Digital Commons Network^™

Effective Visibility In The Infrared Bands, Peter L. Dean-Erlander, Steven T. Fiorino, Ronald G. Driggers

Faculty Publications

Visibility is an atmospheric metric for the comparison of terrestrial imaging conditions and locations. A limitation of visibility is that it is defined for the visible spectrum only, and there is no simple infrared equivalent. This study compares three reflective infrared wavebands: near-IR (NIR), shortwave IR (SWIR), and extended shortwave IR (eSWIR), to the visible band across a global set of cities to develop three rule-of-thumb functions for IR effective visibility. To accomplish this, a radiometric sensor model is combined with the Laser Environmental Effects Definition and Reference (LEEDR) software package to calculate the visibility of a black-and-white contrast target …

The Mathematics Of Calendar Patterns: Day-Of-Week Phenomena, Number Patterns, And Recreational Investigations, Vijender Nallapu, Mrinal Kanti Roychowdhury

School of Mathematical & Statistical Sciences Faculty Publications

Calendars are among the most familiar mathematical structures encountered in everyday life. Beyond their practical use in organizing dates and measuring time, calendars contain a rich collection of numerical patterns, symmetries, and arithmetic relationships. Many of these patterns can be explored using only elementary mathematics, making calendars an attractive subject for recreational investigation and mathematical enrichment.

In this article we examine several mathematical properties arising from the structure of monthly calendars. We investigate arithmetic patterns associated with rows, columns, and rectangular blocks of dates, establish properties of calendar squares, and explore connections with modular arithmetic through day-of-week calculations. We also …

Gwtc-4.0: Constraints On The Cosmic Expansion Rate And Modified Gravitational-Wave Propagation, A. G. Abac, I. Abouelfettouh, Teviet Creighton, Mario C. Diaz, Raul Alberto Espinosa Perez, J. Lawrence, Francisco Llamas Villarreal, Soma Mukherjee, Volker Quetschke, Miriam Ramos Arevalo, Wenhui Wang

Physics & Astronomy Faculty Publications

We analyze data from 142 of the 218 gravitational-wave (GW) sources in the fourth LIGO-Virgo-KAGRA Collaboration (LVK) Gravitational-Wave Transient Catalog (GWTC-4.0) to estimate the Hubble constant H0 jointly with the population properties of merging compact binaries. We measure the luminosity distance and redshifted masses of GW sources directly; in contrast, we infer GW source redshifts statistically through (i) location of features in the compact object mass spectrum and merger rate evolution, and (ii) identifying potential host galaxies in the GW localization volume. Probing the relationship between source luminosity distances and redshifts obtained in this way yields constraints on cosmological parameters. …

Vedic Mathematics And Mental Computation: Fast Multiplication Techniques, Algebraic Foundations, And Historical Perspectives, Rakesh Bhatia, Mrinal Kanti Roychowdhury

School of Mathematical & Statistical Sciences Faculty Publications

Mental computation has fascinated mathematicians, educators, and students for centuries. Various computational techniques allow arithmetic calculations to be performed rapidly without the use of calculators or computers. Among these methods, several popular techniques are associated with what is commonly known as Vedic Mathematics. In this article, we examine a collection of fast multiplication techniques, including multiplication near powers of ten, multiplication by eleven, squaring numbers ending in five, and the vertical-and-crosswise method. For each technique, we provide elementary mathematical explanations based on algebraic identities and place-value representations.

Hidden Symmetries Of The Classical 3×3 Magic Square, Vijender Nallapu, Mrinal Kanti Roychowdhury

School of Mathematical & Statistical Sciences Faculty Publications

The classical 3×3 magic square is one of the most famous objects in recreational mathematics. Its defining feature is that every row, every column, and the two main diagonals have the same sum. However, beyond this well-known property, the square contains several hidden symmetries involving rotations, reflections, complementary pairs, parity, and balance around the central entry. In this article, we present an elementary exploration of these symmetries and show how the classical magic square provides a beautiful introduction to mathematical structure and pattern recognition.

Curious Patterns And Identities Involving Consecutive Integers, Carlo Cattani, Mrinal Kanti Roychowdhury

School of Mathematical & Statistical Sciences Faculty Publications

Consecutive integers exhibit numerous surprising patterns and elegant identities. In this article, we investigate several classical properties involving products, sums, divisibility, perfect squares, cubes, pronic numbers, triangular numbers, sums of squares, and Pythagorean triples. Using only elementary algebra, symmetry arguments, and mathematical induction, we establish a collection of identities that reveal rich connections among these classical topics. The exposition is intended for students, teachers, and recreational mathematicians, illustrating how simple observations about consecutive integers can lead to beautiful mathematical patterns and deeper insights into elementary number theory.

