Digital Commons Network^™

Math 305-102: Statistics-Technology, G. Kariuki

Mathematical Sciences Syllabi

No abstract provided.

Math 322-004: Diff Eqns For Tech, P. Milojevic

Mathematical Sciences Syllabi

No abstract provided.

Math 331-004: Intro Partial Diff Eq, V. Matveev

Mathematical Sciences Syllabi

No abstract provided.

Math 332-002: Intro Complex Variables, T. Nguyen

Mathematical Sciences Syllabi

No abstract provided.

Math 333: Probability & Statistics, P. Natarajan

Mathematical Sciences Syllabi

No abstract provided.

Math 335-002: Vector Analysis, C. Turc

Mathematical Sciences Syllabi

No abstract provided.

Math 451-H04: Methods Appl Math Ii, L. Kondic

Mathematical Sciences Syllabi

No abstract provided.

Math 477-102: Stochastic Processes, D. Horntrop

Mathematical Sciences Syllabi

No abstract provided.

Hss 404-006: Biotechnology And The Law, Mary X. Mitchell

History Syllabi

No abstract provided.

Hss 404-H02: History Seminar:Movement Of Migrants And Refugees In The Eastern Mediterranean, Elektra Kostopoulou

History Syllabi

No abstract provided.

Hss 404-002: History Seminar:Movement Of Migrants And Refugees In The Eastern Mediterranean, Elektra Kostopoulou

History Syllabi

No abstract provided.

Hist 214-004: Technology And Culture In U.S. History, Matthew Friedman

History Syllabi

No abstract provided.

Hist 213-102: The 20th Century World, Elektra Kostopoulou

History Syllabi

No abstract provided.

Hist 372-002:Contemporary Europe, Svanur Petursson

History Syllabi

No abstract provided.

Math 615-102: Approaches To Quantitative Analysis In The Life Sciences, Gareth Russell

Biology Syllabi

No abstract provided.

Biol 340-102, 104: Mammalian Physiology, Edward Sanchez

Biology Syllabi

No abstract provided.

Pushing The Boundaries Of Large Language Models: Innovations And Limitations In Nlp, Finance, And Mathematics, A M Muntasir Rahman

Dissertations

Large Language Models (LLMs) have emerged as transformative tools across a spectrum of domains, yet their practical deployment reveals a blend of remarkable potential and notable limitations. This research explores innovative methodologies to extend the capabilities of LLMs while addressing critical challenges in their evaluation and application. By leveraging rule-based approaches, the in-context learning capabilities of LLMs, and human-in-the-loop validation across three focused studies, this research introduces robust strategies for dataset synthesis, model enhancement, and model assessment in three distinct domains: natural language processing, financial sentiment analysis, and mathematical reasoning

The first study proposes an efficient data augmentation framework, EASE, …

Game-Based Learning With Augmented Reality, Kantida Nanon

Dissertations

Augmented Reality (AR) technology provides an effective and controllable learning environment, especially for complicated, potentially dangerous, or critical content. Effective implementation of interaction and platforms for educational tools can maximize learner engagement and optimize the learning experience. This dissertation explores the potential of AR technology to enhance learning experiences across multiple platforms and subject areas, particularly within chemistry and mathematics. The dissertation is grounded in the field of Human-Computer Interaction (HCI) and explores the potential of AR to increase learner engagement, improve learning outcomes, and transform traditional pedagogical practices.

The dissertation is divided into two parts. The first part examines …

First-Principles Study Of Ferroelectric Properties And Co2 Reduction Reaction Capabilities In Two-Dimensional Monolayers And Heterostructures, Mo Li

Dissertations

Two-dimensional (2D) materials hold significant potential for CO₂ reduction reactions (CO₂RR) due to their high surface-to-volume ratio. However, achieving high selectivity for desired products and overcoming limitations posed by scaling relationships remain challenging. Recent studies suggest that ferroelectric (FE) materials with switchable out-of-plane polarization (OOP) can effectively tune the adsorption behavior, thermodynamics, and kinetics of CO₂RR, offering promising solutions to these challenges. Using density functional theory (DFT) and the Berry phase approach, this work expands the family of 2D ferroelectrics by theoretically identifying Y₂CO₂, Y₂CS₂, and Sc …

