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Faculty Publications

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Advancing Solar Farm Resilience: Cfd-Driven Wind Load Optimization, Aly Mousaad Aly Apr 2025

Advancing Solar Farm Resilience: Cfd-Driven Wind Load Optimization, Aly Mousaad Aly

Faculty Publications

This study investigates wind load design methods for ground-mounted solar panels and arrays by comparing Computational Fluid Dynamics (CFD) simulations with design standards. A case study of a solar farm impacted by Hurricane Maria examines the effects of elevation height, tilt angle, and variations in the American Society of Civil Engineers (ASCE) standards on wind loads and structural failure. Turbulence models, including Reynolds Stress Model, k–ε Model, and Large Eddy Simulation (LES), are used to analyze wind pressures, lift, drag, and peak pressures. Results show Phase 1 experienced higher wind loads than Phase 2 due to design differences. A cost-benefit …

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Beyond Traditional Analytics: Ai's Transformative Role In Marketing Intelligence And Customer Experience Optimization, Anindita Sengupta, Prathima Shivakumar Pattada, Anasuya Sengupta Apr 2025

Beyond Traditional Analytics: Ai's Transformative Role In Marketing Intelligence And Customer Experience Optimization, Anindita Sengupta, Prathima Shivakumar Pattada, Anasuya Sengupta

Faculty Publications

No abstract provided.

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A Skill Assessment Framework For The Fisheries And Marine Ecosystem Model Intercomparison Project, Nina Rynne, Camilla Novaglio, Julia Blanchard, Daniele Bianchi, Villy Christensen, Marta Coll, Jerome Guiet, Jeroen Steenbeek, Andrea Bryndum-Buchholz, Tyler D. Eddy, Cheryl Harrison, Olivier Maury, Kelly Ortega-Cisneros, Colleen M. Petrik, Derek P. Tittensor, Ryan F. Heneghan Apr 2025

A Skill Assessment Framework For The Fisheries And Marine Ecosystem Model Intercomparison Project, Nina Rynne, Camilla Novaglio, Julia Blanchard, Daniele Bianchi, Villy Christensen, Marta Coll, Jerome Guiet, Jeroen Steenbeek, Andrea Bryndum-Buchholz, Tyler D. Eddy, Cheryl Harrison, Olivier Maury, Kelly Ortega-Cisneros, Colleen M. Petrik, Derek P. Tittensor, Ryan F. Heneghan

Faculty Publications

Understanding climate change impacts on global marine ecosystems and fisheries requires complex marine ecosystem models, forced by global climate projections, that can robustly detect and project changes. The Fisheries and Marine Ecosystems Model Intercomparison Project (FishMIP) uses an ensemble modeling approach to fill this crucial gap. Yet FishMIP does not have a standardised skill assessment framework to quantify the ability of member models to reproduce past observations and to guide model improvement. In this study, we apply a comprehensive model skill assessment framework to a subset of global FishMIP models that produce historical fisheries catches. We consider a suite of …

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Government-Backed Insurance For Artificial Intelligence Technologies, Renee Henson Apr 2025

Government-Backed Insurance For Artificial Intelligence Technologies, Renee Henson

Faculty Publications

Artificial intelligence (AI) is an unpredictable technology that has the capacity to both help and harm people. Although insurance plays a key role in compensating for harms in other contexts, AI-produced damages evade traditional principles of risk pricing which limits viable commercial insurance coverage. AI requires modified insurance systems that can compensate diverse and unpredictable losses. Just like AI, at one time nuclear energy was viewed as a new and profitable, yet wholly unpredictable, technology that had the capacity to cause devastating harm. AI poses similar threats to society in certain domains, including, for example, health care (e.g., risk management …

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Exact Inference For Transformed Large-Scale Varying Coefficient Models With Applications, Tianyu Chen, Robert Habans, Thomas Douthat, Jenna Losh, Lida Chalangar Jalili Dehkharghani, Li Hsiang Lin Apr 2025

Exact Inference For Transformed Large-Scale Varying Coefficient Models With Applications, Tianyu Chen, Robert Habans, Thomas Douthat, Jenna Losh, Lida Chalangar Jalili Dehkharghani, Li Hsiang Lin

