Physical Sciences and Mathematics Commons^™

Ai Meets Economics: Can Deep Learning Surpass Machine Learning And Traditional Statistical Models In Inflation Time Series Forecasting?, Ezekiel N.N. Nortey, Edmund F. Agyemang, Enoch Sakyi-Yeboah, Obu-Amoah Ampomah, Louis Agyekum

School of Mathematical & Statistical Sciences Faculty Publications

This study examined the forecasting ability of deep learning (DL) and machine learning (ML) models against benchmark traditional statistical models for the monthly inflation rates in the USA. The study compared various DL and ML models like transformers, linear regression, gradient boosting (GB), extreme gradient boosting (XGBoost), and adaptive boosting (AdaBoost) with traditional baseline time-series models like autoregressive integrated moving averages (ARIMA) and exponential smoothing (ETS) with Holt-Winters seasonal method utilizing data sourced from the Federal Reserve Bank of St. Louis. The study consistently showed that all DL and ML models outperformed the traditional approaches. In particular, the Transformer (RMSE …

Automating Course Scheduling With Linear Programming And The Python Pulp Framework: First Steps, George K. Thiruvathukal

Computer Science: Faculty Publications and Other Works

This article presents a pragmatic approach to automating course scheduling in an academic setting using linear programming.

We explore how linear optimization via current open-source tools can efficiently handle scheduling constraints such as instructor preferences, teaching loads, course section requirements, and specific time slots. Using Python’s PuLP library and matplotlib for visualization, we built a flexible and accessible scheduling system.

Our research prototype balances course assignments while addressing department-specific needs, demonstrating how linear programming can simplify academic scheduling and improve efficiency.

Although this is a research prototype, our results already demonstrate the ability to generate a correct course schedule that …

Ontologies For Smart Agriculture: A Path Toward Explainable Ai-A Systematic Literature Review, Rima Grati, Najla Fattouch, Khouloud Boukadi

All Works

Smart agriculture has grown significantly over the last few years, particularly with the integration of advanced technologies (e.g., the Internet of Things, robots, artificial intelligence, etc.), leading to the development of intelligent agricultural systems. However, these systems often lack data integration, interoperability, and semantic explainability. Various approaches have been proposed to address these challenges. This study provides a comprehensive literature review that addresses, on the one hand, the use of semantic resources (e.g., semantic web technologies and ontologies) to tackle data integration and interoperability in smart agriculture systems and, on the other hand, the integration of explainable artificial intelligence into …

Balancing Privacy And Security: A Comparative Analysis Of Ai-Driven Surveillance In The Uae And Usa, Belal Alghafri, Abdallah Tubaishat

All Works

AI-driven surveillance has emerged as a critical tool for enhancing public safety, enabling authorities to monitor and prevent crime and terrorism more effectively. In countries like the UAE and the USA, these systems are often implemented under the pretext of national security, offering advanced methods to track potential threats. However, the increasing reliance on AI for surveillance raises significant ethical concerns about privacy and individual freedoms. The boundary between protecting citizens and infringing on their privacy becomes increasingly blurred, potentially leading to abuses of power, diminished public trust, and a pervasive atmosphere of fear. This paper explores the complex relationship …

Improving Efficiency Of Wind Flow Modeling For Wildfire Applications Using Analytical Parameterization And Neural Networks, Laura Jane Housley

Theses and Dissertations

We model wind velocity fields for applications in wildfire modeling. We improve the accuracy of the mass-conserving WindNinja solver without compromising efficient temporal complexity. We consider two approaches: the parameterization method and the neural network method. In the parameterization method, we analyze eddies in the wake of obstacles by parameterizing cavity zones as inspired by the research by Kaplan & Dinar in 1996. Kaplan & Dinar parameterize cavity zones around rectangular buildings, and we approximate a continuous landscape with rectangular buildings in order to extend this parameterization to complex terrains. In the neural network method, we train a U-net CNN …

Digital Thread: Bridging Macro–Micro Services In System-Of-Systems, Joshit Mohanty

Graduate Student Government Association Research Conference

Organizations and industries increasingly rely on distributed services in decentralized environments—ranging from large-scale, system-of-system architectures to fine-grained, agent-based microservices. While this distributed paradigm offers flexibility and innovation, it presents critical challenges such as interoperability gaps, inconsistent data formats, and a lack of holistic oversight. Traditional integration approaches, including ad-hoc middleware or enterprise service buses, tend to solve these issues reactively. As a result, technical debt accumulates, stakeholder misalignments persist, and scaling to new demands becomes complex.

