a comprehensive overview of these models and enabling technologies currently needs to be provided. This paper aims to present the research status, prospects, applications, and challenges of human modeling for HRC and DT in industrial environments. This paper adopts a Systematic Literature Review (SLR) approach. Moreover, a framework is proposed to systematize human modeling aspects, the technologies …

Enhancing Self-Efficacy For Engagement With Diversity, Equity, And Inclusion Initiatives: Evaluation Of A Seven-Day Immersive Pilot Program In A College Of Education, Health, And Human Sciences, Jennifer M. Jabson Tree, Stefanie Benjamin, Dorian L. Mccoy Ph.D.

Faculty Publications

Colleges of health, human sciences, and education are often administratively organized to include a diverse array of human sciences departments that share roots in social justice and a commitment to advancing equity and inclusion. Despite espoused commitments to equity and inclusion, they frequently struggle to achieve the changes necessary for advancing equity, and inclusion principles across policies, practices, procedures, and units. We implemented and evaluated a 7-day, total immersion, 56-h, educational program entitled the “Social Justice Institute” (SJI) for two cohorts of faculty, staff, and administrators to develop self-efficacy to intervene in systemic oppression in their academic units. SJI delivered …

Global Campus, Christian Stuart

Faculty Publications

No abstract provided.

Soaring To New Heights: Team Angelwings Takes Innovation To Nasa's Stage, Armand Poblete

Faculty Publications

No abstract provided.

Simultaneous Observations Of Irregular Sporadic E Structures Using The Lwa And A Dps4d, Kenneth S. Obenberger, C. A. Taylor, Jonah J. Colman, Eugene Dao, J. Dowell, J. D. Eccles, Daniel J. Emmons, C. T. Fallen, J. M. Holmes, G. B. Taylor

Faculty Publications

Multi-instrument studies have recently shed new light on the morphology of sporadic E, especially intense sporadic E. Here we present simultaneous observations of dense sporadic E (E_s) structures using the Long Wavelength Array (LWA) radio telescopes and a Digisonde Portable Sounder 4D (DPS4D). Our coordinated observations show that the LWA radio telescopes in central New Mexico can reliably locate regions of dense Es structures as they pass over a Digisonde located over 500 km away in Texas. The LWA appears to be most sensitive to the densest E_s structures, which also appear to contain irregularities with vertical …

Urban Heat Mapping Strategies For Predicting Near-Surface Air Temperature In Unsampled Cities In Iowa, Mark D. Ecker, John P. Degroote, Clemir A. Coproski, Bingqing Liang, John Darko, James T. Dietrich

Faculty Publications

Elevated urban temperatures are a significant concern across the globe due to their negative health effects and increased energy use. Understanding the spatial variation in urban air temperatures can lead to informed mitigation and planning efforts. Air temperatures for multiple urban areas in the state of Iowa, USA, at three times of the day, were collected using customized sensors mounted on vehicles driven through a variety of landscapes in each urban area. Geographic information systems technology was used to process high-resolution landscape datasets and derive variables that summarize the urban landscape surrounding each temperature measurement point. Five different statistical models: …

Global Ionospheric F Region Parameters From Gnss-Pod Limb Measurements: Evaluations And Comparisons With Two Empirical Models - Iri-2020 And Nequick-2, Nimalan Swarnalingam, Dong L. Wu, Dieter Bilitza, Daniel J. Emmons, Cornelius Csar Jude H. Salinas, Artem Smirnov, Yenca Migoya-Oru

Faculty Publications

An optimal estimation (OE) technique has recently been developed for F region electron density (Ne) using Global Navigation Satellite System (GNSS) limb sounding on low Earth orbit (LEO) satellites (COSMIC-2, Spire, and FengYun-3). This method provides unprecedented spatiotemporal sampling for global monthly Ne climatology within 100–500 km in 2 hr intervals. The global dataset, collected during mid to moderately high solar activity, is compared with leading models: IRI-2020 and NeQuick-2. Diurnal variations in summer, winter, and equinoctial months are examined for the F2-layer peak, as well as the topside and bottomside of the F region. The observed and modeled NmF2 …

