Digital Commons Network^™

Patterns Of Multimorbidity Among Low-Income Adults Who Smoke With Implications For Tailored Interventions: A Cluster Analysis Using A Mixture Of Bernoulli Model, Monique T. Cano, Michael R. Lindstrom, Oscar F. Rojas Perez, Ricardo F. Muñoz

School of Mathematical & Statistical Sciences Faculty Publications

Introduction: Smoking cigarettes remains a leading modifiable risk factor for preventable health conditions. In the United States, the health burden of smoking disproportionately impacts low-income individuals. Multimorbidity is common in this group, complicating treatment and worsening outcomes. Identifying multimorbidity clusters can support targeted, individualized interventions. This study aimed to identify multimorbidity clusters among individuals who smoke and experience economic hardship and provide clinical recommendations to enhance health outcomes.

Method: Individuals who smoke and experience economic hardship (N = 60) were recruited from the San Francisco Health Network (SFHN) and were assessed for physical and mental conditions. Cluster analysis was …

Ai Adoption In Research Administration At Emerging Research Institutions, Dylan Ruediger, Ruby Macdougall, Stefanie Brachfield, Douglas R. Dechow, Jonathan Parker, Jana Remy

Library Articles and Research

"With funding from the National Science Foundation’s GRANTED program (grant #2437518), Ithaka S+R, Chapman University, and Montclair State University organized two workshops to help research administrators consider how to leverage AI to build research capacity at ERIs. Our first workshop, held at Montclair State in September 2025, brought together 31 participants from 13 academic and medical institutions in the New York/New Jersey/Pennsylvania region. Our second workshop, hosted by Chapman University on December 5, 2025, included 32 participants from 13 colleges and universities in Southern California. The approximately 2,600 ERIs in the United States receive a disproportionately small amount of federal …

The Effects Of Affordability And Quality Of Care On Utilization Of Primary Healthcare Among Rural Residents In Twifo Ati-Morkwaa District, Ghana: A Qualitative Study, Peter Ansah Boakye, Francis Tei-Nartey, Gideon Owusu

Michigan Tech Publications

Objective: This qualitative study examines how affordability and quality of care influence the utilization of primary healthcare (PHC) services among rural residents in the Twifo Ati-Morkwaa District, Ghana. While Ghana has implemented policies such as CHPS and NHIS to improve access, subjective experiences of rural residents regarding total costs of care and perceived quality remain underexplored. Methods: We conducted a qualitative cross-sectional study between April and September 2024 using purposive and snowball sampling. Ten gender-segregated focus group discussions (FGDs; n = 90 residents) and six in-depth interviews (IDIs) with PHC providers were conducted. Data were collected in Twi, transcribed and …

Association Between The Dietary Index For Gut Microbiota (Di-Gm) And Colorectal Cancer In The Plco Cohort, Bezawit E. Kase, Angela D. Liese Ph.D., Jiajia Zhang Ph.D., Elizabeth Angela Murphy, Susan E. Steck Phd, Mph, Rd

Faculty Publications

Objectives: The study aimed to examine the association between a dietary index for gut microbiota (DI-GM) and the risk of incident colorectal cancer (CRC). Clarifying the role of diet-induced alterations in the composition and function of gut microbiota on the development of CRC can contribute to prevention efforts. Methods: Participants from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening trial enrolled in the intervention arm and who completed baseline assessments were included in the analysis (n = 55,685). The DI-GM is a literature-derived index used to score diet quality in terms of maintaining healthy gut microbiota. A time-dependent Cox model …

Association Between The Dietary Index For Gut Microbiota (Di-Gm) And Colorectal Cancer In The Plco Cohort, Bezawit E. Kase, Angela D. Liese, Jiajia Zhang, Elizabeth Angela Murphy, Susan E. Steck

Faculty Publications

Objectives: The study aimed to examine the association between a dietary index for gut microbiota (DI-GM) and the risk of incident colorectal cancer (CRC). Clarifying the role of diet-induced alterations in the composition and function of gut microbiota on the development of CRC can contribute to prevention efforts. Methods: Participants from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening trial enrolled in the intervention arm and who completed baseline assessments were included in the analysis (n = 55,685). The DI-GM is a literature-derived index used to score diet quality in terms of maintaining healthy gut microbiota. A time-dependent Cox model …