This research proposes digital thread (DT) as the unifying framework to create an authoritative source of truth: a continuous flow of information across the …

Meta-Clustering For Specialized Language Models: Enhancing Contextual Adaptation And Mitigating Hallucinations In Diverse Healthcare Environments, Joshit Mohanty, Vaishali Vaishali, Sandeep Kumar Nayak, Sumit Lahiri

Graduate Student Government Association Research Conference

Large Language Models (LLMs) have significantly advanced conversational AI by enabling dialogic information-seeking and task execution across diverse domains. However, their extensive parameters and broad domain scope lead to “data hallucinations.” These shortcomings are particularly evident in dynamic and diverse environments like India’s healthcare sector, where myriad languages, regional practices, and cultural nuances demand specialized, localized expertise rather than one-size-fits-all generalist models. This paper introduces a meta-clustering framework that integrates Distilled Language Models (DLMs) and Small/Specialized Language Models (SLMs) with meta-learning principles to address these limitations. By drawing on evidence from works such as MedHalu and Med-HALT, the framework seeks …

Tamos: Task-Aware Multi-Agent Orchestrator System, Joshit Mohanty, Sandeep Kumar Nayak, Sumit Lahiri

Graduate Student Government Association Research Conference

Large language models (LLMs) are increasingly at the core of multi-agent systems (MAS). However, the high resource demand, error propagation, and lack of adaptive evaluation mechanisms pose significant challenges in deploying these agentic solutions at scale. To address these concerns, this research proposes a Task-Aware Multi-Agent Orchestrator System designed to refine the agentic framework, categorizing tasks autonomously, assigning specialized evaluation datasets, and balancing token usage against functional effectiveness. This approach underscores robust data management, including AsyncHow, Mosaic AI, and Synthetic Preference Optimization (PO) corpora. Each dataset targets specific dimensions of agent performance, such as dynamic task decomposition and tool integration …

Graphtreemed: A Hybrid Graph-Tree Rag Architecture For Mission-Critical Medical Applications, Joshit Mohanty, Sandeep Kumar Nayak, Sumit Lahiri

Graduate Student Government Association Research Conference

Studies within engineering management indicate that decision-making is often based on the cognitive processing of grouped and pictographic information clusters entangled with high-level pattern recognition. Similarly, graph-based retrieval-augmented generation (RAG) architectures substantially improve diagnostic accuracy and interpretability, while tree-structured systems reduce critical misses through hierarchical reasoning. However, existing solutions often lack a unified framework that seamlessly integrates these two paradigms to address the multifaceted demands of mission-critical healthcare settings. This proposal introduces GraphTreeMed, a novel hybrid RAG architecture designed to harness the complementary strengths of graph-based and tree-based retrieval mechanisms, thereby advancing the safety and efficacy of clinical decision support …

Building Climate Resilience Through Food Recovery, San Diego Regional Climate Collaborative, Alexander Pourfard, Aranza Ruvalcaba

San Diego Regional Climate Collaborative

In an effort to combat GHG emissions from food waste, California passed Senate Bill 1383 (SB 1383) into law in 2016. The bill contains key provisions aimed at increasing edible food recovery. By rescuing food that would otherwise go to the landfill, California entities have been able to help save precious food and advance sustainable solutions to address climate change in their local communities. Since the food recovery requirements took effect in January of 2022, over 217,000 tons of unsold food have been recovered annually, providing more than 700 million meals to food-insecure Californians.

Stereotyping In Language (Technologies): An Examination Of Racial And Gender Stereotypes In Natural Language And Language Models, Messi Lee

McKelvey School of Engineering Graduate Student Theses & Dissertations

This dissertation examines stereotyping across natural language and language technologies through three interconnected studies. The first chapter applies a contemporary model of race relations from social psychology to investigate America's racial framework within American English, revealing how language encodes hierarchical associations between racial/ethnic groups and attributes of superiority and Americanness. The second chapter extends this analysis to Large Language Models (LLMs), finding that these language technologies portray socially subordinate groups as more homogeneous compared to dominant groups. The third chapter investigates stereotyping in Vision Language Models (VLMs), showing that these language technologies generate more uniform representations for women than men …

