Physical Sciences and Mathematics Commons^™

Research And Education In Robotics: A Comprehensive Review, Trends, Challenges, And Future Directions, Mutaz Ryalat, Natheer Almtireen, Ghaith Al-Refai, Hisham Elmoaqet, Nathir Rawashdeh

Michigan Tech Publications

Robotics has emerged as a transformative discipline at the intersection of the engineering, computer science, and cognitive sciences. This state-of-the-art review explores the current trends, methodologies, and challenges in both robotics research and education. This paper presents a comprehensive review of the evolution of robotics, tracing its development from early automation to intelligent, autonomous systems. Key enabling technologies, such as Artificial Intelligence (AI), soft robotics, the Internet of Things (IoT), and swarm intelligence, are examined along with real-world applications in healthcare, manufacturing, agriculture, and sustainable smart cities. A central focus is placed on robotics education, where hands-on, interdisciplinary learning is …

Review Of Averting The Digital Dark Age: The Future Of Digital Literary Heritage, Rebecca L. Hastings

Journal of Contemporary Archival Studies

In Averting the Digital Dark Age: How Archivists, Librarians, and Technologists Built the Web a Memory, Ian Milligan explores the efforts of technologists and information professionals to develop means of preserving information shared on the World Wide Web. The web lacks a built-in archiving function, raising fears of a “digital dark age,” an unprecedented gap in the historical record as information sharing via the web increases. Milligan tracks the development of web preservation debates and activities from the anxiety and turmoil of the early to mid-1990s to the coming of age of web preservation in the wake of the …

A Study Of Machine Learning Techniques In Solving Biochemical And Chemical Problems, Kenneth Micheal Plackowski

Chemistry and Chemical Biology ETDs

Data-driven approaches to solving problems in biology and chemistry require utilization of reliable techniques and machine learning algorithms are the modern reliable approach. This work presents three problems that involve use of supervised learning techniques when classification is the goal and unsupervised learning techniques when global data representation is the goal.

In the first problem, we demonstrate the use of unsupervised clustering techniques, self-organizing maps and K-means, to ascertain analyte detection capabilities of carbon nitride dots. In the second problem, we add scalability features to a functional group classification model applied to infrared data and evaluate its ability to inform …

Nanoporous Gold Nanoparticles And Model Membrane Systems Applied To The Study Of Lipopolysaccharide (Lps), Dhanbir Lingden

Dissertations

Lipopolysaccharide (LPS), a key structural component of Gram-negative bacteria, is a major trigger of sepsis and septic shock, the systemic inflammatory conditions responsible for millions of deaths annually. Even at concentrations as low as 1 ng/mL, LPS can activate the immune system and initiate inflammatory cascades. According to the Global Burden of Disease Study 2017, sepsis accounted for 48.9 million cases globally, with 11 million deaths, emphasizing the urgent need for improved diagnostic and therapeutic strategies.

Accurate and rapid detection of LPS is vital for early intervention and improved clinical outcomes. While traditional detection methods are still widely used, modern …

Using Linear Programming And Game Theory To Optimize The Relation Between Us And China, Junhao Su

Student Works

This paper develops an optimization model to analyze U.S.–China bilateral trade dynamics and competition in artificial intelligence (AI). First, grounded in WTO tariff limits, we formulate a linear programming model to maximize the combined trade volume and conduct a comprehensive sensitivity analysis on tariff parameters. Second, we integrate zero‑sum and non‑zero‑sum game‑theoretic frameworks to identify the Nash equilibria governing both trade negotiations and technological rivalry. The model is implemented in Python using PuLP and is empirically validated with real‑world tariff data to highlight the policy relevance of the optimal solutions. Our results reveal a high concordance between the zero‑sum game …

How Multi-Scale Modeling Can Help Examine Social Determinants Of Health And Resulting Disparities, Kyoko Yoshida, Elsje Pienaar, Shalanda A. Bynum, Naomi Chesler, Mitchel J. Colebank, Jessie Heneghan, Nadra Tyus, Jasmine Miller-Kleinhenz, Bruce Y. Lee

Faculty Publications

Social determinants of health (SDOH) are the conditions in which people live, work, and play, and the wider set of factors (e.g., social and economic systems and policies) that shape a person’s daily life. SDOH can differ significantly across communities and populations, having positive impacts for some and negative impacts for others. Ultimately, this results in differences in health and disease distribution, that are known as health disparities. Despite the known impacts of SDOH and calls to characterize, address, reduce, and eliminate health disparities, they persist and, in some cases, have worsened. To address this challenge, a session at the …

Integrating Artificial Intelligence And Machine Learning Technologies Into Common Operating Picture And Course Of Action Development, C. Anthony Pfaff, Christopher John Hickey

Books, Monographs & Collaborative Studies

C. Anthony Pfaff and Christopher John Hickey, Principal Investigators

©2025 C. Anthony Pfaff. All rights reserved.

