Digital Commons Network^™

Development Of Novel Anti-Cancer Colchicine Analogs: Synthesis And Configurational Studies, Orugbani S. Eli

Dissertations, Theses, and Capstone Projects

Colchicine, a naturally occurring alkaloid, has long been known as a potent therapeutic agent. Despite its efficacy treating gout and other inflammatory disorders, its severe toxicity, limited selectivity, and poor pharmacological profile have restricted its broader clinical application. Reported advantages of colchicine-site ligands as vascular-disrupting agents, including reduced susceptibility to multidrug resistance, have revived interest in the colchicine site on tubulin as a validated therapeutic target for cancer. This dissertation focuses on the development of new colchicine analogs, detailing the synthetic method to functionalized AC-ring derivatives, as well as the evaluation of their configurational stability and biological activity.

In Chapter …

Online Visual Query System For Real-Time Large-Scale Spatio-Temporal Data Explorations With Error Bounds, Xueqi Huang

Dissertations, Theses, and Capstone Projects

Modern datasets continue to grow in size, dimensionality, and heterogeneity, creating increasing tension between the need for responsive, interactive analysis and the computational cost of accessing, aggregating, and visualizing large volumes of data. Traditional database engines and visualization tools often assume that full data retrieval is feasible or that exact computation is necessary for meaningful insight. In practice, however, analysts frequently benefit from timely, uncertainty-aware approximations than from delayed and exact results. This thesis investigates how data summarization techniques, specifically mergeable sketches can be combined with progressive, out-of-core visualization methods to support interactive exploration of datasets that exceed main memory. …

Courts Of New York: A Visual Atlas Of The City’S Public Basketball Spaces, Nathaniel Rattner

Dissertations, Theses, and Capstone Projects

Basketball courts in New York City are recreation facilities, community anchors and part of the city’s cultural image. In the basketball capital of the world, New Yorkers are rarely more than a few blocks away from a court. The visual diversity of these courts, however, is not widely documented in systematic ways.

This project makes that diversity visible to the public, combining open data, aerial imagery and computational analysis to document this important public space across the five boroughs. It is a narrative story and digital atlas of New York City’s public basketball courts, using surface color as a way …

Searching Smarter, Connecting Better, Abby Veiman

University of Nebraska-Lincoln Libraries: White Papers

In Searching Smarter, Connecting Better, Abby Veiman reflects on her experience as a data-driven researcher to reimagine the future of research libraries. Drawing from her work as an Ameritas Research Assistant in Actuarial Science and Risk Management, she highlights how modern research is increasingly digital, analytical, and dependent on technological infrastructure rather than physical materials. Veiman argues that libraries must continue evolving beyond access to databases by strengthening technological capacity, supporting responsible AI integration, and providing clearer guidance on the effective and ethical use of emerging research tools. She emphasizes the need for libraries to serve as trusted institutions …

Analysis Method Of Impact Of Complex Electromagnetic Signals On Measurement Errors In Single-Phase Electric Energy Meters, Hongtao Shen, Chong Li, Hao Wang, Juchuan Guo, Penghe Zhang, Cong Wang

Journal of Electric Power Science and Technology

In complex electromagnetic environments, the diverse array of electromagnetic interference signals with varying modulation schemes leads to degraded performance in single-phase electric energy meters. With the random and dynamic characteristics of complex electromagnetic signals, a parametric model is established for m-sequence dynamic test signals. Subsequently, a structured measurement model for single-phase electric energy meters and a mathematical model for the impact of electromagnetic signals on single-phase electric energy meter errors are developed. The impact of electromagnetic signals on random interference in electric energy meters is then simulated. Finally, through simulation and experimental tests, the impacts of electromagnetic signals on measurement …

Peer-To-Peer Electricity Trading Method Considering Supply-Demand Relationship Tariffs And Tariff Time Period Division, Renjun Zhou, Xin Peng, Jingjie Huang, Xiaojiao Tong

Journal of Electric Power Science and Technology

A two-layer optimization model of a leader-follower game is constructed for peer-to-peer electricity trading between distributed energy power stations and industrial parks. The profit maximization of the distributed energy station is taken as the goal in the upper layer, with decision variables including tariff time period division, tariff pricing, and the charging/discharging power of on-site energy storage equipment. The difference between electricity supply and demand is used as the state variable, and a certain range of difference is used to determine the valley and peak price periods. A tariff response coefficient is designed, which depends on the demand response effect …

Iowa Waste Reduction Center Newsletter, February 2026, University Of Northern Iowa. Iowa Waste Reduction Center.