Fabrication And Analysis Of Bi₂O₃/Si Heterojunction For Solar Cell Applications, Maher Khaleel Ibrahim, Assel Mustafa Abdul Majeed, Randa Kamel Husain, Jamal M. Rzaij

Al-Bahir

Bismuth oxide (Bi₂O₃) nanoparticles were synthesized via a green route and integrated into Bi₂O₃/Si heterojunction solar cells. Structural analysis confirmed the monoclinic α-Bi₂O₃ phase with a nanoscale crystallite size, while SEM revealed a uniform morphology. Optical studies showed a direct band gap suitable for absorbing visible light. Photoluminescence spectra exhibited dual emissions at 498 nm and 250 nm, attributed to defect states and quantum confinement, highlighting the dual role of Bi₂O₃ as both absorber and transparent window layer. Electrical characterization under dark conditions demonstrated rectifying diode behavior, confirming the formation of a junction. Capacitance–voltage analysis yielded a built-in potential of …

Classification Of Land Cover In Sentinel-2 Imagery Using Machine Learning Models, Ehsan Ali Al-Zubaidi, Mohammed Ridha Hammoodi, Ahmed Naser Alzurfi

Al-Bahir

Remote sensing data of medium resolution are commonly used to classify land cover, and machine learning (ML) models have taken on a central aspect in the necessary data analysis. Ordinarily, land cover is coded on a pixel basis on the basis of Digital Number (DN) values, which in turn are computed across several spectral bands. This paper is concerned with land cover mapping in Mosul, Iraq, based on satellite images captured by Sentinel-2. Two platforms featuring unsupervised classification algorithms were used, Google Earth Engine and ArcMap, making it possible to use K-means and X-means in Google Earth Engine and ISO …

Weakening Relations Over Duals Of Limit Ordinals, Nikolaos Galatos, Isis A. Gallardo

Mathematics: Faculty Scholarship

We prove that all varieties generated by weakening relation algebras over duals of limit ordinals have a decidable equational theory. We also show that there are only countably-many such varieties and they form a chain that embeds in. The time warp algebra is isomorphic to one of these weakening relation algebras (over the dual of the naturals), so we obtain the main result of a recent publication as a special case. The algebras we study connect to the theory of relation algebras, to time warps and graded modalities, and to the algebraic semantics of substructural logics. Our methods are inspired …

A Study Of Transients From Ground-Based Surveys Reveals New Ultra-Compact Accreting White Dwarf Binaries, Jan Kára, Liliana E. Rivera Sandoval, Wendy Mendoza, Thomas J. Maccarone, Manuel Pichardo Marcano, Luis Salazar Manzano, Ryan J. Oelkers, Jan Van Roestel

Physics & Astronomy Faculty Publications

AM CVn stars are ultra-compact semi-detached binaries consisting of a white dwarf primary and a hydrogen-depleted secondary. In this paper, we present spectroscopic and photometric results of 15 transient sources pre-classified as AM CVn candidates. Our analysis confirms 9 systems of the type AM CVn, 3 hydrogen-rich cataclysmic variables (accreting white dwarfs with near-main-sequence stars for donors), and 3 systems that could be evolved cataclysmic variables. Eight of the AM CVn stars are analysed spectroscopically for the first time, which increases the number of spectroscopically confirmed AM CVns by about 10%. TESS data revealed the orbital period of the AM …

When Ai Writes The Doctoral Thesis: Reclaiming The Oral Defence As A Learning Development Intervention, Valerie A. Storey

All Faculty and Staff Scholarship

Large language models have fundamentally challenged traditional methods of verifying doctoral competency as AI-generated text becomes increasingly difficult to distinguish from human scholarship. This paper argues that thesis committees and doctoral supervisors must reclaim the oral defence as a critical checkpoint for assessing authentic threshold crossing rather than a ceremonial rite of passage. Drawing on historical examples from medieval oral disputations through to the rise of written theses, this paper asserts the necessity of returning to rigorous oral assessment. Given the limitations of detection technologies and the growing use of AI in thesis writing, oral defences must move from confirmatory …

Interview With Orca Award Winner David Hage, Unl Chemistry; Drug-Protein Interactions, Mark Griep, David Hage