The Use Of Machine Learning Models For Predicting The Dielectric Strength Of Gases, Matthew Mileski, Paul W. Groth, Timothy S. Wolfe, Adib J. Samin

Faculty Publications

Technological advancements in high voltage systems have pushed sulfur hexafluoride (SF6) to its operational limits. Furthermore, this gas has other drawbacks including a high liquefaction temperature and a high global warming potential. Therefore, there has been an urgent need to find alternative gases with high dielectric strength (DS). In this work, density functional theory (DFT) is used to calculate molecular descriptors that are fed into an artificial neural network (ANN) and a random forest (RF). These machine learning (ML) models are then used to predict the DS for hundreds of molecules. A finite element model (FEM) is also used to …

An Atmospheric Optical Turbulence Structure Parameter Measurements System Based On Direct Ri Sensing Using High-Resolution Fiber-Optic Sensors, Matej Njegovec, Simon Pevec, Vedran Budinski, Boris Macuh, Melissa K. Beason, Denis Onlagic

Faculty Publications

The paper presents a method to characterize the refractive index structure parameter (C_n²) associated with optical turbulence directly. The characterization system is based on miniature, high‑resolution, all‑fiber refractive index (RI) sensors. The refractive index sensors employ open‑path, low‑finesse Fabry–Perot interferometers that are approximately 8 mm long and 200 μm in diameter. The active part of the interferometers is made of ultra‑low‑expansion glass, which eliminates the influence of thermal expansion on the refractive index measurements. The proposed refractive index sensor, operating in a differential configuration, achieved a resolution of 2×10^-9 RIU using a custom‑designed spectral interrogation system …

Demonstrating Superresolution In Radar Range Estimation Using A Denoising Autoencoder, Robert Czupryniak, Abhishek Chakraborty, Andrew N. Jordan, John C. Howell

Mathematics, Physics, and Computer Science Faculty Articles and Research

We apply machine learning methods to demonstrate radar range superresolution using a denoising autoencoder trained without supervision. Focusing on the estimation of a single physical parameter, the separation between two scatterers in the subwavelength regime, we constrain the network to a one-dimensional bottleneck layer with its size matched to the parameter dimensionality. We find that the bottleneck layer forms a reproducible, monotonic mapping with the true separation, showing that the network learns a low-dimensional representation directly aligned with the underlying physical parameter. We further show that this representation preserves the Fisher information of the signal, indicating that the network recovers …

Moss Biomonitoring And Isotopic Signatures Reveal Aviation Fuel As Local Lead Source Near Oregon Airports, Oluwaseyi E. Aderinboye, Alyssa E. Shiel, Lauren Stalford, Christina J. Murphy, Dean B. Atkinson

Chemistry Faculty Publications and Presentations

Lead emissions from piston-engine aircraft using leaded aviation gasoline (avgas) remain a significant source of environmental contamination near regional airports, posing potential health risks to nearby communities. Passive biomonitoring using the epiphytic moss Orthotrichum lyellii was combined with lead isotope fingerprinting to quantify lead concentrations and distinguish between legacy leaded gasoline and current avgas emissions near Hillsboro and Troutdale airports in Oregon. Here we show that lead levels are elevated by up to 50 times the rural background near airports, with isotopic signatures confirming avgas as a dominant local source. These findings indicate that avgas use contributes substantially to environmental …

Enabling Asl Digital Communication Under Poor Internet Access, Swann Thantsin

Student Theses

Video conferencing degrades asymmetrically. When bandwidth falls, a hearing caller loses picture quality and keeps the conversation; a deaf and hard of hearing signer, whose language is carried entirely in the visual modality, loses the conversation. This thesis asks whether signed video reduced to the rates at which commercial platforms fail can be reconstructed at the receiver well enough to keep signing legible. A twostage reduction pipeline crops to the signer and transmits the face and hands at higher fidelity than their surroundings, achieving a reduction of approximately 99%; reconstruction uses a recurrent bottleneck mixer architecture, trained both conventionally and …

Enhancing A Mid-Wave Infrared Fourier Transform Hyperspectral Imager For Explosions, James T. Stofel, Kody A. Wilson, Martin Larivière-Bastien, Anthony L. Franz, Michael L. Dexter