Iowa Waste Reduction Center Newsletter

Contents:

--- Cedar Rapids Linn County SWA Wins Award
--- Energy Savings Made Simple: What is An Energy Audit?
--- New IEDA Grant Helps Rural Grocers Power Up
--- Dan Nickey Gets Thermography Certified
--- You’re Invited to UNI Day of Service
--- Minor Source Emission Inventory
--- IStorm 26
--- Industry News

Coe Annual Technical Review 2025, Jianshun Zhang, Bing Dong

SyracuseCoE

No abstract provided.

Obstructive Sleep Apnea Prediction: A Comprehensive Review And Comparative Study, Thi Khanh Chi Huynh, Amonae Dabbs-Brown, Anna Jurek-Loughrey, James Mulhall, Tuan Dung Pham, Ngoc Phu Doan, Viet Hung Tran, Zichi Zhang, Xuan Hoang Nguyen, Yimeng An, Peixin Li, Phi Hung Nguyen, Thi Linh Hoang, Xinming Shi, Hans Vandierendonck, Sebastien Bailly, Jean-Louis Pépin, Thai Son Mai

Research Collection School Of Computing and Information Systems

Obstructive Sleep Apnea (OSA) is a highly prevalent sleep disorder linked to considerable public health burdens and comorbidities. However, its heterogeneous presentation and the limited accessibility of traditional diagnostic tools such as polysomnography (PSG) lead to widespread underdiagnosis. As a result, artificial intelligence (AI) approaches, including machine learning (ML) and deep learning (DL) models, have attracted attention as an alternative pathway to detection. This paper first provides a comprehensive review of AI-driven OSA diagnosis, covering different diagnosis problems, input-data types, data biases, pre-processing techniques, and model performance. We then leverage the largest clinical dataset used in OSA prediction to date, …

Physics-Informed Self-Supervised Diagnosis Of Rotating Machinery Using Latent Odes And Transformer Encoders, Md Al Amin, Mohammad Shafat Ahsan, Jannatul Maua, Mumtahina Ahmed, Kamruddin Nur

Student Publications [Scholarly]

This paper proposes a novel Physics-Informed Self-Supervised Diagnosis (PI-SSD) framework for rotating machinery fault detection, combining physical modeling, self-supervised representation learning, and uncertainty-aware classification. The architecture integrates a multi-resolution convolutional encoder, a windowed Transformer for temporal context modeling, and a latent neural ordinary differential equation (ODE) module that embeds mechanical priors, such as Jeffcott rotor dynamics, directly into the learning process. A masked segment reconstruction objective enables self-supervised pretraining using unlabeled healthy signals, while an evidential classifier head produces fault probabilities with calibrated uncertainty. We evaluate PI-SSD on two publicly available datasets, the NASA PHM’09 Gearbox dataset and the Aalto …

Duration And Properties Of The Embedded Phase Of Star Formation In 37 Nearby Galaxies From Phangs-Jwst, Lise Ramambason, Mélanie Chevance, Jaeyeon Kim, Francesco Belfiore, J. M. Diederik Kruijssen, Andrea Romanelli, Amirnezam Amiri, Médéric Boquien, Ryan Chown, Daniel A. Dale, Simthembile Dlamini, Oleg V. Egorov, Ivan Gerasimov, Simon C.O. Glover, Kathryn Grasha, Hamid Hassani, Hwihyun Kim, Kathryn Kreckel, Hannah Koziol, Adam K. Leroy, José Eduardo Méndez-Delgado, Justus Neumann, Lukas Neumann, Hsi An Pan, Debosmita Pathak, Karin Sandstrom, Sumit K. Sarbadhicary, Eva Schinnerer, Jiayi Sun, Jessica Sutter, David A. Thilker, Leonardo Ubeda

