Digital Commons Network^™

Intersections Of Art And Medicine, Lina Adwer

EMET Projects

No abstract provided.

Accuracy Of Voluntary Force Modulation During The Isometric Mid-Thigh Pull, S. Alexander Long, Olivia Vadas, Stephanie Balint, Michael H. Stone, Christopher Taber

Exercise Science Faculty Publications

The purpose of this study was to investigate voluntary force modulation accuracy during the isometric mid-thigh pull (IMTP) and to investigate biological sex and relative strength as factors relating to error. Strength-trained males (n = 18) and females (n = 18) completed ascending (ASC) (25%, 50%, 75%) or descending (DESC) (75%, 50%, 25%) submaximal testing followed by maximal testing. Subjects rested before completing the opposite submaximal testing sequence. External feedback was not provided during testing. Measured and intended (INT) forces were analyzed with two-way repeated-measures ANOVAs with within- (ASC, DESC, and INT) and between-subject factors (male or female). Independent-samples t-tests …

How The U.S. Constitution Shapes International Tax Law: Instrument Choice In Tax Agreements, Noam Noked, Young Ran (Christine) Kim, Reuven S. Avi-Yonah

Articles

The U.S. Constitution’s Treaty Clause, which requires Senate approval by a two-thirds vote for treaties, has significantly influenced the development of international tax law. This Article examines the implications of Senate supermajority requirements on bilateral and multilateral tax treaties and agreements, alternative instruments, relevant international tax standards, and global tax governance.

Historically, tax treaties have been approved exclusively as Article II treaties requiring Senate approval. The difficulty of entering into treaties with the United States has influenced the instrument choice and design of U.S. and international tax standards. To address this challenge, several U.S. and international tax reforms have employed …

Methods Of Harvest And Compositional Characterization Of Hemp Fibers From Medical Cannabis Biomass Using Detergent Fiber Analysis, Abigail Lazier, Srishti Banerji, Jennifer Macadam, Bruce Bugbee

Publications

Medical cannabis production generates large quantities of stem biomass that is typically discarded despite its potential as a source of natural hemp fibers. This study developed and evaluated two methods for recovering fibers from greenhouse-grown medical cannabis using water retting and compared their composition with commercially produced industrial hemp fibers. Fiber quality was assessed using detergent fiber analyses to quantify cellulose, hemicellulose, and lignin fractions. Both harvest methods produced usable fibers, although one approach (method A) yielded cleaner fibers after shorter retting durations, while the second approach (method B) offered greater potential for large-scale processing. Increasing retting duration improved fiber …

Master's Recital: Anna Godtland, Anna Godtland

Dissertations and Theses @ UNI

The two purposes of this recital are to partially fulfill the requirements for the Master of Music degree in Jazz Pedagogy and to display proficiency on the drum set, piano, and string bass. Anna Godtland presented her graduate recital on April 27, 2026, at 7:30 PM in Bengtson Auditorium. The recital program included two pieces each on piano, drum set, string bass, and four pieces on trombone. These selections showcased an array of styles and required a variety of skills necessary to fulfill the requirements of the Jazz Pedagogy degree. The recital featured personnel from two UNI jazz combos (Cincopatos …

Discipling The Youth Through Preaching, Rodney Palmer

Faculty Publications

No abstract provided.

Echoes Of The Nation: Janáček, Bartók, And The Articulation Of Central European Identity In Opera, Elyse Morris

Dissertations and Theses @ UNI

This thesis examines how opera functioned as a medium for articulating and negotiating national identity in the final decades of the Austro-Hungarian Empire. Focusing on Leoš Janáček’s Jenůfa (1904) and Béla Bartók’s Bluebeard’s Castle (1911), it explores how composers employed dramatic structure, linguistic choice, and musical language to construct culturally specific forms of expression within a rapidly shifting political and social landscape. Drawing on approaches from musicology and cultural history, the study considers how operatic works not only reflect national identity but actively participate in its formation.