Faculty Publications

Studying migration patterns driven by extreme environmental events is crucial for building a sustainable society and stable economy. Motivated by a real dataset about human migrations, this paper develops a transformed varying coefficient model for origin and destination (OD) regression to elucidate the complex associations of migration patterns with spatio-temporal dependencies and socioeconomic factors. Existing studies often overlook the dynamic effects of these factors in OD regression. Furthermore, with the increasing ease of collecting OD data, the scale of current OD regression data is typically large, necessitating the development of methods for efficiently fitting large-scale migration data. We address the …

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Monitoring Molecular Interactions With Cell Membranes Using Time-Dependent Second Harmonic Generation Microscopy, Prakash Hamal, Sushant P. Sahu, Peter P. Piers, Huy Nguyen, Shashank S. Kamble, Robin L. Mccarley, Manas R. Gartia, Louis H. Haber Apr 2025

Monitoring Molecular Interactions With Cell Membranes Using Time-Dependent Second Harmonic Generation Microscopy, Prakash Hamal, Sushant P. Sahu, Peter P. Piers, Huy Nguyen, Shashank S. Kamble, Robin L. Mccarley, Manas R. Gartia, Louis H. Haber

Faculty Publications

Time-resolved second harmonic generation (SHG) microscopy is used to investigate the physicochemical interactions between positively charged, hydrophobic, drug-like molecules and the plasma membrane of human cells (nonsmall cell lung cancer, H596). In the present study, molecular adsorption and transport of the cationic molecules, malachite green (MG) and malachite green isothiocyanate (MGITC), are studied in real time in living H596 cells and in dead, fixed H596 cells. MGITC is shown to have stronger adsorption and more rapid transport kinetics as compared to MG due to increased dipole-dipole interactions. Additionally, MGITC is found to have faster adsorption and transport kinetics in living …

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Endothelial Dysfunction Promotes Age-Related Reorganization Of Collagen Fibers And Alters Aortic Biomechanics In Mice, Liya Du, Jeffrey Rodgers, Nazli Gharraee, Olivia Gary, Tarek Shazly, John F. Eberth, Susan M. Lessner Apr 2025

Endothelial Dysfunction Promotes Age-Related Reorganization Of Collagen Fibers And Alters Aortic Biomechanics In Mice, Liya Du, Jeffrey Rodgers, Nazli Gharraee, Olivia Gary, Tarek Shazly, John F. Eberth, Susan M. Lessner

Faculty Publications

Endothelial dysfunction, defined as a reduction in the bioavailability of nitric oxide (NO), is a risk factor for the occurrence and progression of various vascular diseases. This study investigates the effect of endothelial dysfunction on age-related changes in aortic extracellular matrix (ECM) microstructure and the relationship between microstructural adaptation and the mechanical response. Here, we used groups of NOS3 knockout (KO), NOS3 heterozygotes (Het), and wild-type (WT) B6 mice (controls) to study changes in hemodynamic parameters, collagen fiber organization, and both active and passive aortic mechanics using biaxial pressure myography over a time course from 1.5 to 12 mo. Our …

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Adipocyte-Derived Cchamide-1, Eiger, Growth-Blocking Peptide 3, And Unpaired 2 Regulate Drosophila Melanogaster Oogenesis, Chad Simmons, Isaiah Williams, Tancia Bradshaw, Alissa R. Armstrong Apr 2025

Adipocyte-Derived Cchamide-1, Eiger, Growth-Blocking Peptide 3, And Unpaired 2 Regulate Drosophila Melanogaster Oogenesis, Chad Simmons, Isaiah Williams, Tancia Bradshaw, Alissa R. Armstrong

Faculty Publications

In addition to energy storage, adipose tissue communication to other organs plays a key role in regulating organismal physiology. While the link between adipose tissue dysfunction and pathophysiology, including diabetes, chronic inflammation, and infertility, is clear, the molecular mechanisms that underlie these associations have not been fully described. We use Drosophila melanogaster as a model to better understand how adipose tissue communicates to the ovary. In this study, we utilized D. melanogaster’s robust genetic toolkit to examine the role of five adipokines known to control larval growth during development, CCHamide-1, CCHamide-2, eiger, Growth-blocking peptide 3, and unpaired 2 in regulating …