Synthesis Of Novel Five-Membered Heterocycles As Bioactive Agents, Subrata Roy

Student Theses and Dissertations

This dissertation outlines the synthesis of various types of heterocyclic small molecules across three chapters. The first chapter discusses the efficient synthesis of seventy-one fluorinated pyrazole molecules and thirty-five 3,4-dihydroxy and 4-hydroxy phenyl-substituted thiazole molecules. These molecules were designed and synthesized for their potential antibacterial properties. The second chapter discusses the synthesis of fused thiazole molecules with enone-based steroidal natural products. This chapter reports on sixty-five novel fused thiazole molecules distributed across three libraries featuring different steroidal natural products: ethisterone, bisnoralcohol, and diosgenin. These molecules were designed and synthesized as potential anticancer agents. The third chapter comprises the development of …

Multiparticle Quantum Plasmonics: Fundamentals And Applications, Mingyuan Hong

LSU Doctoral Dissertations

Quantum plasmonics explores the interaction between light and collective charge oscillations at metal-dielectric interfaces, enabling strong light confinement and enhanced quantum effects at the nanoscale. While traditional quantum optics has primarily focused on single-photon systems, an intermediate regime exists between classical and single-photon optics - multiparticle (or multiphoton) quantum optics. In this regime, classical light sources, when analyzed through techniques such as photon-number-resolving (PNR) detection and projective measurement, can reveal nontrivial quantum correlations. This thesis investigates how multiparticle quantum plasmonics harnesses these correlations to control quantum statistical properties, enhance coherence, and enable novel applications in quantum technologies.

In this thesis, …

Impacts Of Extreme Weather Conditions On Coastal Fisheries Near Bayou Teche, Louisiana From 2019–2023, Georgia C. Davis

LSU Master's Theses

The region of Southcentral Louisiana, particularly around Bayou Teche, thrives on its commercial and recreational fisheries. These fisheries are occasionally subject to events like extreme weather that cause sudden and unexpected losses (NOAA Fisheries, 2024a). Between 2019 and 2023, a series of extreme weather events impacted southcentral Louisiana near Bayou Teche. This series of extreme events includes Hurricane Barry (2019), Hurricane Laura (2020), Hurricane Delta (2020), Hurricane Zeta (2020), Hurricane Ida (2021), and a United States Drought Monitor (USDM) D4 drought (2023). This study analyzes the impacts of the extreme weather series on coastal fish observed species richness (SR) in …

04.21.2025 Ored Connect, Liz Williamson

ORED Newsletter

ORED Stakeholder Meetings

SEC Travel Grants

The Locus Of Intersecting Tangents, James Austin Canard

Student Theses and Dissertations

Consider two tangent lines to a given function, whose horizontal distance between the tangent points is a constant h. As the tangent lines change on the function, the intersection of the two tangent lines trace out a curve. We study the relationship between this new curve, called the locus curve, and the original function. We begin by studying the effects of vertical scaling, vertical shifting, vertical sheering, horizontal scaling, and horizontal shifting. Then we use asymptotical methods to study the behavior of the locus as the tangent lines approach infinity. We use a similar method to observe the behavior as …

A Novel Approach To Detect Shapes Of Constant Width, Ian D. Monk

Theses and Dissertations

Bodies of constant width have been studied mathematically since Euler and have found various applications in engineering and other areas. In this paper, we seek to develop novel tools to gain deeper insights into these shapes and to better be able to analyze them. The idea which motivates the development in this paper is as follows: a body has constant width if and only if the chord connecting the intersections of the boundary of the body with each pair of parallel hyper planes which lie tangent to a body lies perpendicular to the hyper planes.This is a known fact and …

Innovative Machine Learning, Isotopic, And Hydrogeochemical Techniques For Groundwater Analysis In Arid Landscapes In Egypt’S Eastern Desert, Saad Ahmed Mohallel, Hesham Morgan, Ali Elgendy, Surendra Maharjan, Shahryar Fazli, Wenzhao Li, Rejoice Thomas, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Groundwater serves as a lifeline in Egypt’s hyper-arid Eastern Desert, particularly for agricultural and domestic uses. However, a comprehensive understanding of groundwater origin, quality, and recharge dynamics in the region remains limited due to geological complexity, data scarcity, and the high cost of isotopic analysis. This study addresses these challenges by integrating stable isotopes (δ¹⁸O and δ²H), hydrogeochemical parameters, remote sensing, and explainable artificial intelligence (AI) to investigate groundwater dynamics and support sustainable water management strategies. A total of 34 groundwater samples were collected from three key aquifers: the Quaternary alluvium, Nubian Sandstone, and fractured Basement aquifers. Hydrochemical analyses and …

