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The Rider (2024-10-07), Natalie Lapsley

The Rider Archives

Vol.10 No.7

Stability Of Optimal Spherical Codes, Károly J. Böröczky, Alexey Glazyrin

School of Mathematical & Statistical Sciences Faculty Publications

For many extremal configurations of points on a sphere, the linear programming approach can be used to show their optimality. In this paper we establish the general framework for showing stability of such configurations and use this framework to prove the stability of the two spherical codes formed by minimal vectors of the lattice E8 and of the Leech lattice.

Further Results On Vanishing Coefficients In The Series Expansion Of Lacunary Eta Quotients, Timothy Huber, James Mclaughlin, Dongxi Ye

School of Mathematical & Statistical Sciences Faculty Publications

In a previous paper the authors proved the existence of various pairs\break (A(q),B(q)) of lacunary eta quotients with identically vanishing coefficients. Further experiments indicated that the results in this previous paper were just the "tip of the iceberg". We provide a comprehensive description of what experiment suggests and employ a variety of methods to prove experimentally-derived results. The work is a template and atlas for the subsequent study of lacunary eta quotients. A broad range of proof strategies are applied to confirm the vanishing structure. These comprise a representative sample of techniques that may be used to study the remaining …

Interpretable Spatio-Temporal Embedding For Brain Structural-Effective Network With Ordinary Differential Equation, Haoteng Tang, Guodong Liu, Siyuan Dai, Kai Ye, Kun Zhao, Wenlu Wang, Carl Yang, Lifang He, Alex D. Leow, Paul Thompson

Computer Science Faculty Publications

The MRI-derived brain network serves as a pivotal instrument in elucidating both the structural and functional aspects of the brain, encompassing the ramifications of diseases and developmental processes. However, prevailing methodologies, often focusing on synchronous BOLD signals from functional MRI (fMRI), may not capture directional influences among brain regions and rarely tackle temporal functional dynamics. In this study, we first construct the brain-effective network via the dynamic causal model. Subsequently, we introduce an interpretable graph learning framework termed Spatio-Temporal Embedding ODE (STE-ODE). This framework incorporates specifically designed directed node embedding layers, aiming at capturing the dynamic inter-play between structural and …

A Multivariate Approach To Assessing Landscape Structure Effects On Wildlife Crossing Structure Use, Thomas J. Yamashita, Humberto L. Perotto-Baldivieso, David B. Wester, Kevin W. Ryer, Richard J. Kline, Michael E. Tewes, John H. Young Jr., Jason V. Lombardi

School of Earth, Environmental, & Marine Sciences Faculty Publications

Background

Complexity in landscape structure is often assessed using individual metrics related to ecological processes. However, this rarely incorporates important relationships among metrics and may miss landscape structure effects. Multivariate statistics provide techniques for assessing overall landscape structure effects. We assessed how multivariate statistics could be used to connect landscape structure with an ecological process [bobcat (Lynx rufus) wildlife crossing structure (WCS) use]. We tested how landscape structure at WCS sites compared to the surrounding landscape and how structure affected detections at WCS sites. Our study was conducted in Cameron County, Texas, USA where WCSs are in various …

Local Environmental Organizations And Long-Term Investor Value Appropriation, Chune Young Chung, Gia Han Doan, Kainan Wang

Finance Faculty Publications

Highlights

• We explore how external and internal firm factors influence the impact of local activism.
• Our results indicate a positive effect of local activism on LIVA.
• Firms tend to increase their environmental disclosures in response to local activism.
• Local activism effects are greater when long-term value is prioritized.
• The findings support the social movement theory.

Abstract

This study uses datasets representing local non-governmental organizations (NGOs) to examine how local environmental organizations enhance long-term investor value appropriation (LIVA). We explore how external and internal firm factors influence the impact of local activism. Our results indicate a positive effect of local activism …

Computational And Experimental Data For Undoped And Er-Doped Lithium Tantalate Nanofluorescent Probes, Mkhitar A. Hobosyan, Andrea Pelayo Carvajal, Bhupendra B. Srivastava, Tamanna Zakia, Mohammed Jasim Uddin, Karen S. Martirosyan, Eric Rodriguez, Kofi Nketia Ackaah-Gyasi, Nicholas Dimakis