Small-Scale Production Of Bespoke Accelerated Aging Plutonium Alloy, K. S. Holliday, A. M. Parkes, Devin Rappleye, D. J. Roberts, K. J. Kondrat, S. A. Simpson, A. J. Swift, U. Mehta, R. M. Kissinger, D. L. Rosas, L. S. Ripplinger, E. R. Tardiff, S. L. Harris, S. Perkins, C. Hawkins, N. J. Gibbs, R. J. P. Driscoll, A. T. Sidambe, R. A. Cole, T. T. Roehling, J R. Jeffries

Faculty Publications

This study demonstrates the 100 g scale manufacture of a plutonium alloy that ages at an accelerated rate. The resulting alloy ages six times faster than typical weapons-grade plutonium due to the addition of ²³⁸Pu. As a major innovation, the process involved using a partial direct oxide reduction technique. This method was achieved by developing a new, complex geometry stirrer using additive manufacturing to reduce the ²³⁸Pu oxide and efficiently incorporate it into weapons-grade plutonium metal. The material was then purified by molten salt extraction and electrorefining before being alloyed with gallium. The alloy was then cold-rolled and …

Mechanism Of Property Enhancement Of Cu−Ti Alloys Via Microalloying With Cr And Mg Elements, Huan Wei, Hong Wei, Hua-Yun Du, Qian Wang, Cai-Zhi Zhou, Ying-Hui Weu, Li-Feng Hou

Faculty Publications

The effect of adding Cr and Mg on the microstructure and properties of Cu−Ti alloys was examined. Cu−Ti−Cr−Mg alloys were fabricated using vacuum induction melting. The microstructure and phase composition of Cu−Ti−Cr−Mg alloys in different aging states were characterized. Additionally, the hardness and electrical conductivity of the materials were investigated. Results show that the precipitation pattern in Cu−Ti−Cr−Mg alloys resembled that of binary Cu−Ti alloys, with Cr and Ti forming the intermetallic compound of Cr2Ti during casting. The introduction of Cr and Mg increased the hardness of the alloy. Increasing the Mg content in the Cu−Ti−Cr−Mg alloy led to grain …

Scalable Cyber-Physical Testbed For Cybersecurity Evaluation Of Synchrophasors In Power Systems, Shuvangkar Chandra Das, Tuyen Vu, Hebert L. Ginn Iii

Faculty Publications

This paper presents a synchrophasor-based real-time cyber-physical power system testbed with a novel security evaluation tool, pySynphasor, that can emulate different real attack scenarios on the phasor measurement unit (PMU). The testbed focuses on real-time cyber-security emulation using different components, including a real-time digital simulator, virtual machines (VM), a communication network emulator, and a packet manipulation tool. The script-based VM deployment and software-defined network emulation facilitate a highly scalable cyber-physical testbed, which enables emulations of a real power system under different attack scenarios such as address resolution protocol (ARP) poisoning attack, man-in-the-middle (MITM) attack, false data injection attack (FDIA), and …

Unveiling The Interfacial Reconstruction Mechanism Enabling Stable Growth Of The Delafossite Pdcoo2 On Al2o3 And Laalo3, Anna Scheid, Tobias Heil, Y. Eren Suyolcu, Qi Song, Niklas Enderlein, Arnaud P. Nono Tchiomo, Prosper Ngabonziza, Philipp Hansmann, Darrell G. Schlom, Peter A. Van Aken

Faculty Publications

Delafossites, composed of noble metal (A⁺) and strongly correlated sublayers (BO2^-), form natural superlattices with highly anisotropic properties. These properties hold significant promise for various applications, but their exploitation hinges on the successful growth of high-quality thin films on suitable substrates. Unfortunately, the unique lattice geometry of delafossites presents a significant challenge to thin-film fabrication. Different delafossites grow differently, even when deposited on the same substrate, ranging from successful epitaxy to complete growth suppression. These variations often lack a clear correlation to obvious causes like lattice mismatch. Unidentified stabilization mechanisms appear to enable growth in certain …

Porous Hierarchically Ordered Hydrogels Demonstrating Structurally Dependent Mechanical Properties, Elisabeth C. Lloyd, Sujata Dhakal, Shahrouz Amini, Rami Alhasan, Peter Fratzl, Douglas R. Tree, Svetlana Morozova, Robert J. Hickey