Through analysis of libretto, language, and musical style, this thesis demonstrates that both …

Can’T Sue A Movement: Greenpeace’S $667 Million Crisis Narrative, Trevor Hart

Dissertations and Theses @ UNI

In March of 2025, the environmental group Greenpeace was found liable for nearly $667 million to Energy Transfer, the oil company behind the Dakota Access Pipeline (DAPL). The lawsuit was widely regarded as a SLAPP, or Strategic Lawsuit Against Public Participation, because it weaponized civic torts to infringe upon Greenpeace’s First Amendment rights. The $667 million verdict is an unprecedented figure in SLAPP litigation–it’s enough to bankrupt Greenpeace in the United States and “chill” democratic participation. This thesis used a hybrid blame-renewal crisis narrative framework to analyze Greenpeace’s public response to lawsuits against a backdrop of cultural and political economic …

“En La Clase, Sí. En Estados Unidos, No.”: La Motivación, La Pertenencia Y El Intercambio De Recursos En Clases Para Adultos Aprendices De Inglés, Katie Hunter

Graduate Research Papers

En los Estados Unidos, los programas de educación para adultos pueden abrir la puerta a varias oportunidades para estudiantes con trasfondos académicos no tradicionales. Los programas como la educación básica para adultos, la equivalencia de escuela secundaria, la preparación profesional y técnica y, lo más importante para los propósitos de este estudio, la educación para adultos aprendices de inglés, promueven el progreso profesional y apoyan a los estudiantes a identificar y trabajar para alcanzar sus metas personales. Sin embargo, en este contexto educativo es de suma importancia reconocer el conocimiento arraigado en la experiencia vivida que los estudiantes traen al …

A Quantum Algorithm For Multidimensional Partial Differential Equations With Practical Case Studies, Manu Chaudhary, Kareem El-Araby, Devon Bontrager, Alvir Nobel

Electrical Engineering and Computer Science Faculty Publications

Partial differential equations (PDEs) play a central role in scientific and engineering analysis, with applications spanning fluid dynamics, heat and mass transfer, electromagnetism, quantum mechanics, and financial modeling, where they are used to describe diffusion processes, wave propagation, and the evolution of complex systems over space and time. Solving multidimensional partial differential equations (PDEs) is a computationally challenging problem, even for the most advanced classical systems. Over the past decade, quantum computing has attracted significant interest as a potential approach for solving complex computational problems, including multidimensional PDEs. Although a variety of approaches have been proposed for solving PDEs, most …

Approximate Decryption In Homomorphic Division And Privacy Impact, Ameerah Alsulami, Marius Silaghi

Electrical Engineering and Computer Science Faculty Publications

The result of a computation over secret inputs inherently reveals some information about those inputs; such semantic leakage is unavoidable. The challenge is to ensure that the computation method does not introduce additional, avoidable disclosure beyond what is implied by the output itself. This issue is particularly critical in privacy-preserving machine learning and cloud-based data processing, where homomorphic encryption enables computation over encrypted data but often relies on practical approximations. Division-enabled homomorphic encryption schemes based on rational encodings preserve arithmetic correctness, but their decrypted outputs may retain sufficient algebraic structure to enable inference of the original operands, creating representation-induced leakage. …

A Systematic Review Of Artificial Intelligence For Software Requirements In Open-Source Software, Amal Alharbi, Khaled Slhoub

Electrical Engineering and Computer Science Faculty Publications

Software stakeholders have seamlessly integrated artificial intelligence technologies into the requirements engineering processes for open-source software, which presents both opportunities and challenges. This systematic literature review examines the current state of artificial intelligence application in requirements engineering for open-source software and emphasizes the opportunities and challenges faced by practitioners. Based on the reviewed literature, artificial intelligence technologies, such as machine learning, natural language processing, large language models, and deep learning techniques, have been increasingly applied to support key requirements engineering activities, particularly requirements elicitation, classification, prioritization, and stakeholder analysis. However, the lack of structured data in open-source software directly challenges …

Botox Detection And Face Analytics Using Deep Learning, Audison Beaubrun, Gabriella Pangelinan, Michael King