Physics & Astronomy Faculty Publications

The density functional theory (DFT) and experimental data presented in this paper refer to the research article “Computational and experimental study on undoped and Er-doped lithium tantalate nano fluorescent probes”. The DFT data contain electronic and optical properties for both LiTaO3 and LiTaO3:Er+3, with Er+3 occupying either Li or Ta sites at 4.167 mol. %. All these properties were calculated at the generalized gradient approximation (GGA) limit. Additionally, electronic information was calculated using the hybrid functional by Heyd, Scuseria, and Ernzerho (HSE06), which accurately predicts the location in energy for all Er-4f orbitals. We also include simulated X-ray near edge …

Stable Isotope Analysis Of Antipatharian Diets From The Northwestern Gulf Of Mexico, Keir J. Macartney, Victoria Salinas, Zachary Rivera, David Hicks

School of Earth, Environmental, & Marine Sciences Faculty Publications

Antipatharians, or black corals, form a key component of the benthic fauna found throughout the Gulf of Mexico (GOM). In the northwestern GOM, they are a common member of the communities found on the natural relic coral—algal banks known as the South Texas Banks as well as on artificial reefs. They are known to provide important habitat for many ecologically and economically important species in the area but are relatively understudied. Here we present a study on their trophic ecology using stable isotope analysis of carbon and nitrogen from 2 species, Antipathes atlantica and Stichopathes luetkeni from a mesophotic artificial …

Search For Eccentric Black Hole Coalescences During The Third Observing Run Of Ligo And Virgo, A. G. Abac, R. Abbott, H. Abe, Teviet Creighton, Mario C. Diaz, Francisco Llamas Villarreal, Soma Mukherjee, Gaukhar Nurbek, Volker Quetschke, Wenhui Wang

Physics & Astronomy Faculty Publications

Despite the growing number of binary black hole coalescences confidently observed through gravitational waves so far, the astrophysical origin of these binaries remains uncertain. Orbital eccentricity is one of the clearest tracers of binary formation channels. Identifying binary eccentricity, however, remains challenging due to the limited availability of gravitational waveforms that include the effects of eccentricity. Here, we present observational results for a waveform-independent search sensitive to eccentric black hole coalescences, covering the third observing run (O3) of the LIGO and Virgo detectors. We identified no new high-significance candidates beyond those that have already been identified with searches focusing on …

The P-Adic Schrödinger Equation And The Two-Slit Experiment In Quantum Mechanics, Wilson A. Zuniga-Galindo

School of Mathematical & Statistical Sciences Faculty Publications

p-Adic quantum mechanics is constructed from the Dirac-von Neumann axioms identifying quantum states with square-integrable functions on the N-dimensional p-adic space, Q_{p}^{N}. This choice is equivalent to the hypothesis of the discreteness of the space. The time is assumed to be a real variable. p-Adic quantum mechanics is the response to the question: what happens with the standard quantum mechanics if the space has a discrete nature? The time evolution of a quantum state is controlled by a nonlocal Schrödinger equation obtained from a p-adic heat equation by a temporal Wick rotation. This p-adic heat equation describes a particle performing …

On Helly Numbers For Crystals And Cut-And-Project Sets, Alexey Garber

School of Mathematical & Statistical Sciences Faculty Publications

We prove existence of Helly numbers for crystals and for cut-and-project sets with convex windows. Also we show that for a two-dimensional crystal consisting of 𝑘 copies of a single lattice the Helly number does not exceed 𝑘 + 6.

Testing The Reliability Of Chatgpt In Providing Spectral Information Of Organic Molecules, Ana Fraiman, Paola Barron, Brooke Dorsey, Sofia A. Delgado, Emanuel Sanchez

School of Mathematical & Statistical Sciences Faculty Publications

ChatGPT has emerged as a tool for predicting Hydrogen-1 Nuclear Magnetic Resonance (1HNMR), Infrared (IR), and Mass Spectrometry (MS) spectra. In order to rely on ChatGPT as a tool, a proof of concept for its application was essential. Having the ChatGPT outputs be validated by experimental data and databases (SDBS) has proven valuable for future uses. By inference, ChatGPT spectra output can be used to design problems without interference of copyright infringement of existing databases.

