Digital Commons Network^™

Vision-Language Integration For Enhanced Locomotion Mode Prediction, Ehsan Ahmadi

LSU Master's Theses

Wearable exoskeletons offer significant potential in enhancing human mobility in industrial environments. However, their adaptability to dynamic, task-intensive settings presents challenges, especially in accurately predicting locomotion modes such as ladder climbing, stair navigation, low-space movement, and obstacle navigation. This research proposes a multimodal framework that integrates visual data and speech commands to improve locomotion mode prediction in unpredictable environments. Multimodal data was collected using smart glasses, capturing both the user’s perspective (field-of-view, FOV) and voice during locomotion tasks. State-of-the-art models—CLIP, ImageBind, and GPT-4o—process these visual and linguistic inputs to predict locomotion activities. The models were evaluated in zero-shot and fine-tuned …

Development Of Machine Learning-Based Tool For Prediction Of Long-Term Field Performance Of Asphalt Concrete Overlays In A Hot And Humid Climate, Elise Mansour

LSU Doctoral Dissertations

The performance of pavement plays a critical role in Maintenance, Rehabilitation and Reconstruction (MR&R) for highway agencies. Reliable and accurate estimation of pavement performance can be instrumental in prioritization of the limited resources and funding for highway agencies. The latter requires robust prediction models that can handle large-scale, real-world data and can forecast pavement performance in the long run. Unfortunately, the traditional performance prediction models have raised concerns regarding their efficiency and accuracy. It is because these models were based on a limited number of explanatory variables, were not up-to-date and were designed for forecasting short-term (up to five years) …

Pooled Analysis Of Physical Activity, Sedentary Behavior, And Sleep Among Children From 33 Countries, Kar Hau Chong, Thomas Suesse, Penny L. Cross, Sarah T. Ryan, Eivind Aadland, Oluwayomi Aoko, Ankhmaa Byambaa, Valerie Carson, Jean Philippe Chaput, Hayley Christian, Dylan P. Cliff, Marieke De Craemer, Clarice Maria De Lucena Martins, Christine Delisle Nyström, Catherine E. Draper, Asmaa El Hamdouchi, Alex Antonio Florindo, Hongyan Guan, Amy S. Ha, Najmeh Hamzavi Zarghani, Kylie D. Hesketh, Mohammad Sorowar Hossain, Jajat Jajat, Thanh Kim, Denise Koh, Anna V. Kontsevaya, Nicholas Kuzik, Marja H. Leppänen, Marie Löf, Himangi Lubree, Kim Meredith-Jones, Tawonga W. Mwase-Vuma, Johan Y.Y. Ng, Rachel Novotny

Population and Public Health Science Faculty Publications

Importance: The prevalence estimates of physical activity, sedentary behavior, and sleep (collectively known as movement behaviors) in 3-and 4-year-old children worldwide remains uncertain. Objective: To report the proportion of 3-and 4-year-old children who met the World Health Organization guidelines for physical activity, sedentary behavior, and sleep across 33 countries. Design, Setting, and Participants: Pooled analysis of data from 14 cross-sectional studies (July 2008 to September 2022) identified through systematic reviews and personal networks. Thirty-Three countries of varying income levels across 6 geographical regions. Each study site needed to have at least 40 children aged 3.0 to 4.9 years with valid …

Mapping Gender And Territory: The Continent Allegories On European World Maps From The Late Middle Ages To The Renaissance Until The Early Enlightenment, Katharina Eva Oehmichen

Comparative Woman

The four-part set of female continent allegories, which originated in the early Renaissance, was born out of a European need for a new vehicle of thought that served the functions of representing imperial expansion and the superiority of Western science and civilization, while gratifying the curiosity of the readers about newly discovered lands at the same time. The shift from the Christian symbolic tradition of the representation of the continents on world maps mirrored the shift in Western perception and knowledge of the world and Europe’s position in it. Apart from geo-political and imperial agendas that might have favored a …