Electrical Engineering and Computer Science Faculty Publications

Non-surgical cosmetic procedures like Botox are increasingly common, yet their impact on facial analytics systems remains unexplored. We curate a novel dataset of 1,990 before-and-after images from 390 individuals who received cosmetic injectables. Using this dataset, we demonstrate that these subtle facial modifications measurably affect age estimation: FairFace and FaceXFormer show statistically significant shifts toward younger age estimates (-1.43 and-3.27 years, p <0.05), while MiVOLO remains stable. We also show these modifications are detectable: training deep learning models (ResNet-50, DenseNet-121, ConvNeXt-Tiny) to classify cosmetically-altered faces achieves up to 89% accuracy. Our findings reveal that even minor, non-surgical facial changes can bias age-based analytics and are algorithmically detectable-raising critical concerns for privacy, fairness, and robustness as facial analytics expand into high-stakes domains like insurance, hiring, and health assessment. © 2026, Florida Online Journals, University of Florida. All rights reserved.

Collaboration On Waltz Labels Can Achieve Qualitative Stereo Vision, Ben Mathew, Majed Alotaibi, Debasis Mitra, Marius Silaghi

Electrical Engineering and Computer Science Faculty Publications

Stereo vision requires calibration that can be hard to achieve or guarantee, so we propose a Qualitative Stereo Vision approach based on logical reasoning about edges detected from cameras/robots having different points of view. The proposed technique builds upon the qualitative reasoning of Waltz filtering on edges of a 2D image, which is extended to reasoning about edges from multiple images with different points of view. An assumption is that edges and vertexes that appear in different images can be identified based on their features. We find that a consistent spatial interpretation of a scene, classifying occluding, convex, and concave …

Counting Constraints In Pomdps Based On Pid Controllers, Garrett Gmeiner, Patrick Quinn, Asmaa Alqurashi, Prem Swaroop Pochiraju

Electrical Engineering and Computer Science Faculty Publications

A Bayesian architecture is proposed for integrating counting constraints in the process of decision making with Partially Observable Markov Decision Models for robotics. In addressed problems, the counted events are detected with noisy sensors, making their detection uncertain. We handle their count as a random variable to be updated on observations. In our test scenario, an iRobot Create3 moves along a hallway and needs to count how many doors it passes while following the wall. After detecting that the given number of doors have been passed, the robot should turn around and return to the starting region. For this kind …

Cladding-Engineered Hollow-Core Fiber For Polarization Control And Optical Sensing Applications, M.M. Shoaib Hasan, Md Shah Alam, Md Selim Habib, Md Asiful Islam

Electrical Engineering and Computer Science Faculty Publications

A polarization-selective hollow-core antiresonant fiber (HC-ARF) is proposed and numerically investigated for single-polarization guidance in the telecom band. Finite-element simulations across the 1.50–1.60 μm range show that the optimized design achieves a high polarization extinction ratio of 1.73x 104 at 1.55 μm, with low loss for the y–polarized mode (0.72 dB/km) and strongly suppressed x–polarized loss (12531 dB/km). The fiber also provides a modal birefringence of 0.21x 10-4, enabling strong polarization selectivity and stable single-polarization guidance. These results indicate that the proposed PM-HC-ARF is well-suited for polarization-sensitive applications such as interferometry, sensing, and coherent communication. © 2009-2012 IEEE.

Culticon-Bench: A Pilot Benchmark For Cultural Interpretation Of Visual Icons, Rwaida Alssadi, Marius Silaghi

Electrical Engineering and Computer Science Faculty Publications

Visual icons are widely used in user interfaces and multimodal AI systems, yet their interpretation often varies across cultural contexts. Symbols that appear universal may convey different meanings depending on social norms and cultural conventions. We introduce CultIcon-Bench, a pilot benchmark designed to study culturally grounded interpretation of visual icons. The benchmark pairs icon-like visual symbols with short textual contexts and cultural identifiers, enabling controlled evaluation of whether a model correctly interprets the intended meaning under different cultural settings. The dataset is organized around a taxonomy of cultural conflict classes including gestures, politeness norms, privacy expectations, religion, holidays, rituals, dress …

From Recommendation To Reflection: Measuring Moral Value Stability In Human–Ai Collaboration Using Cognitive Value Recontextualization, Waseem M. Samkari, Thomas C. Eskridge