Physics and Astronomy Faculty Publications

Light reprocessed by dust grains emitting in the infrared enables the study of the physics at play in dusty embedded regions, where ultraviolet and optical wavelengths are attenuated. Infrared telescopes such as JWST have made it possible to study the earliest feedback phases, when stars are shielded by cocoons of gas and dust. Comprehending this phase is crucial for unravelling the effects of feedback from young stars that leads to their emergence and the dispersal of their host molecular clouds. Here we show that the transition from the embedded to the exposed phase of star formation is short (< 4 Myr) and sometimes almost absent (< 1 Myr) across a sample of 37 nearby star-forming galaxies covering a wide range of morphologies, from massive barred spirals to irregular dwarfs. The short duration of the dust-clearing timescales suggests a predominant role of pre-supernova feedback mechanisms in revealing newborn stars, confirming previous results on smaller samples and allowing, for the first time, a statistical analysis of their dependencies. We find that the timescales associated with mid-infrared emission at 21 μm, tracing a dust-embedded feedback phase, are controlled by a complex interplay between giant molecular cloud properties (masses and velocity dispersions) and galaxy morphology. We report relatively longer durations of the embedded phase of star formation in barred spiral galaxies, while this phase is significantly reduced in low-mass irregular dwarf galaxies. We discuss tentative trends with gas-phase metallicity, which may favor faster cloud dispersal at low metallicities.

Holographic Duality From Howe Duality, Anatoly Dymarsky, Johan Henriksson, Brian Mcpeak

Physics and Astronomy Faculty Publications

We discuss the holographic correspondence between 3d “Chern-Simons gravity” and an ensemble of 2d Narain code CFTs. Starting from 3d abelian Chern-Simons theory, we construct an ensemble of boundary CFTs defined by gauging all possible maximal subgroups of the bulk one-form symmetry. Each maximal non-anomalous subgroup is isomorphic to a classical even self-dual error-correcting code over ℤ_p × ℤ_p, providing a way to define a boundary “code CFT.” The average over the ensemble of such theories is holographically dual to Chern-Simons gravity, a bulk theory summed over 3d topologies sharing the same boundary. In the case of …

Least Squares As Random Walks: The General Case Of Arbitrary Spacing, Daniel Kestner, Alexander Kostinski

Michigan Tech Publications

Recently, we introduced the notion of a random walk based on a discrete sequence of data samples ( data walk ) and discovered a surprising link between ordinary least squares (OLS) fits to evenly sampled data and random walks. Here we generalize earlier results by showing that the slope of a linear fit to data which annuls the net area under a residual data walk equals that found by OLS for irregularly spaced data sequence. We also discover a deep connection with the orthogonality principle of estimation theory, leading to interpretation of suitably defined scalar products of data vectors as …

The Role Of Spatial Abilities In Stem Learning And The Influence Of Individual Differences, Styliani Malkogeorgou

Masters

Students’ decisions to pursue education and careers in Science, Technology, Engineering, and Mathematics (STEM) are shaped by an interplay of cognitive, social, and motivational factors. Spatial ability is among the most reliable predictors of STEM success, yet less is known about how it relates to students’ STEM attitudes and aspirations, and whether visuospatial working memory (VSWM) explains this relationship. This study tested the hypotheses that (a) stronger spatial abilities and VSWM would be associated with more positive STEM attitudes and stronger STEM aspirations, and (b) VSWM would mediate the relationship between spatial abilities and STEM attitudes/aspirations, while examining the influence …

Artificial Neural Network Model For Predicting Fatigue Endurance Limit Of Hot Mix Asphalt Using Uniaxial Tension–Compression Tests, Prashanta Kumar Acharjee, Mayzan Isied, Mena I. Souliman, Sameer Jung Karkl, Tanvir Ahmed, Gokhan Saygili

Civil Engineering Faculty Publications and Presentations

Fatigue is one of the major distresses occurring in asphalt concrete pavement. Repeated traffic loading causes escalated structural damage which results in the formation of cracks. There is a strain level below which the Hot Mix Asphalt goes under fatigue failure, and it is called endurance limit. In this study, a predictive model is developed to predict endurance limit strain values by using artificial neural network. Uniaxial tension-compression fatigue test results conducted under NCHRP Project 9–44 A were utilized in the model development process. An equation is also extracted from the model along which gives the exact values as the …

Variation In Food Web Reliance On Green And Brown Energy Pathways Across Ecosystem Gradients, James W. Sturges, W. Ryan James, Ryan J. Rezek, Rolando O. Santos, Mack White, Gina A. Badlowski, Shakira Trabelsi, Jordan Massie, Justin S. Lesser, Joel C. Trexler, James Nelson, Jennifer S. Rehage