Faculty Publications

Capturing reliable hyperspectral imager data at a meaningful frame rate for explosions and other fast-changing scenes is not possible in the mid-wave infrared region under traditional sensor operating configurations and processing techniques, which typically have frame rates on the order of 0.5–2.0 Hz. To combat these shortcomings, the scene acquisition parameters were tailored for explosions and a new method for processing optical signatures of fast transient scenes with Fourier-transform infrared hyperspectral imagers was developed. For this technique, the instrument was first configured to collect asymmetric interferograms while optimizing the number of measurement points on the short side of the interferogram. …

Chemistry & Fine Arts Collaboration Posters 2025 - 2026, Basil Masri Zada, Corey Beck

Exhibition Catalogs

Supervisors Dr. Basil Masri Zada and Dr. Corey Beck bring together students from Ohio University’s Chemistry and Digital Art + Technology programs in a collaborative effort to make scientific concepts more accessible. Each artist has been assigned to work with a group of chemistry students, and they are then prompted to create an art poster with only the name of the compound. Enclosed in this pamphlet is the hard work of both students from the digital art program and the chemistry program.

Participating artists include:  Addie Smith, Madelyn Bartolone, Jabin Tasmin, Sara Pirahmaiden, Parker McGowan, Sophie Hillis, Leena Blanton, Jack …

Surface-Modified Zinc Oxide Nanostructures For Ammonia Detection, Gowri Shonali N Ms

Theses and Dissertations

The escalating demand for sustainable gas sensors with low-ppm detection has driven research on surface-engineered nanomaterials for portable air-quality and health screening. This thesis focuses on the fabrication of nanostructured ZnO films as chemiresistive sensors for NH3 detection at RT. This study investigates surface modification using GO, rGO, g-C3N4, Ti3C2Tx; (Se, and Te) ink using a drop casting technique over DC-sputtered ZnO films. Surface modification plays a vital role by providing plenty of active sites, enhancing defective oxygen vacancy, facilitating charge transfer, and synergistic interaction between adsorbed oxygen species to enhance the sensing performance at RT. Among them trGO/ZnO hybrid …

Building Agentic Ai Systems With Langgraph: From Llm Pipelines To Multi-Agent Decision Workflows, Mohammad Amin Kuhail

All Works

Recent advances in large language models (LLMs) have enabled the development of increasingly sophisticated agentic AI systems capable of reasoning, planning, tool use, memory management, and collaborative problem solving. Agentic AI refers to AI systems that can autonomously pursue goals and execute tasks by combining reasoning, decision-making, memory, interaction with external tools, and adaptive workflows. Unlike traditional LLM applications that primarily rely on single-prompt interactions or fixed sequential pipelines, agentic AI systems can iteratively analyze problems, coordinate multiple steps, invoke external resources, refine intermediate outputs, and dynamically adapt their behavior in response to evolving context and feedback. Recent developments in …

Learning To Evaluate: Cost-Effective Model Evaluation On Unlabeled Data With Meta-Learning, Trinh Pham, Viet Huynh, Hongzhi Yin, Quoc Viet Hung Nguyen, Thanh Tam Nguyen

Research outputs 2022 to 2026

The rapid advancement of machine learning has led to an unprecedented expansion of model ecosystems, making it increasingly difficult to assess the reliability of newly released models on unseen and unlabeled data. Existing evaluation pipelines typically rely on costly annotation, repeated fine-tuning, or assumptions that do not generalize well to new models. We introduce MetaEvaluator, a cost-effective, model-agnostic framework for fast, label-free evaluation of unseen models across diverse architectures and modalities. MetaEvaluator meta-learns over a pool of reference models to acquire an effective initialization for accurate assessment of unseen models, thereby amortizing evaluation cost and eliminating the need for per-model …

Adaptive Phishing Url Detection Using Hybrid Fuzzy C-Means Clustering And Xgboost, Muntadher Mohammed Kareem, Rawaa I. Farhan

Karbala International Journal of Modern Science

Phishing attacks continue to evolve in sophistication, rendering static detection methods increasingly ineffective. Existing URL-based approaches suffer from limited adaptability to emerging phishing patterns, mislabeled training data, and insufficient validation protocols. This paper proposes a hybrid phishing URL detection system that integrates Fuzzy C-Means (FCM) clustering with XGBoost classification, enhanced by a novel Micro Adaptive Feature Extractor (MAFE). The system employs a multi-stage pipeline: feature engineering generating 36 statistical and interaction features, MAFE producing 15 adaptive features through class-aware dynamic weighting, micro-pattern detection, and entropy analysis, and FCM with K=2 clusters providing soft membership features to XGBoost. A two-pass confidence-based …