Electrical Engineering and Computer Science Faculty Publications

High-stakes human–AI collaboration is typically evaluated on outcome quality or efficiency. In morally charged domains such as disaster response, however, a key question is whether decisions align with the decision-maker’s core moral values. Research in moral psychology shows that individuals may accept identical outcomes under one framing but reject them under another—signals of value stability, not noise. We propose Value-Recontextualizing Decision Support (VRDS), demonstrated in a wildfire crisis simulation. The system employs Cognitive Value Recontextualiza-tion (CVR), which re-presents decisions under morally intensified framings, and Adaptive Preference Alignment (APA), which clarifies whether contradictions reflect situational reasoning or genuine value change. We …

Json-Ld 1.2 And Beyond: Extensions For Machine Learning Data Exchange, Muntaser Syed, Marius Silaghi, Sheikh Abujar, Rwaida Alssadi

Electrical Engineering and Computer Science Faculty Publications

JSON-LD has become the dominant format for structured data on the web, underpinning schema.org markup, Verifiable Credentials, and knowledge graph serialization. However, the rapid integration of machine learning into data pipelines exposes critical limitations: JSON-LD lacks native mechanisms to express prediction confidence, model provenance, temporal validity, or vector embeddings—metadata essential for trustworthy AI-to-AI and AI-to-human data exchange. Additionally, context injection attacks and unbounded recursion vulnerabilities pose security risks in production deployments. This paper presents a systematic gap analysis of JSON-LD 1.1 against the requirements of modern AI systems, identifying 12 limitation categories spanning security vulnerabilities, performance bottlenecks, validation deficiencies, and …

Physics-Based Design And Simulation Of Hollow-Core Anti-Resonant Fiber Plasmonic Sensors, Mohammad Al Mahfuz, Abdullah Al Mamun, Md Selim Habib

Electrical Engineering and Computer Science Faculty Publications

Next-generation sensing technologies require high-performance sensors that offer ultra-low loss with enhanced sensitivity. To achieve this, we propose a hollow-core anti-resonant fiber (HC-ARF) refractive index (RI) sensor utilizing the surface plasmon resonance (SPR) detection mechanism for superior optical sensing performance. The proposed sensor model features a single ring of uniformly arranged regular cladding tubes, each coated with a thin plasmonic metal layer to facilitate strong coupling between the core and surface plasmon modes. Through rigorous finite-element modeling (FEM) simulations, the fiber structure is carefully optimized and sensing performance is investigated to obtain ultra-low loss and high detection sensitivity via wavelength …

Prom: Protection Against Reverse Engineering Attacks Through Programmable Logic Macros, Pravin Gaikwad, Aritra Dasgupta, Sudipta Paria, Peyman Dehghanzadeh

Electrical Engineering and Computer Science Faculty Publications

The modern supply chain ecosystem exposes hardware intellectual property (IP) blocks to diverse confidentiality attacks aimed at reverse engineering (RE), piracy, or the extraction of design secrets. An emerging and potent design solution for IP protection against these attacks, particularly against RE, is the fine-grained redaction of security-critical logic and replacing the redacted logic with lookup tables (LUTs). The LUTs are then programmed in-field, similar to FPGAs, using protected bitstreams, thereby preventing untrusted foundries or test/assembly facilities from mounting RE attacks. The LUT-based redaction paradigm incurs a substantial hardware cost, with area overhead ranging from 70x to 100x and delay …

Toml: Transistor Operations For Machine Learning: A Physics-Grounded Energy Efficiency Framework, Muntaser Syed, Marius Silaghi, Sheikh Abujar, Sharun Akter Khushbu

Electrical Engineering and Computer Science Faculty Publications

The escalating energy consumption of machine learning systems demands accurate, physics-grounded efficiency measurement beyond conventional proxies like FLOPs and MACs, which fail to capture non-linear operations and memory access costs. While recent work established transistor operations (TOs) as a promising energy proxy for convolutional neural networks, this approach remains limited to a single metric and narrow architectural scope. We present TOML (Transistor Operations for Machine Learning), a framework introducing six novel metrics grounded in CMOS physics: Switching Activity Factor per Token (SAF-T), Logic State Residence Time (LSRT), Energy per Capability Unit (ECU), Memory-Compute Energy Ratio (MCER), Data-Dependent Energy Variation (DDEV), …