Marine Science

Aquatic food webs typically include highly coupled fast, ‘green’ energy pathways driven by algae or phytoplankton and slower, ‘brown’ energy channels driven by detritus and terrestrial plants. Quantifying how much energy biological communities obtain from each of these pathways is essential, particularly across multiple interconnected food webs over large areas, because energy dynamics are known to influence ecosystem structure and function. Despite their importance, few studies track variance in energy channel contributions to food webs across interconnected habitats during distinct hydrologic seasons. In this study, we used tri-isotope Bayesian mixing models to quantify seasonal contributions of energy pathways to consumers …

Quantum Chebyshev Transform-Based Graph Neural Networks For Financial Fraud Detection, Minrui Xu, Bingyan Guan, Bethel Hui Ting Loke, Paul R. Griffin

Research Collection School Of Computing and Information Systems

Financial fraud detection is a critical challenge requiring accurate identification of anomalous patterns in complex transaction networks. Graph Neural Networks (GNNs) have emerged as powerful tools for fraud detection by capturing relational structures among entities. Meanwhile, quantum computing offers new possibilities to enhance machine learning through high-dimensional Hilbert spaces and parallelism. In this paper, we propose a hybrid classical-quantum model called QCTGNN (Quantum Chebyshev Transform-based Graph Neural Network) for financial fraud detection. The QCTGNN integrates a classical graph neural network component based on Simplified Graph Convolutions (SGConv) with a quantum component that performs a Chebyshev polynomial-based transform via variational quantum …

Biob 591.05: Ecological Models And Data, Robert O. Hall

University of Montana Course Syllabi, 2026-2030

No abstract provided.

M 105.01: Contemporary Mathematics, J. Michael Sulock

University of Montana Course Syllabi, 2026-2030

No abstract provided.

M 132.02: Numers And Operations For Elementary School Teachers, Frederick Peck

University of Montana Course Syllabi, 2026-2030

No abstract provided.

M 171.02: Calculus I, Molly Sager

University of Montana Course Syllabi, 2026-2030

No abstract provided.

Characterization Of Sediment Loads And Size Distribution In Nebraska Roadway Runoff, Bruce I. Dvorak, David M. Admiraal, Pavel Shrestha

Nebraska Department of Transportation: Research Reports

The Nebraska Department of Transportation (NDOT) must manage sediment, and pollutant loads from roadway runoff to meet stormwater regulations. The SAFL Baffle, a hydrodynamic separator used by NDOT, depends on reliable estimates of total suspended solids (TSS) and particle size distribution (PSD). However, limited data exists for Nebraska roadways. In this study stormwater runoff was monitored at four NDOT-maintained sites, two in Lincoln and two in Beatrice, over 1.5 years to characterize TSS and PSD and to evaluate SAFL Baffle performance using the SHSAM model. Results showed large variability across sites and seasons. Median TSS ranged from 158 to 580 …

A Runtime-Adaptive Transformer Neural Network Accelerator On Fpgas, Ehsan Kabir, Jason D. Bakos, David Andrews, Miaoqing Huang

Electrical Engineering and Computer Science Faculty Publications and Presentations

Transformer neural networks (TNN) excel in natural language processing (NLP), machine translation, and computer vision (CV) without relying on recurrent or convolutional layers. However, they have high computational and memory demands, particularly on resource constrained devices like FPGAs. Moreover, transformer models vary in processing time across applications, requiring custom models with specific parameters. Designing custom accelerators for each model is complex and time-intensive. Some custom accelerators exist with no runtime adaptability, and they often rely on sparse matrices to reduce latency. However, hardware designs become more challenging due to the need for application-specific sparsity patterns. This paper introduces ADAPTOR, a …

M 221.01: Introduction To Linear Algebra, Emily F. Stone

University of Montana Course Syllabi, 2026-2030

No abstract provided.

M 584.01: Topics In Combinatorics And Optimization - The Probablistic Method, Cory T. Palmer

University of Montana Course Syllabi, 2026-2030

No abstract provided.

M 225.01: Introduction To Discrete Mathematics, Esmaeil Parsa

University of Montana Course Syllabi, 2026-2030

No abstract provided.

M 462.01: Theoretical Big Data Analysis, Javier Perez-Alvaro

University of Montana Course Syllabi, 2026-2030

No abstract provided.

M 151.01: Precalculus, Esmaeil Parsa

University of Montana Course Syllabi, 2026-2030

No abstract provided.

Stat 342.01: Probability And Simulation, Jakob B. Oetinger

University of Montana Course Syllabi, 2026-2030

No abstract provided.

M 491.01: The Geometry Of Surfaces, Eric B. Chesboro

University of Montana Course Syllabi, 2026-2030

No abstract provided.