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Endothelial Dysfunction Promotes Age-Related Reorganization Of Collagen Fibers And Alters Aortic Biomechanics In Mice, Liya Du, Jeffrey Rodgers, Nazli Gharraee, Olivia Gary, Tarek Shazly, John F. Eberth, Susan M. Lessner Apr 2025

Endothelial Dysfunction Promotes Age-Related Reorganization Of Collagen Fibers And Alters Aortic Biomechanics In Mice, Liya Du, Jeffrey Rodgers, Nazli Gharraee, Olivia Gary, Tarek Shazly, John F. Eberth, Susan M. Lessner

Faculty Publications

Endothelial dysfunction, defined as a reduction in the bioavailability of nitric oxide (NO), is a risk factor for the occurrence and progression of various vascular diseases. This study investigates the effect of endothelial dysfunction on age-related changes in aortic extracellular matrix (ECM) microstructure and the relationship between microstructural adaptation and the mechanical response. Here, we used groups of NOS3 knockout (KO), NOS3 heterozygotes (Het), and wild-type (WT) B6 mice (controls) to study changes in hemodynamic parameters, collagen fiber organization, and both active and passive aortic mechanics using biaxial pressure myography over a time course from 1.5 to 12 mo. Our …

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Shear-Thinning Hydrogel For Delayed Delivery Of A Small Molecule Metalloproteinase Inhibitor Attenuates Myocardial Infarction Remodeling, Joshua E. Mealy, William M. Torres, Lisa A. Freeburg, Shayne C. Barlow, Alison A. Whalen, Chima V. Maduka, Tarek Shazly, Jason A. Burdick, Francis G. Spinale Apr 2025

Shear-Thinning Hydrogel For Delayed Delivery Of A Small Molecule Metalloproteinase Inhibitor Attenuates Myocardial Infarction Remodeling, Joshua E. Mealy, William M. Torres, Lisa A. Freeburg, Shayne C. Barlow, Alison A. Whalen, Chima V. Maduka, Tarek Shazly, Jason A. Burdick, Francis G. Spinale

Faculty Publications

No abstract provided.

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Ultrasonic Spray Coating Of Carbon Fibers For Composite Cathodes In Structural Batteries, Thomas Burns, Liliana Delatte, Gabriela Roman-Martinez, Kyra Glassey, Paul Ziehl, Monirosadat Sadati, Ralph E. White, Paul T. Coman Apr 2025

Ultrasonic Spray Coating Of Carbon Fibers For Composite Cathodes In Structural Batteries, Thomas Burns, Liliana Delatte, Gabriela Roman-Martinez, Kyra Glassey, Paul Ziehl, Monirosadat Sadati, Ralph E. White, Paul T. Coman

Faculty Publications

Structural batteries, also known as “massless batteries”, integrate energy storage directly into load-bearing materials, offering a transformative alternative to traditional Li-ion batteries. Unlike conventional systems that serve only as energy storage devices, structural batteries replace passive structural components, reducing overall weight while providing mechanical reinforcement. However, achieving uniform and efficient coatings of active materials on carbon fibers remains a major challenge, limiting their scalability and electrochemical performance. This study investigates ultrasonic spray coating as a precise and scalable technique for fabricating composite cathodes in structural batteries. Using a computer-controlled ultrasonic nozzle, this method ensures uniform deposition with minimal material waste …

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Machine Learning For Reactor Power Monitoring With Limited Labeled Data, C. L. Stewart, B. L. Goldblum, R. G. Abbott, L. Appleby, Brett J. Borghetti, V. Hollingshead, J. H. Whetzel Apr 2025

Machine Learning For Reactor Power Monitoring With Limited Labeled Data, C. L. Stewart, B. L. Goldblum, R. G. Abbott, L. Appleby, Brett J. Borghetti, V. Hollingshead, J. H. Whetzel

