Digital Commons Network^™

Large Language Scholarship, Alan Rozenshtein, Kevin Frazier

FIU Law Review

Generative AI isn’t just coming for the legal academy—the era of what we call “Large Language Scholarship” is already here. Law professors are using AI to write articles in weeks rather than months. Law reviews face an unprecedented deluge of AI-polished submissions. And the judges who increasingly lean on AI to help draft their own opinions will soon struggle to distinguish genuine insight from sophisticated imitation. Yet, existing scholarship offers only fragmented responses, trapped in misguided debates about plagiarism and disclosure while missing the full scope of the unfolding transformation.

This Article provides the first comprehensive analysis of how generative …

Computational Insights Into Orthotropic Fracture: Crack-Tip Fields In Strain-Limiting Materials Under Non-Uniform Loads, Saugata Ghosh, Dambaru Bhatta, S. M. Mallikarjunaiah

School of Mathematical & Statistical Sciences Faculty Publications

A finite element framework is presented for analyzing crack-tip phenomena in transversely isotropic, strain-limiting elastic materials. Mechanical response is characterized by an algebraically nonlinear constitutive model, relating stress to linearized strain. Non-physical strain singularities at the crack apex are mitigated, ensuring bounded strain magnitudes. This methodology significantly advances boundary value problem (BVP) formulation, especially for first-order approximate theories. For a transversely isotropic elastic solid with a crack, the governing equilibrium equation, derived from linear momentum balance and the nonlinear constitutive model, is reduced to a second-order, vector-valued, quasi-linear elliptic BVP. This BVP is solved using a robust numerical scheme combining …

Strong Q-Analogues For Values Of The Dirichlet Beta Function, Ankush Goswami, Tim Huber

School of Mathematical & Statistical Sciences Faculty Publications

An infinite class of relations between modular forms is constructed that generalizes evaluations of the Dirichlet beta function at odd positive integers. The work is motivated by a base case appearing in Ramanujan’s Notebooks and a parallel construction for the Riemann zeta function. The identities are shown to be strong q-analogues by virtue of their reduction to the classical beta evaluations as 𝑞→1− and explicit evaluations at CM points for |𝑞|< 1. We also determine asymptotic formulas for the Fourier coefficients of the associated modular forms.

A Conceptual Framework For Cognitive Engagement And Human Agency In Ai-Mediated Learning, Sam Rhodes, Zuhal Yilmaz, Terrie Galanti, Younggon Bae, Victoria Delaney, Zareen Gul Aga, Stephen Hutt

School of Mathematical & Statistical Sciences Faculty Publications

The purpose of this chapter is to propose a conceptual framework that unpacks AI use in instructional practice across three interrelated dimensions: human agency, cognitive demand, and AI orchestration. We ground this work in historical perspectives on technology's mediating role in education. We then propose the conceptual framework and three characterizations of the use of AI in school instruction: cognitive load reducer, cognitive capacity builder, and cognitive companion. We then illustrate a worked example of each, including a curricular example and sample student prompts, and conclude with questions that educators might ask themselves as they consider if and how to …

Optimal Quantization On Spherical Surfaces: Continuous And Discrete Models—A Beginner-Friendly Expository Study, Mrinal Kanti Roychowdhury

School of Mathematical & Statistical Sciences Faculty Publications

This expository paper provides a unified and pedagogical introduction to optimal quantization for probability measures supported on spherical curves and discrete subsets ofthe sphere, emphasizing both continuous and discrete settings. We first present a detailedgeometric and analytical foundation for intrinsic quantization on the unit sphere, includingdefinitions of great and small circles, spherical triangles, geodesic distance, Slerp interpolation,the Fréchet mean, spherical Voronoi regions, centroid conditions, and quantizationdimensions. Building upon this framework, we develop explicit continuous and discretequantization models on spherical curves, namely great circles, small circles, and greatcircular arcs—supported by rigorous derivations and pedagogical exposition. For uniformcontinuous distributions, we compute optimal …