UNL ORCA Interview Series

Prof. David Hage was awarded the ORCA in 2018. He was born in La Crosse, Wisconsin, and earned a bachelor’s in chemistry at the University of Wisconsin in La Crosse. Next, he earned a doctorate from Iowa State University and did postdoctoral work at the Mayo Clinic in Rochester, Minnesota. In 1989, he joined UNL as an assistant professor of chemistry and quickly rose through the ranks. He has won many awards and, for the past 13 years, has been the James Hewet University Professor of Chemistry. Allow me to share some remarkable facts about Dave before we get into …

Properties Of The Graph Modularity Matrix And Its Applications, Benjamin Quiring, Panayot S. Vassilevski

Mathematics and Statistics Faculty Publications and Presentations

We study the popular modularity matrix and respective functional ([20]) used in connection with graph clustering and derive some properties useful when performing vertex aggregation of the associated graph. These properties are employed in the derivation of a multilevel parallel pairwise aggregation algorithm. Comparative performance results of the studied algorithm applied to graph clustering tested against the popular Louvain algorithm [4], [3] are presented. Some illustrative examples show that the resulting aggregates if used in an adaptive algebraic multigrid (AMG) are able to follow strong direction of anisotropy in finite element problems. 1.

Objective Conditioning Via Latent-Factor Modeling, Kalman-Smoothing, And Hidden Markov Clustering: An Application To Empirical Asset Pricing, Melanie R. Neller

Theses and Dissertations

Empirical asset pricing relies heavily on conditioning sets--industry groupings, size buckets, valuation screens, and other researcher-defined partitions--to structure cross-sectional tests and portfolio construction. While intuitive, such partitions risk blending heterogeneous firms, masking latent exposures, and introducing omitted-variable bias. This thesis develops an objective, mathematically grounded conditioning pipeline that replaces subjective groupings with latent-factor extraction, Kalman-based data techniques, and probabilistic clustering via Gaussian hidden Markov models (HMMs). Using CRSP monthly returns, the framework stabilizes noisy return data, extracts systematic structure through principal component analysis (PCA), identifies homogeneous stock cohorts through HMM clustering, and models regime persistence using Markov transition matrices. These …

Curriculum For A Two Semester Calculus Course Specializing In Life Science And Data Science, Patrick Mcclain

LSU Master's Theses

Traditionally, introductory calculus has been designed for engineering and physics students, often leaving students majoring in data science or life sciences with a curriculum that lacks professional relevance and is overly reliant on problems that focus on computational fluency. This thesis proposes a two-semester sequence, called MATH 153X and MATH 154X, specifically tailored for the Louisiana State University (LSU) Dual Enrollment program and university-level data science and life sciences majors. By integrating modern computational tools—such as symbolic calculators and artificial intelligence (AI) tools—the proposed curriculum shifts the pedagogical focus from procedural symbolic manipulation toward conceptual literacy.

Through a series …

Geospatial Understanding Of Foundation Models: Self-Supervision, Robustness, And Resource-Constrained Learning, Gilbert Kibet Rotich

USF Tampa Graduate Theses and Dissertations

Research on large-scale geospatial understanding from satellite imagery remains relativelyunderexplored. Much of the field still relies on narrow, task-specific pipelines, and there is lim- ited work on developing true foundation models or on systematically evaluating how well these models generalize under domain shift, geographic variation, and real-world scene diversity. This dissertation advances foundation-model pretraining and evaluation for remote sensing along three complementary axes. First, we develop self-supervised multispectral pretrained models for satellite image classification by adapting BYOL, BarlowTwins, DINO, MoCo, and Simsiam to multispectral inputs, addressing the limited amount of curated satellite datasets relative to ImageNet. Pretraining on Eurosat and …

Towards Understanding Robust Neural Coding Through Representational Geometry, Zeyuan Ye

Arts & Sciences Graduate Student Theses and Dissertations

Neural activity is high-dimensional and variable, yet animals represent information robustly. This thesis studies the origins of such robustness through the lens of representational geometry and deep neural network modeling. From a geometric perspective, population responses concentrate near smooth manifolds embedded in a high-dimensional state space. To infer these manifolds from noisy data and quantify their geometric properties, we develop a statistical method based on Gaussian processes and kernel regression (GKR). Applying GKR to simultaneously recorded grid-cell population activity during open-field navigation, we show that increasing running speed expands a torus-like representational manifold and improves spatial decodability. Thus, despite faster …