Determination Of Electrochemical Parameters For Predicting Reaction Mechanism And Algorithmic Approaches To Pain Assessment, Huize Xue

Dissertations

This dissertation introduces novel advancements in electrochemical kinetics and pain assessment, structured into two main parts. The first part focuses on the comprehensive analysis of the kinetic and mechanistic aspects of electrochemical reactions, utilizing a combination of experimental techniques and simulation methods. A new software tool, Envismetrics, was developed using Python to facilitate the analysis of complex electrochemical data, including cyclic voltammetry (CV), chronoamperometry (CA), and hydrodynamic voltammetry (HDV). The software was rigorously tested and validated with well-characterized redox systems such as the ferricyanide/ferrocyanide couple, dimethylamine borane (DMAB), and Per- and Polyfluoroalkyl Substances (PFAS). It was successfully used to determine …

Gas-Generating Reactive Materials: Design, Evaluation And Effect Of Morphology On Their Ignition And Combustion, Purvam Mehulkumar Gandhi

Dissertations

This work investigates optimizing gas-generating reactive materials, focusing on particle morphology's effect on ignition and combustion behavior. Metal-based gas-generating energetic materials (EMs) are promising alternatives to replace traditional CHNO compounds. Design of advanced metal-based EMs requires consistent ways of evaluating how their characteristics affect their ignition and combustion. Many relevant evaluation approaches exist; however, the results may be difficult to compare directly across different studies. Commonly, experimentalists ignite metal-based EMs in enclosed chambers and report pressures, P. However, direct comparison of these pressures is hindered by variations in the chamber volume, V, and the EM mass, m. To standardize the …

Ensemble Learning Models For Large-Scale Time Series Forecasting In Supply Chain, Minjuan Zhang

Dissertations

Machine learning and AI techniques are transforming supply chain forecasting, driven by the expanding availability of data assets. These advanced methods offer powerful opportunities to optimize management processes, reduce operational costs, and enhance strategic decision-making, which is crucial for enterprise success. However, conventional statistical approaches, such as Autoregressive Integrated Moving Average Models (ARIMA), dynamic regression, and Unobserved Component Models (UCMs)—which have long dominated time series forecasting—often fall short in accuracy and scalability. These traditional models face limitations in batch processing, handling large-scale data, addressing uncertainty-induced disruptions, and synchronizing demand-supply scenarios.

To address these challenges, a novel class of AI-powered ensemble …

Knowledge Diffusion In Networks Of Artificial Learners, Ehsan Beikihassan

Dissertations

The dissertation draws inspiration from the topic of peer learning in the social sciences and the study of information dissemination and knowledge diffusion in network science. In particular, it introduces and studies a setting involving a population or network of artificial learners, with the objective of optimizing aggregate performance measures under constraints on training resources. In this context, natural knowledge diffusion processes in networks of interacting artificial learners are studied. The term "natural" refers to processes that emulate human peer learning, where the internal state and learning processes of students remain largely opaque, and the main degree of freedom lies …

Interactive Visualization Workflows For Mitigating Analytical Uncertainty, Kaustav Bhattacharjee

Dissertations

This dissertation takes a process-centric and stakeholder-first perspective for handling analytical uncertainty: the form of uncertainty that confronts data analysts' insight-generation processes in high-consequence decision-making scenarios. The cost of an incorrect decision when data is used for movie recommendations as opposed to when personal data is used to drive insights or when data-driven modeling is used to drive real-time decisions for maintaining the health of a grid are vastly different in terms of consequences. This dissertation looks at analytical uncertainty in two real-world scenarios: i) how sensitive information leakage can be prevented during the open data release process with data …

Decomposition Of Diisopropyl Methylphosphonate (Dimp) Exposed To Elevated Temperatures And Combustion Products Of Reactive Materials, Elif Irem Senyurt

Dissertations

The safe and efficient destruction of chemical weapon agents (CWAs) stockpiles remains a critical global challenge. Understanding the thermophysical properties, decomposition mechanisms, and interactions of CWAs with their environment is crucial for developing effective strategies to mitigate their threats. Diisopropyl methylphosphonate (DIMP) and Dimethyl methylphosphonate are commonly used nerve agent surrogates. This dissertation focuses on DIMP and DMMP, investigates their properties and behavior under conditions relevant to prompt defeat scenarios.