Faculty Publications

Real-time reactor power monitoring is critical for a variety of nuclear applications, spanning safety, security, operations, and maintenance. While machine learning methods have shown promise in monitoring reactor power levels, there is limited research on their efficacy in label-starved environments. The goal of this work is to assess the feasibility of classifying nuclear reactor power level using multisource data in scenarios with limited labels. Data were collected using low-resolution multisensors at four nuclear reactor facilities: two large research reactors and two TRIGA reactors. Within each pair, one reactor dataset served as the source and the other as the target in …

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Adipocyte-Derived Cchamide-1, Eiger, Growth-Blocking Peptide 3, And Unpaired 2 Regulate Drosophila Melanogaster Oogenesis, Chad Simmons, Isaiah H. Williams, Tancia W. Bradshaw, Alissa R. Armstrong Apr 2025

Adipocyte-Derived Cchamide-1, Eiger, Growth-Blocking Peptide 3, And Unpaired 2 Regulate Drosophila Melanogaster Oogenesis, Chad Simmons, Isaiah H. Williams, Tancia W. Bradshaw, Alissa R. Armstrong

Faculty Publications

In addition to energy storage, adipose tissue communication to other organs plays a key role in regulating organismal physiology. While the link between adipose tissue dysfunction and pathophysiology, including diabetes, chronic inflammation, and infertility, is clear, the molecular mechanisms that underlie these associations have not been fully described. We use Drosophila melanogaster as a model to better understand how adipose tissue communicates to the ovary. In this study, we utilized D. melanogaster’s robust genetic toolkit to examine the role of five adipokines known to control larval growth during development, CCHamide-1, CCHamide-2, eiger, Growth-blocking peptide 3, and unpaired 2 in regulating …

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Distribution Comparisons Of Eac Cost Growth For Aircraft Work Breakdown Structure Elements, Kyle P. Marquis, Edward D. White, Brandon M. Lucas, Robert D. Fass, Jonathan D. Ritschel, Shawn M. Valentine Apr 2025

Distribution Comparisons Of Eac Cost Growth For Aircraft Work Breakdown Structure Elements, Kyle P. Marquis, Edward D. White, Brandon M. Lucas, Robert D. Fass, Jonathan D. Ritschel, Shawn M. Valentine

Faculty Publications

This article analyzes and investigates the distribution of cost growth of the Estimate at Completion (EAC) for the Work Breakdown Structure (WBS) elements of approximately 60 historical United States Acquisition Category I Research, Development, Test and Evaluation aircraft programs. Using the method of maximum likelihood in conjunction with the Akaike Information Criterion, the authors suggest that both the lognormal and Weibull distributions provide relatively good fit to EAC cost growth, with the lognormal slightly edging out the Weibull. As a summarized finding, the authors present their empirical results for the mean, coefficient of variation (CV), the 15th and 85th percentiles …

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The Effects Of Snow Cover On The Dynamic Pressure Of Nuclear Detonation Blast Waves, Adam Card, Andrew W. Decker Apr 2025

The Effects Of Snow Cover On The Dynamic Pressure Of Nuclear Detonation Blast Waves, Adam Card, Andrew W. Decker

Faculty Publications

Blast pressure is the primary military targeting metric for nuclear weapons. Any local conditions that affect blast pressure have the potential for altering nuclear plans, both from defensive and offensive standpoints. Understanding the impact of snow to the blast wave, therefore, provides a benefit both to military planners and to warfighters on the ground, for any operation occurring in arctic environments. No existing data provides a quantitative description of how snow on the ground affects a nuclear detonation blast wave passing over it. Similar blast waves passing over dust have experimentally proven to enhance blast pressure in a localized region.1 …

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Analyzing Stability Of Estimates At Completion For Long Duration Development Efforts, Bradley Vuu, Jonathan D. Ritschel, Brandon M. Lucas, Edward D. White Apr 2025

Analyzing Stability Of Estimates At Completion For Long Duration Development Efforts, Bradley Vuu, Jonathan D. Ritschel, Brandon M. Lucas, Edward D. White