Stability, Binding, And Charge Localization In Amorphous Tin Monoxide: A-Sno And A-Sn-Ta-O With Variable Composition, Julia E. Medvedeva, Joshua Santy, Mathew Pollard

Physics Faculty Research & Creative Works

The structural, electronic, and optical properties of amorphous SnO and Sn-Ta-O (with Sn:Ta ratios 5:1, 3:2, and 1:2) are investigated as a function of density and metal composition using ab initio molecular dynamics simulations and hybrid functional calculations. The short- and medium-range structures of amorphous oxides are thoroughly studied by calculating the effective coordination numbers, effective distances, distortion, and their time variances for metal-oxygen (M-O), O-M, M-M shells, the M-O-M and O-M-O angle distribution, as well as the volume, shape, and distribution of structural voids. Disorder is found to suppress both Sn-O and Sn-Sn coordination but not the O-Sn coordination. …

Trimming Five Generated Gorenstein Ideals, Luigi Ferraro, W. Frank Moore

School of Mathematical & Statistical Sciences Faculty Publications

Let (𝑅,𝔪,𝕜) be a regular local ring of dimension 3. Let I be a Gorenstein ideal of R of grade 3. It follows from a result of Buchsbaum and Eisenbud that there is a skew-symmetric matrix of odd size such that I is generated by the sub-maximal pfaffians of this matrix. Let J be the ideal obtained by multiplying some of the pfaffian generators of I by 𝔪; we say that J is a trimming of I. In a previous work, the first author and A. Hardesty constructed an explicit free resolution of 𝑅/𝐽 and computed a DG algebra …

Prime Factors Of The Third Cyclotomic Polynomial Of The Same Form And Inequalities Related To Odd Perfect Numbers, Cody Stenberg Hansen

Theses and Dissertations

This thesis relates some results about odd perfect numbers and factorizations of cyclotomic polynomials evaluated on integers. In Chapter 2 we parameterize solutions to the equality $\Phi_3(x)=\Phi_3(a_1)\Phi_3(a_2)\cdots\Phi_3(a_n)$, where $x,a_i\in \Z_{>0}$ for $1\leq i\leq n$, $\Phi_3$ is the third cyclotomic polynomial, and each $\Phi_3(a_i)$ is prime, in the cases that $n=2,3,4,5$. Let $N$ be an odd perfect number. Let $\omega(N)$ be the number of distinct prime factors of $N$ and let $\Omega(N)$ be the total number (counting multiplicity) of prime factors of $N$. In Chapter 3, we use a result from Chapter 2 to show that $\frac{99}{37}\omega(N) - \frac{187}{37} \leq …

Shaping Mathematics Identity: An Exploratory Study On Specifications Grading In Calculus I At A Hispanic-Serving Institution, Luis M. Fernandez, Kaitlyn Stephens Serbin, Cristina Villalobos, Shaghayegh Azadi Setayesh, Guillermo Garza

School of Mathematical & Statistical Sciences Faculty Publications

Calculus I courses play a pivotal role in shaping students' STEM pathways, making it essential to adopt pedagogies that foster both achievement and mathematics identity development, particularly among underserved groups such as Hispanic students. This study explores the impact of Specifications Grading, an alternative assessment method where students meet specific course learning objectives through multiple attempts, on students’ mathematics identity development. Through a comparative case study of two Calculus I students at a Hispanic-Serving Institution, one enrolled in a specification graded course and the other in a traditionally-graded course, we examine shifts in their self-perceptions of competence, interest, recognition in …

2025 Academic Conference Program

Holy Cross Undergraduate Academic Conference

No abstract provided.

Rings Of Ice, Erik Johnson

Natural Science Faculty

Have you ever seen the Milky Way? For most Americans, it is impossible to see the band of light from the galaxy outside their homes. To be able to experience the night sky as our ancestors did, we have to travel to places away from light pollution. International Dark Sky Week, which is happening April 22–30 this year, celebrates the night sky and offers everyone a chance to reflect on the lighting choices made around their home and in their community.