The thermophysical properties of DIMP and DMMP were experimentally characterized, including vapor-liquid surface tension (3-60 °C) and viscosities of neat and aqueous solutions. Results revealed significant non-ideal behavior in aqueous …

Visual Analytic Techniques For Interpretable Algorithmic Ranking Systems, Jun Yuan

Dissertations

Rankings have a profound impact on the increasingly data-driven society. From leisurely activities like the movies to watch, the restaurants to patronize; to highly consequential decisions, like making educational and occupational choices or getting hired by companies— these are all driven by sophisticated yet mostly opaque algorithmic rankers. A small change in how these rankers order the data items can have profound consequences, like deterioration of the prestige of a university or a job applicant missing out on being on the list of the top candidates for an organization. These scenarios necessitate data-driven and human-centered innovation to make rankers accessible, …

Beyond Status Quo: Why Is Transdisciplinary Communication Instrumental In Disruptive Innovation? [Peer-Review Journal], James Lipuma, Cristo Leon

STEM for Success Resources

Explore "Beyond Status Quo: Why is Transdisciplinary Communication Instrumental in Innovation?" by James Lipuma and Cristo Leon. This groundbreaking research highlights the role of transdisciplinary communication in overcoming disciplinary silos, fostering collaboration, and driving disruptive innovation. Learn how these strategies enable systemic change, address societal challenges, and create impactful solutions. Published in the Journal of Systemics, Cybernetics, and Informatics (Vol. 22, No. 7, 2024). Read more about the transformative potential of inclusive research environments and collaborative frameworks.

Surveying The Role Of Visual Analytics In Human-Machine Teaming, Naga Datha Saikiran Battula

Theses

Humans and machines both possess their unique capabilities and have their strengths and weaknesses, which can be complementary to one another and allow them to achieve a common goal. Teaming in the modern era involves text prompts, voice commands, gesture recognition, touch interfaces, and the latest visualization techniques that allow parties/agents to interact. Communication through visualization plays a vital role in allowing robust insights to be gained through a glance. Using visualization as a medium between humans and machines can increase the communication bandwidth. Human-machine teaming has witnessed much progress, with many theories and practical examples emerging. In the report, …

Automated Segmentation Of The Ulnar Nerve In Mri Using Deep Learning Techniques, Akhil Nagulapalli

Theses

Cubital Tunnel Syndrome (CuTS), a condition caused by compression of the ulnar nerve, results in numbness, tingling, pain, and even muscle atrophy, affecting fine motor skills and diminishing patient quality of life. Accurate diagnosis of CuTS is challenging, as current diagnostic methods—including clinical exams, nerve conduction studies, and unaided MRI—often lack the precision to reliably identify the nerve and detect compression in its early stages. Deep learning-based segmentation offers a promising solution, enabling precise and automated identification of nerve structures in MRI images, which could significantly improve diagnostic accuracy and support timely intervention.

A novel deep learning model for segmenting …

Advancing Prediction Of Stimulant Medication Misuse Through Graph Representation Learning, Hamid Razavi

Theses

The misuse of stimulant prescription medications poses a significant and escalating public health concern in the United States, particularly among young adults. Addressing this issue requires sophisticated methodologies capable of uncovering complex patterns and relationships in data. Geometric Deep Learning, a paradigm designed to analyze data with non-Euclidean structures, has achieved remarkable success across various domains, offering a powerful framework for tackling complex graph structure data challenges.

This study leverages Graph Convolutional Networks (GCNs) to predict the likelihood of stimulant medication misuse using data from the National Survey on Drug Use and Health (NSDUH). Individuals are represented as nodes in …