Integrating Artificial Intelligence and Machine Learning Technologies into Common Operating Picture and Course of Action Development explores the potential of artificial intelligence (AI) and machine learning to revolutionize military planning processes by enhancing situational awareness and expediting course of action development within the Joint planning process. The study delves into technical, organizational, and resource considerations that are critical for AI integration. In addition, the study highlights the importance of clean, structured data in training AI systems, addresses challenges in data collection across varying formats …

Analyzing Environmental Health: Air Pollution And Human Health Using Geospatial Data Science, Yanhong Huang

Geography ETDs

Air pollution from industrial emissions and wildfire smoke poses growing threats to public health. This dissertation employs geospatial data science to examine these challenges through three interrelated studies. The first study investigates the relationship between maternal residential exposure to industrial pollutants and low birth weight, identifying five chemicals as significant risk factors. The second study assesses the impact of industrial air pollution on lung and bronchus cancer survival, finding that exposure to 1,1,1-trichloroethane and cobalt is associated with reduced survival. The third study explores disparities in wildfire smoke PM_2.5 exposure and its association with asthma exacerbations. Results show disparities …

Re: Comment Letter For The Butte Priority Soils Operable Unit (Bpsou) 2022 Draft Final Insufficiently Reclaimed Sites Sampling: Bres No. 38 – Sister Dump Site Evaluation Summary Report (December 31, 2024), Molly Roby

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Theoretical And Experimental Investigation Of Liquid-Liquid Phase Separation: Characterizing Elastin-Like-Polypeptides, Adam D. Quintana

Chemical and Biological Engineering ETDs

This dissertation develops and validates a semi-empirical Flory–Huggins-based interaction model, combined with Cahn–Hilliard simulations, for predicting multi-component liquid–liquid phase separation (LLPS) in elastin-like polypeptide (ELP) systems. Equilibrium droplet compositions, measured using a PDMS-based microfluidic device, enabled direct parameterization of interaction coefficients. The model was applied to generate phase diagrams and assess composition dependence in ternary mixtures. Cahn–Hilliard simulations were conducted to explore potential phase morphologies under different interfacial conditions. Multi-component Lattice Boltzmann simulations were implemented to model droplet morphology evolution under varying interfacial and diffusive parameters, reproducing experimentally relevant morphologies. A three-phase wetting study revealed conditions for selective wetting and …

Re: Comment Letter For The Butte Priority Soils Operable Unit (Bpsou) 2022 Draft Final Insufficiently Reclaimed Sites Sampling: Bres No. 32 Site Evaluation Summary Report (Dated December 11, 2024), Molly Roby

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Approximating Transport Maps Using Minibatch Optimal Transport Plans, Dawson Collins

Theses and Dissertations

Optimal Transport theory (OT) has gained much attention in the past decades due to its rich mathematical content and striking connections to other areas of math. It has also naturally found a place in data science as a way to compare and transform probability measures. Unfortunately, there remain significant computational burdens that restrict its effectiveness in large-scale data regimes. In this thesis, we consider Minibatch Optimal Transport (MiniOT), which breaks up the task of computing the optimal transport plan between datasets into several smaller tasks of computing the optimal transport plan between minibatches of the datasets. After obtaining the minibatch …

Integrating Belowground Recovery Into Tropical Forest Restoration Design And Monitoring, Lauren Toro, Leland K. Werden, Shalom D. Addo-Danso, Kelly M. Andersen, Sarah Batterman, Matilde M. Bragadini, Pooja Choksi, Rebecca J. Cole, Liza S. Comita, Daniela Cusack, Daisy H. Dent, Lee H. Dietterich, Joshua B. Fisher, Katrin Fleischer, Lucia Fuchslueger, Nohemi Huanca-Nunez, Janey R. Lienau, Lindsay A. Mcculloch, Ember M. Morrissey, Jennifer S. Powers, Mareli Sánchez-Juliá, Oscar Valverde-Barrantes, Anita Weissflog, Michelle Y. Wong