Faculty Publications

While hierarchical ordering is a distinctive feature of natural tissues and is directly responsible for their diverse and unique properties, efforts to synthesize biomaterials have primarily focused on using molecular-based approaches with little emphasis on multiscale structure. Here, we report a bottom-up self-assembly process to produce highly porous hydrogel fibers that resemble extracellular matrices both structurally and mechanically. Physically crosslinked nanostructured micelles form the walls of micrometer-sized water-rich pores with preferred orientation along the fiber direction. Low elastic moduli (< 1 kPa), high elasticity (extending by more than 12 times the initial length), non-linear elasticity (e.g., hyperelasticity), and completely reversible extension are derived from unevenly distributed strain between the micrometer-sized pores and the polymer chains, which is reminiscent of cellular solids. Control of the material microstructure and orientation over many orders of magnitude (e.g., nm–μm), while holding the nanostructure constant, reveals how the multiscale structure directly impacts mechanical properties.

Methods For Rotating Electrodes In High-Temperature Molten Salts, Ranon Fuller, Eloise Cluff, Edward Mercado, Carlos Mejia, Devin Rappleye

Faculty Publications

In this paper, we describe and validate a custom apparatus for performing high-speed (250-3,500 rpm) rotating electrode and dilatometer density experiments in molten salts. The reported details can aid in decreasing the difficulty and increasing the quality of such experiments. We discuss the design challenges and the solutions identified. We investigate methods for accurately measuring the solution resistance using electrochemical impedance spectroscopy (EIS), calculating the working electrode area using cyclic voltammetry (CV), and measuring the mass transfer-limited current of rotating electrodes using a series of chronoamperometry (CA) pulses. We further demonstrate the efficacy of our methods in producing accurate results.

Effect Of Precursors On Trimetallic Ruthenium-Based Catalysts Supported On Γ‑Al2O3 Pellets For Low-Temperature Ammonia Decomposition, Christopher J. Koch, Jennifer Naglic, Logan Kearney, Daniel Clairmonte, Binod Rai, Jochen A. Lauterbach, Lucas M. Angelette, Tyler Guin

Faculty Publications

Ammonia is a promising candidate as a liquid hydrogen energy storage medium, but it requires catalytic decomposition (ammonia cracking) to regenerate hydrogen. Recently developed trimetallic ruthenium−potassium-promoter (RuKM) ammonia decomposition catalysts have exceptionally low ammonia decomposition temperatures, able to perform the decomposition as low as 250 °C, which is significantly lower than other known catalysts that require temperatures above 500 °C. However, the effects of the RuKM precursor on the catalytic activity have not been investigated. We report the observed differences of 3% ruthenium/12% potassium/1% yttrium (RuKY) catalysts on γ-alumina synthesized from chloride-, nitrate-, and acetate-based precursors. Catalysts synthesized from chloride-based …

Ligo Detector Characterization In The First Half Of The Fourth Observing Run, S. Soni, B. K. Berger, D. Davis, F. Di Renzo, A. Effler, T. A. Ferreira, J. Glanzer, E. Goetz, G. González, A. Helmling-Cornell, B. Hughey, R. Huxford, B. Mannix, G. Mo, D. Nandi, A. Neunzert, S. Nichols, K. Pham, A. I. Renzini, R. M.S. Schofield, A. Stuver, M. Trevor, S. Álvarez-López, R. Beda, C. P.L. Berry, S. Bhuiyan, L. Blagg, R. Bruntz, S. Callos, M. Chan, P. Charlton, N. Christensen, G. Connolly

Faculty Publications

Progress in gravitational-wave (GW) astronomy depends upon having sensitive detectors with good data quality. Since the end of the Laser Interferometer Gravitational-Wave Observatory-Virgo-KAGRA third Observing run in March 2020, detector-characterization efforts have lead to increased sensitivity of the detectors, swifter validation of GW candidates and improved tools used for data-quality products. In this article, we discuss these efforts in detail and their impact on our ability to detect and study GWs. These include the multiple instrumental investigations that led to reduction in transient noise, along with the work to improve software tools used to examine the detectors data-quality. We end …

Ai-Based Detection Of Optical Microscopic Images Of Pseudomonas Aeruginosa In Planktonic And Biofilm States, Bidisha Roy Sengupta, Esther Mallet, Angel Torres, Ravyn Solis

Faculty Publications

No abstract provided.