Genomic Analysis Of Intracranial And Subcortical Brain Volumes Yields Polygenic Scores Accounting For Variation Across Ancestries, Luis M. García-Marín, Adrian I. Campos, Santiago Diaz-Torres, Jill A. Rabinowitz, Zuriel Ceja, Brittany L. Mitchell, Katrina L. Grasby, Jackson G. Thorp, Ingrid Agartz, Saud Alhusaini, David Ames, Philippe Amouyel, Ole A. Andreassen, Konstantinos Arfanakis, Alejandro Arias-Vasquez, Nicola J. Armstrong, Lavinia Athanasiu, Mark E. Bastin, Alexa S. Beiser, David A. Bennett, Joshua C. Bis, Marco P.M. Boks, Dorret I. Boomsma, Henry Brodaty, Rachel M. Brouwer, Jan K. Buitelaar, Ralph Burkhardt, Wiepke Cahn, Vince D. Calhoun, Owen T. Carmichael, Mallar Chakravarty, Qiang Chen, Christopher R.K. Ching, Sven Cichon

Clinical Research Faculty Publications

Subcortical brain structures are involved in developmental, psychiatric and neurological disorders. Here we performed genome-wide association studies meta-analyses of intracranial and nine subcortical brain volumes (brainstem, caudate nucleus, putamen, hippocampus, globus pallidus, thalamus, nucleus accumbens, amygdala and the ventral diencephalon) in 74,898 participants of European ancestry. We identified 254 independent loci associated with these brain volumes, explaining up to 35% of phenotypic variance. We observed gene expression in specific neural cell types across differentiation time points, including genes involved in intracellular signaling and brain aging-related processes. Polygenic scores for brain volumes showed predictive ability when applied to individuals of diverse …

Probabilistic Well-Posedness For The Nonlinear Wave Equation On B2 × T, Aynur Bulut

Faculty Publications

We establish probabilistic well-posedness results for the subcubic nonlinear wave equation, posed on the domain B2 × T, with randomly chosen initial data having radial symmetry in the B2 variable, and with vanishing Dirichlet boundary conditions on ∂B2 × T.

Prediction Of Individual Weight Loss Using Supervised Learning: Findings From The CalerieTm 2 Study, Christina Glasbrenner, Christoph Höchsmann, Carl F. Pieper, Paulina Wasserfurth, James L. Dorling, Corby K. Martin, Leanne M. Redman, Karsten Koehler

Clinical Research Faculty Publications

Background: Predicting individual weight loss (WL) responses to lifestyle interventions is challenging but might help practitioners and clinicians select the most promising approach for each individual. Objective: The primary aim of this study was to develop machine learning (ML) models to predict individual WL responses using only variables known before starting the intervention. In addition, we used ML to identify pre-intervention variables influencing the individual WL response. Methods: We used 12-mo data from the comprehensive assessment of long-term effects of reducing intake of energy (CALERIETM) phase 2 study, which aimed to analyze the long-term effects of caloric restriction on human …

Deephive: A Reinforcement Learning Approach For Automated Discovery Of Swarm-Based Optimization Policies, Eloghosa Ikponmwoba, Opeoluwa Owoyele

Faculty Publications

We present an approach for designing swarm-based optimizers for the global optimization of expensive black-box functions. In the proposed approach, the problem of finding efficient optimizers is framed as a reinforcement learning problem, where the goal is to find optimization policies that require a few function evaluations to converge to the global optimum. The state of each particle within the swarm is defined as its current position and function value within a design space, and the particles learn to take favorable actions that maximize the reward, which is based on the final value of the objective function. The proposed approach …

Research Gaps And Priorities For Quantitative Microbial Risk Assessment (Qmra), Kerry A. Hamilton, Joanna Ciol Harrison, Jade Mitchell, Mark Weir, Marc Verhougstraete, Charles N. Haas, A. Pouyan Nejadhashemi, Julie Libarkin, Tiong Gim Aw, Kyle Bibby, Aaron Bivins, Joe Brown, Kara Dean, Gwyneth Dunbar, Joseph N.S. Eisenberg, Monica Emelko, Daniel Gerrity, Patrick L. Gurian, Emma Hartnett, Michael Jahne, Rachael M. Jones, Timothy R. Julian, Hongwan Li, Yanbin Li, Jacqueline Mac Donald Gibson, Gertjan Medema, J. Scott Meschke, Alexis Mraz, Heather Murphy, David Oryang, Emmanuel De Graft Johnson Owusu-Ansah, Emily Pasek, Abani K. Pradhan