Faculty Publications

Defense program managers utilize Earned Value Management (EVM) methodologies to measure, report, and predict the cost and schedule performance of their programs. Previous research conducted by Christensen (1996) and Kim et al. (2019) has shown varied results in the stability of EVM Estimates at Completion (EACs). Stability is defined as a 10% or less deviation from the final EAC at a specified percent completion point of the program. The Christensen (1996) and Kim et al. (2019) studies also noted that program-specific factors, such as phase, can impact the accuracy of EVM metrics. This study builds upon those works by assessing …

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The Impact Of Solar Angle And Cloud Shadows On 3d Reconstruction Of Rolling Stock Cargo, Carlina M. Ostrand, Adam D. Reiman, Frank W. Ciarallo, Scott L. Nykl, Clark N. Taylor, Joshua F. Krutz Mar 2025

The Impact Of Solar Angle And Cloud Shadows On 3d Reconstruction Of Rolling Stock Cargo, Carlina M. Ostrand, Adam D. Reiman, Frank W. Ciarallo, Scott L. Nykl, Clark N. Taylor, Joshua F. Krutz

Faculty Publications

Meeting the relentless demand for more efficient air cargo transportation is of paramount importance for commercial needs and military missions. This study describes an experiment to test an innovative approach that harnesses cutting-edge stereoscopic vision technology to create 3D point clouds of rolling stock cargo across varying solar angles and cloud shadow conditions. Virtual cargo point clouds are generated by calibrating and systematically organizing the depth and location points from an RGB-D camera and then reprojecting them in a virtual environment. Measurement accuracy was rigorously tested across six camera positions in various combinations of weather conditions against physical ground truth …

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On Large Language Models In National Security Applications, William N. Caballero, Phillip R. Jenkins Mar 2025

On Large Language Models In National Security Applications, William N. Caballero, Phillip R. Jenkins

Faculty Publications

The overwhelming success of GPT-4 in early 2023 highlighted the transformative potential of large language models (LLMs) across various sectors, including national security. This article explores the implications of LLM integration within national security contexts, analyzing their potential to revolutionize information processing, decision-making, and operational efficiency. Whereas LLMs offer substantial benefits, such as automating tasks and enhancing data analysis, they also pose significant risks, including hallucinations, data privacy concerns, and vulnerability to adversarial attacks. Through their coupling with decision-theoretic principles and Bayesian reasoning, LLMs can significantly improve decision-making processes within national security organizations. Namely, LLMs can facilitate the transition from …

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New Method Of Impact Localization On Plate-Like Structures Using Deep Learning And Wavelet Transform, Asaad Migot, Ahmed Saaudi, Victor Giurgiutiu Mar 2025

New Method Of Impact Localization On Plate-Like Structures Using Deep Learning And Wavelet Transform, Asaad Migot, Ahmed Saaudi, Victor Giurgiutiu

Faculty Publications

This paper presents a new methodology for localizing impact events on plate-like structures using a proposed two-dimensional convolutional neural network (CNN) and received impact signals. A network of four piezoelectric wafer active sensors (PWAS) was installed on the tested plate to acquire impact signals. These signals consisted of reflection waves that provided valuable information about impact events. In this methodology, each of the received signals was divided into several equal segments. Then, a wavelet transform (WT)-based time-frequency analysis was used for processing each segment signal. The generated WT diagrams of these segments’ signals were cropped and resized using MATLAB code …

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Electronic And Magnetic Properties Of Nearly Spin-Gapless Semiconducting Fevtaal And Fecrzral, Caden Sadler, Samuel Smith, Nhat Phat Nguyen, Brandon Schmidt, Parashu Kharel, Paul M. Shand, Pavel V. Lukashev Mar 2025

Electronic And Magnetic Properties Of Nearly Spin-Gapless Semiconducting Fevtaal And Fecrzral, Caden Sadler, Samuel Smith, Nhat Phat Nguyen, Brandon Schmidt, Parashu Kharel, Paul M. Shand, Pavel V. Lukashev