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

There is growing recognition that tropical forest restoration is key for sequestering carbon and enhancing ecosystem resilience. Soils, roots, and soil biota are central to ecosystem function and services, but belowground recovery is largely overlooked in restoration monitoring frameworks. Here, we outline current understanding of the links between above- and belowground recovery in tropical forests by examining how belowground properties before and after intervention influence recovery; by evaluating whether aboveground recovery can serve as a proxy for belowground dynamics; and by proposing a blueprint for monitoring dynamic soil physical (bulk density, aggregate stability), chemical (organic matter or carbon, pH), and …

Subspace And Auxiliary Space Preconditioners For High-Order Interior Penalty Discretizations In H(Div), Will Pazner

Mathematics and Statistics Faculty Publications and Presentations

In this paper, we construct and analyze preconditioners for the interior penalty discontinuous Galerkin discretization posed in the space H(div). These discretizations are used as one component in exactly divergence-free pressure-robust discretizations for the Stokes problem. Three preconditioners are presently considered: a subspace correction preconditioner using vertex patches and the lowest-order H1-conforming space as a coarse space, a fictitious space preconditioner using the degree-p discontinuous Galerkin space, and an auxiliary space preconditioner using the degree-(p − 1) discontinuous Galerkin space and a block Jacobi smoother. On certain classes of meshes, the subspace and fictitious space preconditioners result in provably well-conditioned …

Gain Threshold Optimization Using Fano Resonance, Alina Oktiabrskaia

LSU Doctoral Dissertations

The study of resonances in electromagnetics plays a critical role in the design of optical systems. This dissertation investigates the interaction between resonance and gain in optical structures to establish a universal principle for achieving ultra-low-threshold lasing. Through the analysis of geometric symmetries, material properties, and coupling mechanisms, this research develops prototype structures applicable to a wide range of optical and electromagnetic systems. A range of models is considered, starting from a simple onedimensional string-resonator system (based on the model of H. Lamb), then advancing to two- and three-dimensional waveguide models, and culminating with a realistic high-contrast model in open …

Quantitative Boundary Doubling Estimates For Elliptic Equations, Jack Dalberg

LSU Doctoral Dissertations

We present an approach for obtaining quantitative boundary doubling inequalities for elliptic equations with Neumann boundary conditions. Carleman estimates are used to prove three-ball inequalities, which are then used to prove quantitative doubling inequalities, with bootstrapping from the interior to the boundary. This approach is illustrated by its application to the Laplace eigenvalue problem with homogeneous Neumann boundary conditions, where sharp doubling inequalities are recovered.

When then consider a equation with non homogeneous Neumann boundary conditions. By following the approach, we are able to obtain potentially sharp results. Finally, we are able to get an improvement on previously obtained results …

The Role Of The Cytosolic Chaperonin Cct In The Folding And Assembly Of G Protein Complexes, Mikaila I. Sass

Theses and Dissertations

All organisms depend on cellular signaling pathways to dictate cell growth, responses to stimuli, and all other cellular functions necessary to maintain health and survival. Many of these signals rely on G protein coupled receptors (GPCRs) and their associated heterotrimeric G proteins for signal detection and transduction. To perform this function, the three subunits of the G protein must be assembled after synthesis on the ribosome. This process requires additional proteins called molecular chaperones to assist in folding of the subunits and assembly into the G protein heterotrimer. Among these chaperones, the cytosolic chaperonin CCT plays an essential role in …

A New Approach To Stability Of Delay Differential Equations With Time-Varying Delays Via Isospectral Reduction, Quinlan Leishman

Theses and Dissertations

Understanding how time delays impact the stability of a delay differential equation is important for modeling many natural and technological systems that experience time delays. Here we introduce a new stability criterion for delay-independent stability of these equations, called intrinsic stability, showing global exponential stability for a large class of nonautonomous nonlinear systems. Our approach is able to incorporate bounded time-varying delays, including those with certain types of discontinuities. The approach we take to prove this result is novel, associating the delay differential equation with a sequence of finite-dimensional matrices of increasing size and using the graph-theoretic technique of isospectral …