Learning, Abstraction, And Creative Search For Interactive Agents, Jonathan Demke

Theses and Dissertations

Artificial intelligence systems are increasingly deployed as autonomous agents that interact directly with complex environments. Ensuring these agents remain reliable, adaptable, and useful presents an ongoing practical challenge. This dissertation studies agent-environment interactions through a progressive lens, examining behavior from foundational training stability and abstraction discovery to adaptation in unpredictable settings and open-ended creative search. The investigation begins by analyzing how hyperparameter settings affect early-stage Deep Q-Network performance, yielding practical guidelines for debugging reinforcement learning implementations. To address the challenge of long-horizon planning, the discovery of temporally extended options is formalized as the Minimum Shortcut Problem, demonstrating how agents can …

Development Of Minimalist Trifunctional Building Blocks For Chemical Biology Probe Synthesis, Luke E. Bohrer

Electronic Theses and Dissertations

The completion of the Human Genome Project in 2003 generated expectations that genomic information would accelerate the discovery of new protein drug targets and FDA-approved therapies. However, many small-molecule drug candidates continue to fail in clinical trials, and much of the human proteome remains undercharacterized in terms of structure, function, and disease relevance. Chemical tools are therefore essential for defining protein function, mapping protein interactions, and identifying disease-associated molecular mechanisms. Among these tools, small-molecule chemical probes provide a powerful strategy for connecting biological activity with target identification, functional analysis, and the development of effective therapies.

This dissertation describes the development …

Wip: Developing A Generative Ai Autoethnography Assistant, Jyoti Suhag, Jennifer Drewyor, Kathryn Bugbee, Michelle Jarvie-Eggart, Lynn Albers, Leo Ureel

Michigan Tech Publications

Background: Generative AI (genAI) is transforming educational research, offering new possi-bilities for conducting interviews while local sandboxing minimizes data privacy risks and hallucinations. Purpose: This work-in-progress presents the AI Autoethnography Assistant, a project investigating how large language models (LLMs) can support autoethnographic interview design and execution. Approach: Using prompt engineering grounded in Interpretative Phenomenological Anal-ysis, paraphrasing techniques, and structured follow-up questions, we developed protocols that incorporate po-sitionality and prompt reflection on origin stories and pivotal life moments. Outcomes: Conversations conclude at the user’s discretion, with the AI generating a thematic summary. We tested refined prompts across four platforms (Gemini, Claude, …

Fragberta: A Fragment-Aware Molecular Representation Model With Sequential Attachment-Based Fragment Embeddings, Neerav Kaushal, Ajay M.N.V. Penmatsa

Michigan Tech Publications

Automated exploration of chemical space through machine learning has become a central strategy in modern drug discovery, where the ability to learn transferable molecular representations directly impacts predictive modeling performance. Recent advances in chemical language models have primarily relied on atom-level string encodings such as SMILES or SELFIES, treating molecules as sequences of individual atoms or characters. However, medicinal chemistry reasoning is often fragment-centric, emphasizing scaffolds, functional groups, and modular substructures rather than isolated atoms. In this study, we introduce FragBERTa, a molecular fragment-aware transformer-based representation language model pretrained using masked language modeling on Sequential Attachment-based Fragment Embedding (SAFE) representations. …

Re: Approval Letter For Draft Final Silver Bow Creek Conservation Area (Sbcca) Butte Reduction Works (Brw) Air Monitoring For Interim Demolition Activities – Phase Ii Data Summary Report (Dsr) (Dated June 26, 2026), Emma Rott

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Re: Comment Letter For The Draft Final Butte Priority Soils Operable Unit (Bpsou) Butte Treatment Lagoons (Btl) Groundwater Treatment System Quarterly Operation And Maintenance Report – Quarter 1 2026 (Dated June 26, 2026), Emma Rott

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Modeling And Simulation Of Solar Battery Charge Controller Using Adaptive Particle Swarm Optimization Mppt Algorithm, Mustafa Sacid Endiz, Göksel Gökkus