Predicting Students' Academic Performance Via Machine Learning Algorithms: An Empirical Review And Practical Application, Edmund F. Agyemang, Joseph A. Mensah, Obu-Amoah Ampomah, Louis Agyekum, Justice Akuoko-Frimpong, Amma Quansah, Oluwaferanmi M. Akinlosotu

School of Mathematical & Statistical Sciences Faculty Publications

Predicting academic outcomes is complex and influenced by factors like socioeconomic background, motivation, and learning style. Machine Learning (ML) algorithms have become increasingly important due to their ability to analyze large data volumes and identify prediction patterns. Results show ML’s success in predicting academic performance, though effectiveness varies by dataset, algorithm choice, and training features. There is no consensus on the most effective ML method for predicting student performance with broad applicability. Among the five algorithms evaluated in this study, the Random Forest Classifier emerged as the best model, achieving the highest G-Mean and accuracy of 0.9243 and 85.42% respectively. …

A Garch-Midas Approach To Modelling Stock Returns, Ezekiel N.N. Nortey, Ruben Agbeli, Godwin Debrah, Theophilus Ansah-Narh, Edmund F. Agyemang

School of Mathematical & Statistical Sciences Faculty Publications

Measuring stock market volatility and its determinants is critical for stock market participants, as volatility spillover effects affect corporate performance. This study adopted a novel approach to analysing and implementing GARCH-MIDAS modelling methods. The classical GARCH as a benchmark and the univariate GARCH-MIDAS framework are the GARCH family models whose forecasting outcomes are examined. The outcome of GARCH-MIDAS analyses suggests that inflation, interest rate, exchange rate, and oil price are significant determinants of the volatility of the Johannesburg Stock Market All Share Index. While for Nigeria, the volatility reacts significantly to the exchange rate and oil price. Furthermore, inflation, exchange …

Collaborative Practices In Virtual Group Work On Dynamic Geometry Tasks, Younggon Bae, V. Rani Satyam, Zareen G. Aga

School of Mathematical & Statistical Sciences Faculty Publications

The goal of this study is to explore productive ways to engage students in groupwork using dynamic geometry tasks in online synchronous classroom environments. In particular, we aim to understand the social, mathematical, and technological aspects of student collaboration in virtual spaces. We analyzed how three online groups of students collaboratively worked on dynamic geometry tasks of exploring interactive kaleidoscope applets in Desmos and producing visual representations and written descriptions of geometric transformations used in the applets. The students shared their screens in Zoom as they shared their findings and discussed how to draw and write to represent the kaleidoscopes. …

Quinoxaline 1,4-Di-N-Oxide Derivatives As New Antinocardial Agents, Isidro Palos, Alonzo Gonzalez-Gonzalez, Alma D. Paz-Gonzalez, José C. Espinoza-Hicks, Debasish Bandyopadhyay, Norma Paniagua-Castro, Marlene S. Galeana-Salazar, Jorge Ismael Castañeda-Sánchez, Julieta Luna-Herrera, Gildardo Rivera-Sanchez

School of Integrative Biological & Chemical Sciences Faculty Publications

Mycetoma is currently considered as a neglected tropical disease. The incidence of mycetoma is unknown but most of the worldwide cases are present in the “mycetoma belt” including countries like Mexico, India, Senegal, and others. The treatment of mycetoma depends on the etiological agent responsible for the case. Treatment success reaches 60 to 90%; however, common treatment has been reported to be ineffective in some cases, due in part to resistance to the prescribed antibiotics. Therefore, it is necessary to develop new therapeutic options. In the past two decades, quinoxaline derivatives have shown relevance as antibacterial agents. Therefore, in this …

Rotational Machine Fault Diagnosis Using Artificial Intelligence (Ai) Strategies For The Operational Challenges Under Variable Speed Condition: A Review, S. Sowmya, M. Saimurugan, Immanuel A. Edinbarough

Informatics and Engineering Systems Faculty Publications

Rotational machines in industries often encounter uncertainties during operation, are monitored and diagnosed through machine condition monitoring. Particularly when speed varies, Artificial Intelligence (AI) provides a great deal of support in recognizing machine faults efficiently. This paper reviews various articles that focused on fault identification in industrial rotating machines under varying speed condition using AI techniques. It investigates various machinery faults, experimental datasets and reputed institutions datasets, complex signals from faulty components and their processing method. Further, the involvement of intelligent based techniques in extracting and selecting suitable features to classify defects are comprehensively reviewed. In addition, as multi-signal fusion …

The First Joint Observation By Kagra With Geo 600, N. Uchikata, R. Abbott, H. Abe, Teviet Creighton, Mario C. Diaz, Francisco Llamas, Soma Mukherjee, Gaukhar Nurbek, Volker Quetschke, Wenhui Wang

Physics & Astronomy Faculty Publications

KAGRA conducted its first joint observation with GEO 600 for two weeks in April, 2020. We performed two types of all-sky searches and two searches dedicated to gravitational wave signals associated with gamma-ray burst events observed during the run. We did not detect any gravitational wave signals, which was expected given the sensitivity of both detectors at that time. However, we did confirm that several search pipelines used in the LIGO-Virgo-KAGRA collaborations worked effectively on the KAGRA data. In this presentation we will report on the data quality of detectors and the search results.