Faculty Publications

Here, we present results of a computational study of electronic, magnetic, and structural properties of FeVTaAl and FeCrZrAl, quaternary Heusler alloys that have been recently reported to exhibit spin-gapless semiconducting behavior. Our calculations indicate that these materials may crystallize in regular Heusler cubic structure, which has a significantly lower energy than the inverted Heusler cubic phase. Both FeVTaAl and FeCrZrAl exhibit ferromagnetic alignment, with an integer magnetic moment per unit cell at equilibrium lattice constant. Band structure analysis reveals that while both FeVTaAl and FeCrZrAl indeed exhibit nearly spin-gapless semiconducting electronic structure at their optimal lattice parameters, FeVTaAl is a …

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Layer-Dependent Spin-Resolved Electronic Structure Of Ferromagnetic Triple-Layered Ruthenate Sr4ru3 O10, Prosper Ngabonziza, Jonathan D. Denlinger, Alexei V. Fedorov, Gang Cao, J. W. Allen, G. Gebreyesus, Richard M. Martin Mar 2025

Layer-Dependent Spin-Resolved Electronic Structure Of Ferromagnetic Triple-Layered Ruthenate Sr4ru3 O10, Prosper Ngabonziza, Jonathan D. Denlinger, Alexei V. Fedorov, Gang Cao, J. W. Allen, G. Gebreyesus, Richard M. Martin

Faculty Publications

High-resolution angle- and spin-resolved photoemission spectroscopy (ARPES) of the triple-layered ruthenate Sr4Ru3O10 reveals features of the electronic structure that extend our understanding of the layered strontium ruthenates. The spectra near the Fermi energy are very different from the nonmagnetic analogues Sr2RuO4 and Sr3Ru2O7 with distinct Fermi surfaces for wide electronlike minority spin bands around the zone center and narrow holelike majority spin Fermi surface contours around the zone corners. The most dramatic results are two narrow spectral peaks ∼30 meV below the Fermi level, a spin-minority holelike band at the Brillouin zone center, and a spin-majority saddle-band van Hove singularity …

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Neurosymbolic Knowledge-Grounded Planning And Reasoning In Ai Systems, Amit Sheth, Vedant Khandelwal, Kaushik Roy, Vishal Pallagani, Megha Chakraborty Mar 2025

Neurosymbolic Knowledge-Grounded Planning And Reasoning In Ai Systems, Amit Sheth, Vedant Khandelwal, Kaushik Roy, Vishal Pallagani, Megha Chakraborty

Faculty Publications

To build AI systems capable of decision-support assistance, such as AI-assisted healthcare, it is essential to develop user-centric decision-making processes that are robust, interpretable, and capable of effectively processing and acting on natural language interactions. Instruction-based prompting of large language models has demonstrated considerable success in supporting humans with information assistance tasks, including creative writing and content generation. However, recent studies reveal that language models exhibit limitations in performing complex reasoning and planning tasks, such as constructing compositional or hierarchical plans involving multiple reasoning steps. To address these challenges, we propose a neurosymbolic framework that integrates large language models with …

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Origin Of Large Effective Phonon Magnetic Moments In Monolayer Mos2, Hussam Mustafa, Cynthia Nnokwe, Gaihua Ye, Mengqi Fang, Swati Chaudhary, Jia An Yan, Kai Wu, Connor J. Cunningham, Colin M. Hemesath, Andrew James Stollenwerk, Paul M. Shand, Eui Hyeok Yang Mar 2025

Origin Of Large Effective Phonon Magnetic Moments In Monolayer Mos2, Hussam Mustafa, Cynthia Nnokwe, Gaihua Ye, Mengqi Fang, Swati Chaudhary, Jia An Yan, Kai Wu, Connor J. Cunningham, Colin M. Hemesath, Andrew James Stollenwerk, Paul M. Shand, Eui Hyeok Yang

Faculty Publications

Recent helicity-resolved magneto-Raman spectroscopy measurement demonstrates large effective phonon magnetic moments of ∼2.5 μB in monolayer MoS2, highlighting resonant excitation of bright excitons as a feasible route to activate Γ-point circularly polarized phonons in transition metal dichalcogenides. However, a microscopic picture of this intriguing phenomenon remains lacking. In this work, we show that an orbital transition between the split conduction bands (Δ0 = 4 meV) of MoS2 couples to the doubly degenerate E″ phonon mode (Ω0 = 33 meV), forming two hybridized states. Our phononic and electronic Raman scattering measurements capture these two states: (i) one with predominantly phonon contribution …