Search For Continuous Gravitational Waves From Known Pulsars In The First Part Of The Fourth Ligo-Virgo-Kagra Observing Run, A. G. Abac, R. Abbott, I. Abouelfettouh, F. Acernese, Teviet Creighton, Mario C. Diaz, Francisco Llamas Villarreal, Soma Mukherjee, Volker Quetschke, Wenhui Wang

Physics & Astronomy Faculty Publications

Continuous gravitational waves (CWs) emission from neutron stars carries information about their internal structure and equation of state, and it can provide tests of general relativity. We present a search for CWs from a set of 45 known pulsars in the first part of the fourth LIGO–Virgo–KAGRA observing run, known as O4a. We conducted a targeted search for each pulsar using three independent analysis methods considering single-harmonic and dual-harmonic emission models. We find no evidence of a CW signal in O4a data for both models and set upper limits on the signal amplitude and on the ellipticity, which quantifies the …

Decadal To Millennial-Scale Morphodynamic Evolution Of Microtidal Marshes, Samuel Mason Zapp

LSU Doctoral Dissertations

Tidal marshes are susceptible to rapidly converting to open water due to their low elevations within the tidal frame. A continuous supply of externally supplied mineral sediment and in situ organic material production is necessary to offset elevation losses from relative sea level rise and erosion. Complex feedbacks between hydrodynamics, the erosion, transport, and deposition of sediment, and physical modification of the landscape by the establishment of intertidal vegetation govern the spatial and temporal patterns of elevation response. This dissertation focuses on modeling how these dynamics drive marsh landscape evolution in the past, present, and into the future under changing …

Data-Driven Survival Modeling For Breast Cancer Prognostics: A Comparative Study With Machine Learning And Traditional Survival Modeling Methods, Theophilus Gyedu Baidoo, Hansapani Rodrigo

School of Mathematical & Statistical Sciences Faculty Publications

Background This investigation delves into the potential application of data-driven survival modeling approaches for prognostic assessments of breast cancer survival. The primary objective is to evaluate and compare the ability of machine learning (ML) models and conventional survival analysis techniques, to identify consistent key predictors of breast cancer survival outcomes.

Methods This study employs data-driven survival modeling approaches to predict breast cancer survival, including survival-specific methods such as the Cox Proportional Hazards (CPH) model, Random Survival Forests (RSF), and Cox Proportional Deep Neural Networks (DeepSurv), as well as machine learning models like Random Forests (RF), XGBoost, Support Vector Machines (SVM) …

Predicting The Unpredictable: Predicting The March Madness Champion Using Statistical Modeling, Jack Sweeney

Honors Projects in Information Systems and Analytics

One of the more exciting and hardest parts of the men's college basketball postseason tournament, named March Madness, is to pick who wins each game and determine the overall champion of the tournament. The data analysis conducted will help determine the overall tournament winner. A machine learning model is implemented using college basketball metrics from previous years to determine the overall winner of this prestigious tournament in 2025. The model was able to pick up the winner in 2024. The result also finds that the most important features in determining the winner of the tournament are shooting guard height, small …

Analyzing The Declining Role Of Starting Pitchers In Mlb, Andrew Sferratore

Honors Projects in Information Systems and Analytics

In Major League Baseball (MLB) , the use of starting pitchers has faced major scrutiny. This study aims to discuss the declining role of starting pitchers in MLB and analyze how this affects team success during the regular season. Recent literature and statistics reveal that starting pitchers are throwing fewer innings and have lower pitch counts over the last two decades than in previous years. Attributing to this declining role are extreme spikes in the number of Tommy John (UCLR) surgeries, newly added rule changes, and the introduction of new philosophical approaches. Data from the 2024 regular season regarding starting …

Analyticity And Supershift With Regular Sampling, Fabrizio Colombo, Irene Sabadini, Daniele C. Struppa, Alain Yger

Mathematics, Physics, and Computer Science Faculty Articles and Research

The notion of supershift (in itself a generalization of the notion of superoscillation arising in quantum mechanics) expresses the fact that the sampling of a function in an interval allows to compute the values of the function far from the interval. In this paper, we study the relation between supershift and real analyticity. We use a classical result due to Serge Bernstein to show that real analyticity for a complex-valued function implies a strong form of supershift. On the other hand, we use a parametric version of a result by Leonid Kantorovitch to show that the converse is not true. …