Rethinking How The United States And Mexico Share The Colorado River, Eric Kuhn, Anne Castle, Carlos De La Parra, John Fleck, Jack Schmidt, Kathryn Sorensen, Katherine Tara

The Traveling Wilburys of the Colorado River

Since 1945, the United States and Mexico have managed common interests on their two largest shared rivers systems, the Colorado and the Rio Bravo/Rio Grande, under the terms of the 1944 international treaty that was designed from the beginning with tools to adapt to changing hydrologic and societal conditions. A recent emergency agreement on the Rio Bravo/Rio Grande illustrates what is possible, and with old river management rules on the Colorado both within the United States and between the United States and Mexico about to expire, we are at a moment of opportunity for meaningful change. The core problem on …

Learning To Refine Gesture-Derived Voxel-To-Loop For Generative 3d Modeling In Vr, Kallen Lonbom, Trudi Di Qi

Engineering Faculty Articles and Research

We present a generative modeling pipeline for refining and denoising coarse 3D shapes created through freehand interaction in virtual reality (VR). While recent loop-based generative models focus on shape synthesis from clean inputs, our work addresses the challenge of reconstructing coherent geometry from noisy, coarse volumetric data. To this end, we introduce a hybrid voxel-to-loop representation that captures the topology of rough voxel shapes and converts them into sequential cross-sectional loops for generative refinement. We develop two transformer architectures and evaluate their efficacy primarily on synthetically perturbed datasets with additional examples from VR. Our results demonstrate that this representation is …

Making Water Visible: Neutral, Verification-First Infrastructure For Basin-Scale Water Accountability, Eric Mcculley

Spring Runoff Conference

Every Tool Tells You What the Lake Level WAS.

None Tell You What It WILL BE.

• Current monitoring is retrospective — USGS gauges and DWR reports show only historical data
• Decision lag: By the time you see the problem, it’s too late to adjust
• 2024–2025 season: GSL dropped to record lows before upstream conservation kicked in
• Agricultural users, municipalities, and GSL all compete for limited supply with imperfect information

Meteorological Monitoring With Real-Time Data Processing Using Uas, Htoo Poe Sar, Cal Coopmans, Sierra Young

Spring Runoff Conference

Background and Motivation 

• Agricultural water management requires information about crop water use and water consumption 
• Remote sensing methods are used to model and estimate environmental processes – but they require weather data 

Utah Prototype For Visualizing Vulnerabilities Downstream Of Dams, Courtney Flint, Michael Englert, Nicolas Holden

Spring Runoff Conference

The climate question 

• Overtopping = 1/3^rd of all dam failures (ASDSO) 
• Drought concerns and supply uncertainty motivate keeping reservoirs full 
• Overtopping likelier if extreme storm when reservoir full 
• Over much of the country the probability of the annual daily maximum rainfall > 100-year event and the preceding 30-day rainfall >10-year event are going up (Hwang & Lall 2024) 

The Reform Of Prior Appropriation And The Fate Of Great Salt Lake, Christopher L. Lant, Rocky Seeley, Beth Parker, Andrew Follett

Spring Runoff Conference

Legal Issues Raised by the Decline of Great Salt Lake

• Violations of Clean Air Act from dust threaten federal intervention
• Petition to list Wilson’s Phalarope as an endangered species

Microplastics As Physical Hubs Amplifying Antiobiotic Resistance Under Mixed-Contaminant Selection In Wastewater Systems, Liyuan Hou

Spring Runoff Conference

Microplastics (MPs) are tiny plastic particles within the range of (0.1 mm to 5 mm) in size

Integrating Hydrology And Human Water Footprints: A Case Study Of The Great Salt Lake Watershed, Rocky Lynne Seeley

Spring Runoff Conference

A new model, comprehensively integrating hydrologic, water footprint, and virtual water data

Balancing Water Needs In Walker Basin, Peter Stanton

Spring Runoff Conference

Walker Lake 

Supported 50% of Mineral County economy 

Traditional homeland of Walker River Paiute Tribe (Agai Dicutta) 