Math 112 At Byu: Student Averages On Various Exam Questions, Makayla Mcleod

Theses and Dissertations

In Math 112 at BYU, exams play a crucial role in evaluating students' understanding of the course content. Traditionally, these exams are a combination of multiple-choice and free-response questions, with the latter serving as a means to assess a student's thought process while solving problems. Will students perform as well on a multiple-choice exam? Or will students' averages be higher on free-response exams? In this thesis, we test 7 hypotheses related to these questions to provide more insight into the answer. We examine other factors that may affect students' averages, including various Calculus 1 topics, modes of questioning, and difficulty …

"Chatgpt Told Me To Say It": Ai Chatbots And Class Participation Apprehension In University Students, Daisuke Akiba

Publications and Research

The growing prevalence of AI chatbots in everyday life has prompted educators to explore their potential applications in promoting student success, including support for classroom engagement and communication. This exploratory study emerged from semester-long observations of class participation apprehensions in an introductory educational psychology course, examining how chatbots might scaffold students toward active and independent classroom contribution. Four students experiencing situational participation anxiety voluntarily participated in a pilot intervention using AI chatbots as virtual peer partners. Following comprehensive training in AI use and prompt design given to the entire class, participants employed systematic consultation frameworks for managing classroom discourse trepidations. …

Creativity And Curb Cuts: Experiences In Our First Offering Of A Front End Development And Accessibility Focused Cs Course, Briana C. Bettin, Tony Garnett, Alex Gore, Andrea Llanas

Michigan Tech Publications

Students learn an abundance of technical skills while obtaining a computer science degree. The ability to develop meaningful front end user interfaces is often considered the domain of only ''more artistic'' CS students. However, for users to effectively engage with any piece of software, functional user interfaces are critical. Moreover, even among students who have front end skills, semantic and accessible design is all too often less considered. The first author piloted a ''Front End Development and Accessibility'' course this past Fall. This course teaches basic skills of front end with web and leverages key accessibility standards via WCAG. This …

A Review Of Automation In Small Satellite Operations, Joseph Melville, Andrew Narvaez, Lee Jasper

Space Dynamics Laboratory Publications

In the context of congested orbital space, the proliferation of small satellite constellations aims to enhance resilience and responsiveness in on-orbit systems. The escalating number and diversity of small satellites and the need for intersatellite coordination pose operational challenges. Chief among these challenges is that the quantity of operators needed to fully utilize these deployed systems is prohibitively large and expensive; further, operators simply may not be responsive enough to events.

Automated systems, both on-board and ground-based, offer a viable solution to streamline various operational tasks. The integration of automation mitigates the operational burden, minimizes errors originating from human intervention, …

Spike Timing-Dependent Plasticity And Random Inputs Shape Interspike Interval Regularity Of Model Stn Neurons, Thoa Thieu, Roderick Melnik

School of Mathematical & Statistical Sciences Faculty Publications

Background/Objectives: Neuronal oscillations play a key role in the symptoms of Parkinson’s disease (PD). This study investigates the effects of random synaptic inputs, their correlations, and the interaction with synaptic dynamics and spike timing-dependent plasticity (STDP) on the membrane potential and firing patterns of subthalamic nucleus (STN) neurons, both in healthy and PD-affected states. Methods: We used a modified Hodgkin–Huxley model with a Langevin stochastic framework to study how synaptic conductance, random input fluctuations, and STDP affect STN neuron firing and membrane potential, including sensitivity to refractory period and synaptic depression variability. Results: Our results show that random inputs significantly …

Seeking Structure In Complex Systems: From Feature Analysis To Space-Time Causal Discovery With Earth Science Applications, Jeffrey J. Nichol

Computer Science ETDs

Complex systems are difficult to study because of their many interacting parts, emergent phenomena, and feedback loops. These systems underpin all life on Earth. We need improved tools for seeking an understanding of them. This body of research presents my investigations into data-driven methods for understanding complex systems, including my invention of a novel causal discovery meta-algorithm for space-time gridded data. I demonstrated machine learning feature importance and causal discovery capabilities for comparing simulated and observed climate data. I developed a new benchmark for modeling space-time dynamics of locally driven phenomena and examined a prominent causal discovery algorithm. Finding that …