Two-Layer Formulation For Long-Runout Turbidity Currents: Theory And Bypass Flow Case, Hongbo Ma, Gary Parker, Matthieu Cartigny, Enrica Viparelli, S. Balachandar, Xudong Fu, Rossella Luchi

Faculty Publications

Turbidity currents, which are stratified, sediment-laden bottom flows in the ocean or lakes, can run out for hundreds or thousands of kilometres in submarine channels without losing their stratified structure. Here, we derive a layer-averaged, two-layer model for turbidity currents, specifically designed to capture long-runout. A number of previous models have captured runout of only tens of kilometres, beyond which thickening of the flows becomes excessive, and the models without a lateral overspill mechanism fail. In our framework, a lower layer containing nearly all the sediment is a faster, gravity-driven flow that propels an upper layer, where sediment concentration is …

A Survey Of Sampling Methods For Hyperspectral Remote Sensing: Addressing Bias Induced By Random Sampling, Kevin T. Decker, Brett J. Borghetti

Faculty Publications

Identified as early as 2000, the challenges involved in developing and assessing remote sensing models with small datasets remain, with one key issue persisting: the misuse of random sampling to generate training and testing data. This practice often introduces a high degree of correlation between the sets, leading to an overestimation of model generalizability. Despite the early recognition of this problem, few researchers have investigated its nuances or developed effective sampling techniques to address it. Our survey highlights that mitigation strategies to reduce this bias remain underutilized in practice, distorting the interpretation and comparison of results across the field. In …

A Brave New World Of Human Resources Research: Navigating Perils And Identifying Grand Challenges Of The Genai Revolution, Anthony Nyberg, Deidra J. Schleicher, Bradford S. Bell, Corine Boon, Peter Cappelli, David G. Collings, Joseph E. Dalle Molle, Stefan Feuerriegel, Berry Gerhart, Yoojin Jeong, M. Audrey Korsgaard, Dana Minbaeva, Robert E. Ployhart, Prasanna Tambe, Ingo Weller, Patrick M. Wright, Valery Yakubovich

Faculty Publications

This paper reviews the transformative role of Generative Artificial Intelligence (GenAI) in Human Resource (HR) management, from a practice perspective, highlighting both opportunities and challenges and laying out a use-inspired future research agenda. This scoping review is grounded in insights from a unique Summit held in Spring 2024, which brought together HR academic scholars with dozens of Fortune 500 Chief Human Resource Officers (CHROs) and their top technical leaders to discuss the workforce implications of GenAI. The paper identifies six key themes from the Summit practitioners: GenAI as disruptive and transformative, data as competitive advantage, adoption challenges, potential ethical abuses, …

Average Biomechanical Responses Of The Human Brain Grouped By Age And Sex, Ahmed A. Alshareef, Aaron Carass, Yaun-Chiao Lu, Joy Mojumder, Alexa M. Diano, Olivia M. Bailey, Ruth J. Okamoto, Dzung L. Pham, Jerry L. Prince, Philip V. Bayly, Curtis L. Johnson

Faculty Publications

Traumatic brain injuries (TBIs) occur from rapid head motion that results in brain deformation. Computational models are typically used to estimate brain deformation to predict risk of injury and evaluate the effectiveness of safety countermeasures. The accuracy of these models relies on validation to experimental brain deformation data. In this study, we create the first group-average biomechanical responses of the brain, including structure, material properties, and deformation response, by age and sex from 157 subjects. Subjects were sorted intro three age groups—young, mid-age, and older—and by sex to create group-average neuroanatomy, material properties, and brain deformation response to non-injurious loading …

Examining Physiological Responses To Misophonic Triggers, Christian O'Reilly, Xuan Yang, Sewon Oh, Doug Wedell, Svetlana Shinkareva