World-class fishery

Annual Loon Festival

Investigation Of Molecular Footprints In Ultra-High Diluted Homoeopathic Solutions Of Arnica Montana And Rhus Toxicodendron Through Combined Infrared And Impedance Spectroscopy, Sharbadeb Kundu, Usha Rani Dash, Chirasree Roychaudhuri, Aindrila Roy, Sanatan Chattopadhyay, Rathin Chakravarty, Debadatta Nayak, Subhash Kaushik, Amit Roy Chowdhury, Ananya Barui, Subhadip Chakraborty

Indian Journal of Research in Homoeopathy

Background: The study of ultra-high diluted (UHD) solutions is a compelling and controversial domain in modern science. Despite numerous investigations and hypotheses, there is a lack of rigorous, reproducible empirical evidence supporting the existence of molecular footprints beyond the Avogadro's limit. Objective: This study conducted a detailed investigation of the molecular and electrical properties, focusing on hydrogen bonding and impedance to understand the fundamental nature of UHD solutions. Materials and methods: Combined impedance and infrared spectroscopic investigations were performed to probe the structural behaviour of relevant water networks at the molecular level of UHD alcoholic extract of Arnica montana (AM) …

Multi-Modal Tensor Fusion For Alzheimer’S Disease Recognition, Mason Li, Tiffany Le, Jiajing Huang, Yuxin Wen

Engineering Faculty Articles and Research

Accurate and early diagnosis of Alzheimer’s disease (AD) is critical for effective intervention, disease monitoring, and patient care. Traditional diagnostic approaches rely on a single modality, such as clinical assessments, neuroimaging, or genetic markers, which may fail to capture the complex, multifaceted nature of AD. Multimodal learning has therefore been explored to integrate complementary information across data sources. However, conventional fusion strategies, including early feature concatenation and late decision-level fusion, often model modalities independently and fail to capture high-order cross-modal interactions. To address these limitations, we propose a multimodal tensor fusion network (MTFN) that integrates heterogeneous data sources, including visual …

What Does Next-Generation Mass Spectrometry Offer For Proteomics? A Comprehensive Platform Comparison, Filipa Blasco Tavares Pereira Lopes, Daniela Schlatzer, Tara Sudhadevi, Anantha Harijith, Marzieh Ayati, Mehmet Koyutürk, Mark R. Chance

Computer Science Faculty Publications

Next-generation mass spectrometry platforms (Orbitrap Astral, timsTOF Ultra) are reshaping proteomics by enhancing analytical depth and sensitivity. We compared these platforms against Orbitrap Exploris 480 using neonatal mouse lung tissues from a bronchopulmonary dysplasia model (n = 12), employing four acquisition strategies: Exploris 480 DDA/DIA, Astral HR-DIA, and timsTOF Ultra DIA-PASEF. All platforms identified ∼4000 proteins in common, with 98% proteome coverage of data-dependent acquisition (DDA) identifications using data-independent (DIA) methods and 92% concordance between next-generation systems. Orbitrap Astral and timsTOF Ultra quantified >225,000 peptides and 13,000 proteins, representing ∼800% and ∼300% greater depth than Exploris 480 DDA, respectively. …

Secure Machine Learning In Networking Systems, Wenwei Zhao

USF Tampa Graduate Theses and Dissertations

With the rapid integration of Machine Learning (ML) into networking systems, ensuring the security and trustworthiness of these intelligent frameworks has become a paramount concern. While ML offers unprecedented capabilities in spectrum management and collaborative learning, it also introduces novel vulnerabilities that can be exploited by sophisticated adversaries. This dissertation investigates and addresses critical security challenges across three key dimensions of ML-driven networking: adversarial spectrum sensing, the security of privacy-preserving unlearning processes, and efficient post-attack model recovery.

First, we address the threat of adversarial spectrum attacks in cognitive radio networks, where malicious nodes manipulate sensing reports to disrupt spectrum access. …

Pheasant Forever Loan Program, Will Benkelman

Spring Runoff Conference

Bear River Watershed Community Conservation Program

Our Mission: Pheasants Forever (PF) and Quail Forever’s (QF) mission is to conserve pheasants, quail, and other wildlife through habitat improvements, public access, education, and conservation advocacy.