Faculty Publications

The coronavirus disease 2019 pandemic highlighted the need for more rapid and routine application of modeling approaches such as quantitative microbial risk assessment (QMRA) for protecting public health. QMRA is a transdisciplinary science dedicated to understanding, predicting, and mitigating infectious disease risks. To better equip QMRA researchers to inform policy and public health management, an Advances in Research for QMRA workshop was held to synthesize a path forward for QMRA research. We summarize insights from 41 QMRA researchers and experts to clarify the role of QMRA in risk analysis by (1) identifying key research needs, (2) highlighting emerging applications of …

Learning Problems Related To Stochastic Differential Equations, Jinpu Zhou

LSU Doctoral Dissertations

Stochastic differential equations (SDEs) are essential for modeling systems influenced by both deterministic dynamics and random fluctuations, with applications in a wide variety of disciplines. This thesis develops a Bayesian framework for nonparametric learning in SDEs, addressing key challenges in inference, particularly when dealing with complex systems and incomplete data. The thesis begins by establishing a theoretical foundation in optimization over Hilbert spaces, including a generalized representer theorem to address infinite-dimensional optimization problems encountered in nonparametric inference. Building on this, we introduce a Bayesian framework with shrinkage priors to learn drift functions from high-frequency data. Bayesian approach incorporates low-cost sparse …

Resource Harvesting For Parallel Functions In Serverless Workflows, Peiman Fotouhi

LSU Master's Theses

In the rapidly evolving landscape of cloud computing, serverless architectures have gained attention for their scalability and cost-effectiveness. This thesis aims to introduce a novel approach to maximize resource utilization in serverless environments through the concept of harvesting idle resources within Directed Acyclic Graph (DAG)-based workloads. Our proposed solution targets resource harvesting at parallel stages by utilizing Machine Learning models to accurately harvest or accelerate serverless functions. Additionally, we present a scheduling algorithm specifically designed to address the unique requirements of DAG workloads in cloud environments.

The framework leverages dynamic resource allocation techniques to identify and exploit idle resources within …

Improving Rice Yield Using Sensor Based Nitrogen Application And Best Management Practices (Bmps) In Rice-Soybean And Rice-Crawfish Rotational Systems, Charles Darnall

LSU Master's Theses

Sustainable rice farming requires effective nutrient management and conservation practices to maintain high yields and minimize environmental impact. Implementing best management practices (BMPs) promotes lower fertilizer application rates, increased N use efficiency, improves soil health, and water quality. Demonstration fields established in two different rotational cropping systems (rice-soybean and rice-crawfish) in Kaplan, LA in 2023. In each rotation system, the benefits of BMPs (sensor based mid-season N management and cover cropping) compared to farmer’s practice (FP) were evaluated using yield, yield components, soil and water quality as metrics. The rice-soybean fields in 2023 and 2024 were both planted with PVL03 …

Cp Decomposition Initialization Schemes For Speeding-Up Of Convolutional Neural Networks, Mollee M. Swift

LSU Master's Theses

While machine learning and convolutional neural networks (CNNs) are making strides, a persistent effort remains to optimize classification techniques and target redundancies from a large number of parameters naturally present in CNNs. While CNNs have become more accessible across machine learning, the aim is to make their use optimal for central processing unit (CPU) schemes with varying degrees of computational power. Tensor decomposition methods, specifically canonical polyadic (CP) decomposition, look to reduce parameters by compressing specific layers in a CNN. However, they have certain inconsistencies, and their full potential remains untouched as decomposition research is endless, with many facets one …

Coloring Trivalent Graphs: A Defect Tft Approach, Amit Kumar

LSU Doctoral Dissertations

We show that the combinatorial matter of graph coloring is, in fact, quantum in the sense of satisfying the sum over all the possible intermediate state properties of a path integral. In our case, the topological field theory (TFT) with defects gives meaning to it. This TFT has the property that when evaluated on a planar trivalent graph, it provides the number of Tait-Coloring of it. Defects can be considered as a generalization of groups. With the Klein-four group as a 1-defect condition, we reinterpret graph coloring as sections of a certain bundle, distinguishing a coloring (global-sections) from a coloring …