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Real-Time Defect Detection And Classification In Robotic Assembly Lines: A Machine Learning Framework, Fadi El Kalach, Mojtaba Farahani, Thorsten Wuest, Ramy Harik Mar 2025

Real-Time Defect Detection And Classification In Robotic Assembly Lines: A Machine Learning Framework, Fadi El Kalach, Mojtaba Farahani, Thorsten Wuest, Ramy Harik

Faculty Publications

Manufacturing systems have witnessed a significant transformation with the introduction of Industry 4.0, introducing new capabilities with the emergence of new technologies. One such instance is the proliferation of sensors enabling the generation and acquisition of vast amounts of data, leading to advancements in Artificial Intelligence (AI) for manufacturing. One field profiting from this is that of Time Series Analytics (TSC) which includes forecasting and classification. TSC can be crucial for fault detection and diagnosis in manufacturing systems. However, there are still challenges in utilizing manufacturing datasets to train and deploy classification algorithms for real time classification. As such this …

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Msbzip55 Regulates Salinity Tolerance By Modulating Melatonin Biosynthesis In Alfalfa, Tingting Wang, Jiaqi Yang, Jiamin Cao, Qi Zhang, Huayue Liu, Peng Li, Yizhi Huang, Wenwu Qian, Xiaojing Bi, Hui Wang, Yunwei Zhang Mar 2025

Msbzip55 Regulates Salinity Tolerance By Modulating Melatonin Biosynthesis In Alfalfa, Tingting Wang, Jiaqi Yang, Jiamin Cao, Qi Zhang, Huayue Liu, Peng Li, Yizhi Huang, Wenwu Qian, Xiaojing Bi, Hui Wang, Yunwei Zhang

Faculty Publications

No abstract provided.

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Synthesis Of A Series Of Rare-Earth-Based Multi-Anion Chalcogenide Iodides Re3Si2SeXS8-XI (Re = La, Ce, Pr, And Nd) Using The Flux-Assisted Boron-Chalcogen Mixture Method, Gopabandhu Panigrahi, Habiba Binte Kashem, Gregory Morrison, Hans Conrad Zur Loye Mar 2025

Synthesis Of A Series Of Rare-Earth-Based Multi-Anion Chalcogenide Iodides Re3Si2SeXS8-XI (Re = La, Ce, Pr, And Nd) Using The Flux-Assisted Boron-Chalcogen Mixture Method, Gopabandhu Panigrahi, Habiba Binte Kashem, Gregory Morrison, Hans Conrad Zur Loye

Faculty Publications

Single crystals and polycrystalline powders of rare earth mixed chalcogenide iodides La3Si2Se1.21S6.79I, Ce3Si2Se1.39S6.61I, Pr3Si2Se1.22S6.78I, and Nd3Si2Se1.18S6.82I were prepared using the reactive flux-assisted boron-chalcogen mixture (BCM) method at 850 °C. All compounds crystallized in the monoclinic crystal system, space group C2/c (space group number 15). The series adopts the La3Si2O8Cl structure type, containing isolated SiQ4 tetrahedra connected by REQ8 (RE = La, Ce, Pr and Nd) polyhedra; this arrangement creates tunnels that are filled by I atoms. The partial substitution of S by Se was carried out to modulate the optical properties. Phase pure samples and uniform solid solutions were obtained …

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Commodity Thermoplastic Elastomer-Enabled Templated Synthesis Of Large-Pore Ordered Mesoporous Materials, Anthony Griffin, Parker Frame, Yizhi Xiang, Zhe Qiang Mar 2025

Commodity Thermoplastic Elastomer-Enabled Templated Synthesis Of Large-Pore Ordered Mesoporous Materials, Anthony Griffin, Parker Frame, Yizhi Xiang, Zhe Qiang