Faculty Publications

We collected and analyzed an array of biosignals (face electromyogram, skin electrodermal activity, peripheral temperature, and electrocardiogram) in 60 participants with and without misophonia, a condition characterized by decreased tolerance to innocuous sounds. Our goal was to objectively characterize the physiological response to misophonia triggering sounds. We found that misophonic responses can be objectively identified in some cases through atypical physiological reactions to triggering stimuli, though not all participants exhibited this response. Our analyses suggest a large interindividual variability in response to misophonic triggers and highlights the need for methodological adjustments in future experiments to increase the detectability of misophonic …

Removing Eog Artifacts From Eeg Recordings Using Deep Learning, Christian O'Reilly, Scott Huberty

Faculty Publications

The electroencephalogram (EEG) directly measures the electrical activity generated by the brain. Unfortunately, it is often contaminated by various artifacts, notably those caused by eye movements and blinks (EOG artifacts). Such artifacts are usually removed using an independent component analysis (ICA) or other blind source separation techniques. However, it is difficult to assess whether subtracting EOG components estimated through ICA removes some neurogenic activity. It is crucial to address this question to avoid biasing EEG analyses. Toward that objective, we developed a deep learning model for EOG artifact removal that exploits information about eye movements available through eye-tracking (ET). Using …

A Reliable And Efficient Detection Pipeline For Rodent Ultrasonic Vocalizations, Sabah Shahnoor Anis, Devin Mark Kellis, Kris Ford Kaigler, Marlene A. Wilson, Christian O'Reilly

Faculty Publications

Analyzing ultrasonic vocalizations (USVs) is crucial for understanding rodents' affective states and social behaviors, but the manual analysis is time-consuming and prone to errors. Automated USV detection systems have been developed to address these challenges. Yet, these systems often rely on machine learning and fail to generalize effectively to new datasets. To tackle these shortcomings, we introduce ContourUSV, an efficient automated system for detecting USVs from audio recordings. Our pipeline includes spectrogram generation, cleaning, pre-processing, contour detection, post-processing, and evaluation against manual annotations. To ensure robustness and reliability, we compared ContourUSV with three state-of-the-art systems using an existing open-access USV …

Open Accessarticle Crystal Plasticity Modeling Of Dislocation Density Evolution In Cellular Dislocation Structures, Md Mahabubur Rohoman, Caizhi Zhou

Faculty Publications

The complex thermal cycles during the solidification process in metal additive manufacturing (AM) lead to the formation of high-density dislocation networks, organizing into submicron-scale cellular structures. These ultrafine structures are recognized as crucial for enhancing the mechanical properties of AM metals. In this study, we investigate the evolution of dislocation density within these cellular structures under plastic deformation and its impact on mechanical response using dislocation density-based crystal plasticity finite element (CPFE) modeling. The model incorporates the evolution of both statistically stored dislocation (SSD) and geometrically necessary dislocation (GND). Our simulations reveal that the yield and flow stresses of dislocation …

Deep Jansen-Rit Parameter Inference For Model-Driven Analysis Of Brain Activity, Deepa Tilwani, Christian O'Reilly

Faculty Publications

Accurately modeling effective connectivity (EC) is critical for understanding how the brain processes and integrates sensory information. Yet, it remains a formidable challenge due to complex neural dynamics and noisy measurements such as those obtained from the electroencephalogram (EEG). Model-driven EC infers local (within a brain region) and global (between brain regions) EC parameters by fitting a generative model of neural activity onto experimental data. This approach offers a promising route for various applications, including investigating neurodevel- opmental disorders. However, current approaches fail to scale to whole-brain analyses and are highly noise-sensitive. In this work, we employ three deep-learning architectures—a …

Wind-Resilient Solar: Harnessing Cfd For Enhanced Load Estimation, Aly Mousaad Aly

Faculty Publications

Solar panels are a cornerstone of renewable energy infrastructure, playing a pivotal role in global sustainability efforts. To ensure their resilience and long-term viability, accurate wind load estimations are essential for designing supporting structures, which account for nearly 50% of their total cost. However, traditional building codes lack comprehensive guidance for solar panels, resulting in inconsistent estimations due to discrepancies in scaled wall-bounded wind tunnel testing methodologies. These inaccuracies pose safety risks, increase costs, and hinder adoption. Emerging technologies like computational fluid dynamics (CFD) simulations offer a promising alternative by enabling full-scale analysis under realistic conditions of complete turbulence. This …