Effects Of Longitudinal Training Walls On Two-Dimensional Flow Structure, Sediment Transport, And Bed Aggredation Using Numerical Methods And Sediment Core Analyses In Grand River, Michigan, Sumaiya Tul Siddique

LSU Doctoral Dissertations

Longitudinal training walls (LTWs) are structures oriented parallelly with the main flow and separate the channel into two sub-channels. One channel acts as the main navigational channel and the other is considered a secondary side channel. The long-term effects of the LTWs on morphodynamics and deposition remain poorly understood. This study investigated the long-term effects of the LTWs on flow, sediment transport and long-term deposition of the Grand River using numerical approaches and sediment core analysis. The effects of LTWs on 2D flow dynamics and sediment transport were investigated by using numerical approaches. The long-term deposition under the influence of …

The Role Of Siamese In G2 Checkpoint Regulation In Arabidopsis Thaliana, Martha A. Schwall

LSU Doctoral Dissertations

Cyclin-cyclin dependent kinase (Cyclin-CDK) pairs regulate progression through the checkpoints of the cell cycle. They bind with some specificity and must be bound to for their regulative properties to be active. The presence of cyclin-CDKs is conserved throughout eukaryotes, while differences in specifics exist between yeast, animals, and plants. In Arabidopsis, SIAMESE (SIM) is a CDK inhibitor that restricts progression through mitosis. Overexpression of SIM is required for endoreplication in Arabidopsis trichomes, and overexpression of SIM results in increased endoreplication in leaf epidermal cells. SIM is expressed at high levels in the root elongation zone, where it plays a …

Exploring Pre-Service Teachers' Experiences With Emerging Technologies In Secondary Education: A Phenomenological Study, Richelle M. Decuir

LSU Doctoral Dissertations

This research was an investigation of the role of emerging technologies in preparing pre-service teachers to engage with secondary learners effectively. A phenomenological approach was utilized to explore how instructional design models and constructivist learning theories shape technology training in two Southeastern public universities. The research was focused on pre-service teachers' experiences developing digital competencies, integrating instructional design, and utilizing emerging technologies in educational settings. Data was collected through semi-structured interviews and demographic surveys to understand the participants’ perspectives comprehensively. The study findings revealed that while pre-service teachers recognize the value of technology in enhancing student engagement and supporting personalized …

Enhancing Writing By Increasing Metacognitive Skills Through Socratic Seminars In A High Poverty Middle School Social Studies Classroom: An Intrinsic Case Study, Kirsten Hickingbottom

LSU Doctoral Dissertations

The purpose of this intrinsic case study was to better understand the role metacognitive skills play within classroom settings to increase students’ writing through Socratic Seminars. A predominant question guided this study: Can improving student metacognition through the repetitive use of questioning techniques in Socratic Seminars lead to improved writing skills? This question, along with four additional research questions guided the research. There is a need for more activities that promote metacognition due to its correlation with academic achievement, critical thinking skills, problem-solving abilities, and perceived self-efficacy (Keus, 2020). The significance of this study was to examine a method of …

Modern Lakeshore Processes And Historic Lake And Glacial Changes In Taylor Valley, Antarctica, Michael Stanley Stone

LSU Doctoral Dissertations

Taylor Valley, which opens to the Ross Sea, is one of several ice-free valleys comprising the McMurdo Dry Valleys, Antarctica. The valley is home to several close-basin, perennially ice-covered lakes, whose lake levels largely reflect a balance between meltwater inputs and lake ice ablation; both being driven by climate. A dynamic environment exists where the lake ice covers meet the lakeshore, with the ice grounding to the substrate throughout the cold and dark of winter, and melting to form a perimeter of thin-to-no ice around the lake during the summer. These marginal freeze-thaw regions are known as moats. Here, physical, …

Multiscale & Multiphysics Model Of Damage In Lithium-Ion Batteries, Edris Akbari

LSU Doctoral Dissertations

The demand for safer and more reliable lithium-ion batteries (LIBs) in electric vehicles has increased significantly, especially in addressing mechanical failures like internal short circuits (ISCs), which can lead to thermal runaway. This research investigates the mechanical behavior and damage mechanisms in lithium-ion pouch cells through a multiscale and multiphysics approach, focusing on Lithium Nickel Manganese Cobalt Oxide (NMC) and Lithium Iron Phosphate (LFP) batteries.