A Machine Learning Approach To Quantitative X-Ray Diffraction Analysis, Spencer Snow Chandler

Theses and Dissertations

X-ray Powder Diffraction (XRPD) is a powerful method in material sciences that gives insights into the atomical and crystallographic structure of a material, revealing information into the material's properties and suitability for industrial and scientific application. In geology, XRPD analysis is frequently leveraged to identify and quantify the present mineral phases in an unknown mixture. Despite it's widespread use, interpreting XRPD patterns requires highly-specialized knowledge, making the analysis largely dependent upon the background experience of the analyst. To assist experts, computational methods have been developed over the years. Some of these techniques involve fitting diffraction patterns using pseudo-Voigt functions, which …

Qder: Query-Specific Document And Entity Representations For Multi-Vector Document Re-Ranking, Shubham Chatterjee, Jeff Dalton

Computer Science Faculty Research & Creative Works

Neural IR has advanced through two distinct paths: entity-oriented approaches leveraging knowledge graphs and multi-vector models capturing fine-grained semantics. We introduce QDER, a neural re-ranking model that unifies these approaches by integrating knowledge graph semantics into a multi-vector model. QDER's key innovation lies in its modeling of query-document relationships: rather than computing similarity scores on aggregated embeddings, we maintain individual token and entity representations throughout the ranking process, performing aggregation only at the final scoring stage-an approach we call "late aggregation." We first transform these fine-grained representations through learned attention patterns, then apply carefully chosen mathematical operations for precise matches. …

Making Observations, Erik Johnson

Natural Science Faculty

After years of planning and construction, the NSF-DOE Vera C. Rubin Observatory has collected its "first light" from space, and the first images have been released to the public.

Understanding The Roots Of Swarm Intelligence In Defence To Find The Path Forward: A Scientometric Study Of Autonomous Systems, Anton Klarin, Pi-Shen Seet, Janice Jones, Michael N. Johnstone, Helen Cripps, Jalleh Sharafizad, Tony Marceddo

Research outputs 2022 to 2026

Swarm intelligence, inspired by the decentralised, adaptive and self-synchronising behaviours of natural swarms, is a pivotal component of autonomous systems, enhancing efficiency, robustness and scalability. The research in this area is nascent and interdisciplinary. To drive this important research forward, it is necessary to adopt a systems perspective on what is available in the current literature. This chapter offers a comprehensive systems perspective of the integration of swarm intelligence within the broader domain of automation, emphasising its application in the defence sector. A systems perspective of an interdisciplinary field is afforded through scientometrics. Using VOSviewer algorithms, we analysed 1706 publications …

Role Of Staphylococcus Aureus And Streptococcus Pyogenes Biofilms On The Alternation Of Cellular Immunity In Pediat-Ric Tonsillitis Patients, Shayma Ali Hussein, Taban Kamal Rasheed

Karbala International Journal of Modern Science

This study examines the effect of Staphylococcus aureus and Streptococcus pyogenes biofilms on cellular hematological parameters and distribution and phenotyping of cellular immunity in mucosal tissue of tonsils. Thirty healthy controls and fifty pediatric tonsillitis patients participated in the research. Thirty isolated S. aureus and S. pyogenes were tested for biofilm-forming capability (BFC). Hematological parameters were assessed before tonsillectomy, and 9 tonsil samples were evaluated using hematoxylin and eosin stain to investigate the histopathological alterations. Immunohistochemistry (IHC) staining was carried out for detecting dendritic cells (CD1a), neutrophils (CD15), macrophages (CD68), helper T cells (CD4), and cytotoxic T cells (CD8). Hematological …

Neurophysiology And Endocrine Responses To Hunger And Satiety Mechanisms: The Brain-Gut Crosstalk, Nour Shakir Rezaieg, Muthanna M. Awad

Karbala International Journal of Modern Science

Background: Obesity is a main public health problem which substantially increases the risk of many diseases. The complex neural circuitry controls energy homeostasis and food consumption by the incorporation of hormonal and neural signals. Circulating hormones, in specific the gut hormones, have been found to be very important in appetite regulation. These hormones transfer energy situation signs to the brain throughout three principle paths: the circulation system, activation of the vagus nerve, and direct modification of main brain regions such as the hypothalamus and brainstem. The control of food eating is not exclusively dependent on the homeostatic processes, rather it …