A Call For Better Understanding Household Complexity In Environmental Social Science, Rebecca Nixon, Brooke Mcwherter, Anna Erwin, Jonathan Bauchet, Zhao Ma

School of Earth, Environmental, & Marine Sciences Faculty Publications

No abstract provided.

Search For Core Collapse Supernovae Signals In Ligo’S Third Observation Run Using A Network Of Gravitational Wave Detectors Integrated With A Multiclass Convolutional Neural Network, Shahrear Khan Faisal, Gaukhar Nurbek, Michael Benjamin, Bhawana Sedhai, Soma Mukherjee

Physics & Astronomy Faculty Publications

In the existing body of literature, numerous waveforms of core collapse supernovae (CCSN) have emerged from extensive simulations conducted in high-performance computing facilities globally. These waveforms exhibit distinct characteristics related to their explosion mechanisms, influenced by parameters such as progenitor mass, angular momentum, gravitational wave energy, peak frequency, duration, and equation of state. Core collapse supernovae stand out as highly anticipated sources in LIGO’s fourth observation (O4) run, prompting dedicated efforts to detect them. The integration of machine learning, specifically convolutional neural networks (CNN), has become a pivotal avenue for analysis. This study addresses a fundamental query: how can a …

The Rider (2024-09-16), Natalie Lapsley

The Rider Archives

Vol.10 No.4

Hierarchical Neural Networks, P-Adic Pdes, And Applications To Image Processing, Wilson A. Zuniga-Galindo, B. A. Zambrano-Luna, Baboucarr Dibba

School of Mathematical & Statistical Sciences Faculty Publications

The first goal of this article is to introduce a new type of p-adic reaction–diffusion cellular neural network with delay. We study the stability of these networks and provide numerical simulations of their responses. The second goal is to provide a quick review of the state of the art of p-adic cellular neural networks and their applications to image processing.

A Correspondence Between Deep Boltzmann Machines And P-Adic Statistical Field Theories, Wilson A. Zuniga-Galindo

School of Mathematical & Statistical Sciences Faculty Publications

There is a strong interest in studying the correspondence between Euclidean quantum fields and neural networks. This correspondence takes different forms depending on the type of networks considered. In this work, we study this correspondence in the case of deep Boltzmann machines (DBMs) having a tree-like topology. We use p-adic numbers to encode this type of topology. A p-adic continuous DBM is a statistical field theory (SFT) defined by an energy functional on the space of square-integrable functions defined on a p-adic N-dimensional ball. The energy functionals are non-local, meaning they depend on the interaction of all the neurons forming …

Investigating Neuropsychiatric Symptoms In Asian Subgroups With Dementia Using The National Alzheimer's Coordinating Center (Nacc) Database, Maria Sophia Zaraspe Malbas, Luis A. Peña Marquez, Ramya Kunta, Astrid-Ines Foamkom, Ney Alliey-Rodriguez

Research Colloquium

Background: The incidence of neuropsychiatric symptoms (NPS), such as agitation, depression, anxiety, irritability, and sleeping disturbances, is common among patients with dementia. These symptoms are commonly evaluated with the Neuropsychiatric Inventory Questionnaire (NPI-Q), a useful tool for studying changes in behavior associated with neurodegenerative disorders for the past couple of decades. Another database utilizing the NPI-Q to assess NPS is the National Alzheimer’s Coordinating Center (NACC) Database. This uniform data set aims to store participant information, including neuropsychological tests, demographic information, and medical health history, collected from Alzheimer’s Disease Centers across the United States. To date, no studies using the …

Houston, We Have A Problem: Diagnostic Difficulties In The Aids Patient With Tuberculosis Infection, Hector D. Preciado, Maria E. Torres, Muhammad A. Siddiqui, Liza Salloum, Lee Gelpi, Jose E. Campo Maldonado

Research Colloquium

Background: Tuberculosis (TB) is the leading cause of infectious death worldwide with 1.5 million deaths annually and nearly 1/4th of the world’s population (1.7 billion) infected with latent tuberculosis infection (LTBI) in 2014. Its diagnosis conventionally relies on tuberculin skin testing (TST), and Interferon-Gamma Release Assay (IGRA) which relies on the production of Interferon Gamma (IFN-y) in response to Mycobacterium tuberculosis (MTB) specific antigens. IGRA is a useful tool for detecting latent TB disease, and can be used as an adjunct for the diagnosis of active TB cases. Increases in IFN-y levels might support the diagnosis of a new TB …