The study involved quasi-static and dynamic mechanical tests, including indentation and high-speed penetration, to evaluate stress-strain responses and fracture behaviors. Anisotropic mechanical properties were measured for individual battery components (anode, cathode, separator, and shell) using …

Pricing Variance Swaps For The Discrete Bn-S Model, Semere Gebresilasie

Journal of Stochastic Analysis

No abstract provided.

Analyzing The Catfish And Trout Competitiveness Trends And Potential Trade Within The Us And Global Aquaculture Industry: A Gravity Model Approach, Diana Marisol Amaya Discua

LSU Master's Theses

The United States is one of the major seafood product importers in the world. Despite the increases in global seafood and aquaculture consumption over the past decades, there is limited information on the trade dynamics of this sector. To address this gap, this study uses an augmented gravity model to evaluate the factors that have influenced catfish and trout imports into the United States. The analysis was conducted using the Poisson Pseudo Maximum Likelihood (PPML) estimator to address issues of heteroskedasticity, autocorrelation, and zero trade flows. The results highlight the importance of Gross Domestic Product (GDP) and exchange rates in …

Examination Of Musical Sophistication And Working Memory Correlates In High School Students, Abby South

LSU Doctoral Dissertations

Abstract

The purpose of this study was to examine the relationship between musical engagement and cognitive ability in high school students. While musicians’ brains have long been of interest to those who study experience-dependent neuroplasticity, a lack of adolescent data grossly limits our understanding of the relationship between music training and observed neuroplastic changes in the brain. Advances in psychometric testing and brain imaging reveal relationships among musical expertise, changes in brain function, structure, and connectivity, and enhanced cognitive ability, but categorical groupings of dichotomously defined musicians and non-musicians prevent nuanced understanding of these complex relationships. In this study, 83 …

Effect Of Electronegativity On Environmentally Persistent Free Radical (Epfr) Formation, Persistence, And Toxicity On Transition Metal Oxides, Syed Monjur Ahmed

LSU Doctoral Dissertations

Environmentally persistent free radicals (EPFRs) are a group of emerging environmental pollutants. Owing to their resonance structures, EPFRs have significantly higher lifetimes than typical free radicals, and can induce the formation of reactive oxygen species (ROS) known to cause pulmonary and cardiovascular dysfunction. EPFRs form by the adsorption of organic precursors on a transition metal oxide (TMO) surface and exhibit enough stability to cause negative health and environmental effects. While aromatic and substituted aromatic precursors form EPFRs on TMO surface at elevated temperatures and numerous substituted organics are ubiquitous in various industrial processes and household products, studies have largely overlooked …

Rootzone Soil Moisture Dynamics Using Terrestrial Water-Energy Coupling, Vinit Sehgal, Binayak P. Mohanty, Rolf H. Reichle

Faculty Publications

A lack of high-density rootzone soil moisture (θRZ) observations limits the estimation of continental-scale, space-time contiguous θRZ dynamics. We derive a proxy of daily θRZ dynamics — active rootzone degree of saturation (SRZ) — by recursive low-pass (LP) filtering of surface soil moisture (θS) within a terrestrial water-energy coupling (WEC) framework. We estimate the LP filter parameters and WEC thresholds for the piecewise-linear coupling between SRZ and evaporative fraction (EF) at remote sensing and field scale over the Contiguous U.S. We use θS from the Soil Moisture Active-Passive (SMAP) satellite and 218 in-situ stations, with EF from the Moderate Resolution …