Mathematical Modelling and Numerical Simulation with Applications

Implementing an effective Maximum Power Point Tracking method is crucial for optimizing solar energy harvesting against environmental fluctuations like solar radiation and temperature. This paper introduces a novel approach for modeling and simulating a solar battery charge controller, using a modified Particle Swarm Optimization algorithm. The power stage of the system is based on a SEPIC converter, which is employed to manage the power conversion and improve the energy transfer to the battery. The developed circuit model is evaluated under various radiation levels at a constant temperature, as well as under different temperature levels at a constant radiation. Simulations are …

Technical Reliability As Foundation Of Mobile Self-Checkout Experience, Pumezile O. Dyantyi, Fazlyn Petersen

African Conference on Information Systems and Technology

Although mobile self-checkout (MSCO) apps are becoming more common in retail stores, their long-term success in emerging markets depends heavily on the system’s technical reliability. This qualitative thematic study of 888 user reviews for four established MSCO apps investigates how technical reliability shapes the user experience (UX) after app adoption. Using an integration of the UX Honeycomb model and the UTAUT2 framework, this paper demonstrates that technical reliability is a core UX element that affects usability, performance, trust, value, satisfaction and continued use. Technical reliability problems such as system outages, login challenges and transaction difficulties negatively impact several core UX …

South Africa's Inclusive Six-Dimensional Generative Ai Readiness Framework, Keegan Steyn, Ojelanki Ngwenyama, Sibongiseni Tunzelana

African Conference on Information Systems and Technology

Generative artificial intelligence (Gen AI) is transforming governance, knowledge systems, and economic activity, but current preparedness measures pay little attention to the socio-technical, institutional, and postcolonial contexts that shape country trajectories. To address this gap, the study employs a design science methodology to create the National Generative AI Readiness Assessment Framework (N-GAIRF), a six-dimensional diagnostic tool theoretically based on Sen's capability approach to development and incorporating Institutional Theory, Socio-Technical Systems Theory, National Innovation Systems Theory, and Postcolonial Information Systems critique. An example application to South Africa, compared to Zimbabwe and an indicative Organization for Economic Cooperation and Development (OECD) reference …

Secondary Water Saturation Of The Rock Mass And Quality Of Rocks In Active Mine Areas Bordering The Flooded Workings Of Closed Mines, Mirosława Bukowska

Journal of Sustainable Mining

The goal of this paper was to inspect the influence of capillary water saturation on the specific strain energy of sedimentary rocks. The relationships between the determined specific strain energies in an air-dried state and in a state of capillary water saturation for detrital rock were exponential, with a strong correlation among the variables. For silt, the relationships were described by a second-degree polynomial function, and the correlation was medium or weak. The relationships for kinetic and dissipated energy were described with a linear function. Pearson correlation coefficients indicated a strong correlation among the variables for detrital rock and a …

The Role Of Trauma-Informed Care In Supporting At-Risk Students In Alternative Education, Jar'mel D. Taylor Dr.

Transformations

Students with traumatic experiences commonly fail to meet educational goals and control their emotions while also losing their involvement in classes. Standard trauma-informed care practices remain absent from numerous alternative educational institutions, which leaves their teaching community without adequate support to help traumatized students effectively. The application of Trauma-Informed Care (TIC) strategies toward student outcomes requires investigation to develop effective educational approaches that enhance school success. The researcher used qualitative exploratory case study methodology to study TIC practices in alternative education for Black and Brown adolescent students (aged 12–18) in Washington, D.C. Semi-structured interviews and a focus group with 16 …

65th Annual Rocky Mountain Conference On Magnetic Resonance

Rocky Mountain Conference on Magnetic Resonance

Conference program, abstracts, and information about the 65th annual meeting of the Rocky Mountain Conference on Magnetic Resonance. Held in the Snowbird Resort and Conference Center, Snowbird, Utah, August 2-6, 2026.

A Coupled Reaction-Diffusion Model Of Promoting And Suppressing Interactions Among Cancer Hallmarks In Endometrial Cancer, Jeddy Bennett

Theses and Dissertations

Spatial transcriptomics enables the scoring of Cancer Hallmarks at a high resolution across a tissue biopsy, yet current analyses of these data remain largely descriptive, treating each spot as a static, independent observation. This thesis develops a coupled system of advection-diffusion-reaction partial differential equations to model the hallmark landscape as the steady-state solution of an underlying spatial process, and casts the recovery of its physical parameters as a PDE-constrained inverse problem. The framework is implemented in FEniCSx on tissue-specific finite element meshes derived from two endometrial cancer biopsies provided by the Huntsman Cancer Institute, with subdomains, boundary interfaces, and an …