Systematic Review And Meta-Analysis Of Total Elbow Arthroplasty In Outpatient Versus Inpatient Settings, Blake Martin, Yolanda V. Gutierrez, Joshua Nwose, Robert H. Ablove, Juan Lopez-Alvarenga

Research Colloquium

Introduction: Total joint arthroplasties (TJA), including total elbow arthroplasty (TEA), are growing in number, shifting towards a younger age group, and moving more towards the outpatient setting. We are conducting this study to summarize the current knowledge about the outcomes of TEA in outpatient versus inpatient settings to allow for the best possible care when performing this procedure. We believe that outpatient TEA will have better outcomes with lower complication rates compared to inpatient TEA.

Methods: We conducted a systematic review and meta-analysis using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) framework. Electronic database searches were performed …

Investigating The Determinants Of Frailty: The Intersection Of Genetics And Environment In Mexican Americans From South Texas, Natasha Garcia-Rodriguez, Vincent P. Diego, Marcio Almeida, Jacob Galan, Sandra Laston, Joanne E. Curran, Tom Howard, Eron Manusov, John Blangero, Sarah Williams-Blangero

Research Colloquium

Background: The projected worldwide population of people aged 65 and over is anticipated to hit 1.6 billion by 2050. Frailty, marked by increased susceptibility to adverse health outcomes, mortality, and decreased health-related quality of life (HrQoL), poses significant challenges to an aging population. The Frailty Index (FI), is the ratio of the number of accumulated health deficits to the total number of deficits considered, is used to predict frailty.

Methods: Utilizing data from the Mexican American Family Study, we investigated the heritability and gene-environment (GxE) interactions influencing frailty in 1,029 Mexican American participants. We calculated the FI using 34 variables, …

Revolutionizing Medical Education: Harnessing Ai To Cultivate Critical Thinking Skills, Alex Zuo, Anthony L. Alanis

Research Colloquium

Our study intends to integrate AI-enabled tools in medical education, including but not limited to adaptive learning systems, virtual patients, and AI-enhanced assessment methods, to develop and foster critical thinking and problem-solving skills with the engagement of medical students. It is expected that this provides for a good environment for both teachers and students through workshops, online resources, and collaborative academic projects. We also consider AI-generated images and open educational resources that could augment curricula and personalize the experience for learners. Medical educators use storytelling, including AI for data storytelling, to package complex clinical data in approachable yet revealing ways …

Cultural And Socioeconomic Determinants Of Hip And Knee Arthroplasty In The Rio Grande Valley Community, Blake Martin, John M. Gaddis, Jared Hensley, Juan Lopez Alvarenga, Kelsey Baker

Research Colloquium

Background: Joint arthroplasty is one of the most common procedures performed worldwide and the volume of these operations are on the rise. In this study we aimed to discover if there were differences in the demographics and social factors associated with individuals undergoing total knee arthroplasty (TKA) and total hip arthroplasty (THA) in the Rio Grande Valley. We hypothesized that women would have increased risk of THA whereas individuals who are older and have higher BMI would have increased risk of TKA.

Methods: We conducted a retrospective chart review using the UTRGV UTHealth electronic database from January 1, 2018 through …

Molecular Studies In Understanding Novel Curcumin-Glucoside Role In The Prevention Of Alpha-Synuclein Aggregation In Relevance To Parkinson's Disease, Natasha N. Quailes, Nina Mourao, Megan W. Szobody, Sridhar Srinivasan, Sriya Gullapalli, Elias Arellano Villanueva, Tyler Torres, Kory Deshawn Punch, Anesu Karen Murambadoro, Blake Martin, Kelsey Baker, Nikhilesh Anand, Hansapani Rodrigo, Bharathi Shrikanth Gadad

Research Colloquium

Background: The synuclein deposition in the brain of Parkinson’s Disease (PD) is the hallmark pathology of the neurodegenerative pathway. The transformation of alpha-synuclein into synuclein monomer, toxic oligomer, and insoluble fibrils are key steps in cell dysfunction. We synthesized a novel molecule, Curcumin-glucoside (Curc-gluc), which has good water solubility and a favorable partition coefficient.

Methods: The present study focuses on α-syn aggregation kinetics in the presence and absence of Curc-gluc and Curcumin; we also tested the effect of Copper (Cu) and Iron (Fe) Fe binding through a mathematical approach, biophysical techniques, and docking models. In the mathematical approach, we applied …