On The Pathogenesis Of Obesity: Causal Models And Missing Pieces Of The Puzzle, Faidon Magkos, Thorkild I.A. Sørensen, David Raubenheimer, Nikhil V. Dhurandhar, Ruth J.F. Loos, Anja Bosy-Westphal, Christoffer Clemmensen, Mads F. Hjorth, David B. Allison, Gary Taubes, Eric Ravussin, Mark I. Friedman, Kevin D. Hall, David S. Ludwig, John R. Speakman, Arne Astrup

Clinical Research Faculty Publications

Application of the physical laws of energy and mass conservation at the whole-body level is not necessarily informative about causal mechanisms of weight gain and the development of obesity. The energy balance model (EBM) and the carbohydrate–insulin model (CIM) are two plausible theories, among several others, attempting to explain why obesity develops within an overall common physiological framework of regulation of human energy metabolism. These models have been used to explain the pathogenesis of obesity in individuals as well as the dramatic increases in the prevalence of obesity worldwide over the past half century. Here, we summarize outcomes of a …

A Quantitative Adverse Outcome Pathway For Embryonic Activation Of The Aryl Hydrocarbon Receptor Of Fishes By Polycyclic Aromatic Hydrocarbons Leading To Decreased Fecundity At Adulthood, Jon A. Doering, Justin Dubiel, Eric Stock, Cameron H. Collins, Ian Frick, Hunter M. Johnson, Christopher M. Lowrey-Dufour, Justin G.P. Miller, Zhe Xia, Gregg T. Tomy, Steve Wiseman

Faculty Publications

Quantitative adverse outcome pathways (qAOPs) describe the response–response relationships that link the magnitude and/or duration of chemical interaction with a specific molecular target to the probability and/or severity of the resulting apical-level toxicity of regulatory relevance. The present study developed the first qAOP for latent toxicities showing that early life exposure adversely affects health at adulthood. Specifically, a qAOP for embryonic activation of the aryl hydrocarbon receptor 2 (AHR2) of fishes by polycyclic aromatic hydrocarbons (PAHs) leading to decreased fecundity of females at adulthood was developed by building on existing qAOPs for (1) activation of the AHR leading to early …

Prescribed Fire Regimes Influence Responses Of Fungal And Bacterial Communities On New Litter Substrates In A Brackish Tidal Marsh, Viet Q. Dao, Crystal N. Johnson, William J. Platt

Faculty Publications

Processes modifying newly deposited litter substrates should affect fine fuels in fire-managed tidal marsh ecosystems. Differences in chemical composition and dynamics of litter should arise from fire histories that generate pyrodiverse plant communities, tropical cyclones that deposit wrack as litter, tidal inundation that introduces and alters sediments and microbes, and interactions among these different processes. The resulting diversity and dynamics of available litter compounds should affect microbial (fungal and bacterial) communities and their roles in litter substrate dynamics and ecosystem responses over time. We experimentally examined effects of differences in litter types produced by different fire regimes and litter loads …

Louisiana Agriculture Magazine, Fall 2024

Louisiana Agriculture

Questions drive the science behind the LSU AgCenter. Researcher Erin Oberhaus wanted to find data to help educate Louisiana horse owners about the use of an important medication, and a water quality specialist wondered if noneconomic bodies of water, which include agricultural ponds, might help people generate electricity. The research projects that grew from their questions are detailed in this issue of Louisiana Agriculture.

Also in this issue, scientists share the results from important soybean studies, and the AgCenter farm to school team shares success stories from school gardens across the state.

Research is at the core of the LSU …

Technician Assignment In Multi-Shift Maintenance Schedules In An Offshore Wind Farm, Md Imran Hasan Tusar, Bhaba R. Sarker

Faculty Publications

Offshore wind farms are becoming more and more important to sustainable energy strategies, yet their maintenance presents unique logistical challenges. The focus of this research is the Technician Assignment Problem (TAP) which involves a complicated and fluctuating scheduling problem. The key objective is to determine the most effective assignment of technicians to maintenance tasks to maximize operational efficiency and ensure reliable service. This study introduces a mathematical optimization model that processes numerous variables—technician availability, skill sets, and temporal constraints—to minimize unmet maintenance needs and ensure equitable workload distribution. The model is based on assumptions derived from actual operational conditions and …