Faculty Publications

Fabrication of ordered mesoporous materials (OMMs) has predominantly relied on templating-based methods. However, these methods are constrained by several limitations, especially the limited pore sizes attainable with commercially available surfactants used as structure-directing agents. To unlock the full potential of the OMMs, it is essential to develop synthetic strategies that facilitate the production of large-pore OMMs using scalable processes and cost-effective precursors. This work demonstrates the use of thermoplastic elastomer (TPE)-derived carbon replicas for synthesizing ordered mesoporous silica (OMS) and metal oxides (OMMOs) via precursor infiltration and template removal. The nanostructural evolution of the resulting inorganic materials was systematically investigated. …

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Ec2vec: A Machine Learning Method To Embed Enzyme Commission (Ec) Numbers Into Vector Representations, Mengmeng Liu, Xialong Ni, J. Ramanujam, Michal Brylinski Mar 2025

Ec2vec: A Machine Learning Method To Embed Enzyme Commission (Ec) Numbers Into Vector Representations, Mengmeng Liu, Xialong Ni, J. Ramanujam, Michal Brylinski

Faculty Publications

Enzyme commission (EC) numbers play a vital role in classifying enzymes and understanding their functions in enzyme-related research. Although accurate and informative encoding of EC numbers is essential for enhancing the effectiveness of machine learning applications, simple EC encoding approaches suffer from limitations such as false numerical order and high sparsity. To address these issues, we developed EC2Vec, a multimodal autoencoder that preserves the categorical nature of EC numbers and leverages their hierarchical relationships, resulting in more meaningful and informative representations. EC2Vec encodes each digit of the EC number as a categorical token and then processes these embeddings through a …

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Time-Series Forecasting In Smart Manufacturing Systems: An Experimental Evaluation Of The State-Of-The-Art Algorithms, Mojaba A. Farahani, Fadi El Kalach, Austin Harper, M.R. Mccormick, Ramy Harik, Thorsten Wuest Mar 2025

Time-Series Forecasting In Smart Manufacturing Systems: An Experimental Evaluation Of The State-Of-The-Art Algorithms, Mojaba A. Farahani, Fadi El Kalach, Austin Harper, M.R. Mccormick, Ramy Harik, Thorsten Wuest

Faculty Publications

Time-Series Forecasting (TSF) is a growing research area across various domains including manufacturing. Manufacturing can benefit from Artificial Intelligence (AI) and Machine Learning (ML) innovations for TSF tasks. Although numerous TSF algorithms have been developed and proposed over the past decades, the critical validation and experimental evaluation of the algorithms hold substantial value for researchers and practitioners and are missing to date. This study aims to fill this research gap by providing a rigorous experimental evaluation of the state-of-the-art TSF algorithms on thirteen manufacturing-related datasets with a focus on their applicability in smart manufacturing environments. Each algorithm was selected based …

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Comparison Of Deep Learning And Traditional Machine Learning Models For Predicting Mild Cognitive Impairment Using Plasma Proteomic Biomarkers, Kesheng Wang Phd, Ma, Bs, Donald A. Adjeroh, Wei Fang, Suzy M. Walter, Danqing Xiao, Ubolrat Piamjariyakul, Chun Xu Mar 2025

Comparison Of Deep Learning And Traditional Machine Learning Models For Predicting Mild Cognitive Impairment Using Plasma Proteomic Biomarkers, Kesheng Wang Phd, Ma, Bs, Donald A. Adjeroh, Wei Fang, Suzy M. Walter, Danqing Xiao, Ubolrat Piamjariyakul, Chun Xu

Faculty Publications

Mild cognitive impairment (MCI) is a clinical condition characterized by a decline in cognitive ability and progression of cognitive impairment. It is often considered a transitional stage between normal aging and Alzheimer’s disease (AD). This study aimed to compare deep learning (DL) and traditional machine learning (ML) methods in predicting MCI using plasma proteomic biomarkers. A total of 239 adults were selected from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort along with a pool of 146 plasma proteomic biomarkers. We evaluated seven traditional ML models (support vector machines (SVMs), logistic regression (LR), naïve Bayes (NB), random forest (RF), k-nearest neighbor …

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