Gain-Delay Decoupling In Human Stimulus Frequency Otoacoustic Emissions Reveals Constraints On Cochlear Nonlinear Amplification, Yoshita Sharma, Arturo Moleti, Renata Sisto, Teresa Botti, Hansapani Rodrigo, Sri Mishra

School of Mathematical & Statistical Sciences Faculty Publications

Purpose: The nonlinear cochlear amplifier, driven by outer hair cells, underlies the remarkable sensitivity and frequency selectivity of the mammalian auditory system. Stimulus frequency otoacoustic emissions (SFOAEs) provide a noninvasive window into these active cochlear processes, yet the relationship between emission gain and delay across stimulus levels remains incompletely understood. This study examined the level dependence of SFOAEs in normal-hearing human listeners to characterize cochlear nonlinear response properties. We tested how emission gain and delay vary with stimulus level and estimated the frequency of the apical–basal transition associated with the breakdown of the approximate local scaling symmetry.

Methods: SFOAEs were …

Fully Discrete Finite Element Methods For The Stochastic Kuramoto–Sivashinsky Equation With Multiplicative Noise, Hung D. Nguyen, Liet Vo

School of Mathematical & Statistical Sciences Faculty Publications

We investigate a fully discrete finite element approximation for the stochastic Kuramoto–Sivashinsky equation, combining the standard finite element methods in spatial discretization with the implicit Euler–Maruyama scheme in time. Rigorous error estimates are established for two distinct noise regimes. In the case of bounded multiplicative noise, we prove optimal strong convergence rates in full expectation. The analysis relies crucially on a stochastic Gronwall inequality and an exponential stability estimate for the PDE solution, which together control the interplay between the nonlinear drift and the multiplicative stochastic forcing. For general multiplicative noise, where boundedness no longer holds, we derive sub-optimal convergence …

Artificial Intelligence And Machine Learning In Smart Vaginal Formulation Development, Deborah A. Ogundemuren, Vivek Agrahari, Andrew P. Wong, Carolina Herrera, Margaret O. Ilomuanya, Gustavo F. Doncel

CONRAD Publications

Vaginal drug delivery in women's health remains underutilized and insufficiently studied, largely due to the complexity and dynamic nature of the vaginal microenvironment. Variations in vaginal pH, hormonal levels, and microbiota composition introduce significant biological variability, complicating formulation design and contributing to inconsistent therapeutic outcomes and poor patient adherence. Conventional vaginal formulations often fail to account for these individual differences, highlighting the need for more adaptive and predictive approaches. Emerging advances in artificial intelligence (AI) and machine learning (ML) offer promising strategies to address these challenges by enabling multi-parameter, data-driven formulation development that explicitly considers biological variability. Despite their transformative …

Una Nueva Herramienta Para Estimar El Volumen Muscular Independientemente De La Edad, El Sexo Y El Nivel De Actividad Física, José J. Rivera-Sánchez, Laura P. Burgos-Morelos, Daniel Castillo-Castillo, Gerardo Méndez-Suárez, Juan C. Lopez-Alvarenga, Damián Carrillo-Ruíz

School of Medicine Publications

Antecedentes: Actualmente, la evaluación del volumen muscular requiere técnicas invasivas o de imagen, lo que aumenta los costos y retrasa la evaluación, y las mediciones antropométricas con frecuencia son inexactas.

Objetivo: Desarrollar una fórmula matemática para estimar el volumen del cuádriceps.

Material y métodos: Se evaluaron 127 sujetos: 40 adultos sedentarios de 20 a 35 años, 47 atletas adultos de 20 a 45 años y 40 adultos sedentarios de 70 a 94 años. Se dividieron en grupos de desarrollo y de validación. Se realizaron mediciones de variables antropométricas y fuerza de agarre, y se desarrolló la fórmula de predicción del …

Circle Actions On Oriented 4-Manifolds, Donghoon Jang, Oleg R. Musin

School of Mathematical & Statistical Sciences Faculty Publications

In this present paper, we consider an action of the circle group on a compact oriented 4-manifold. We derive the Atiyah–Hirzebruch formula for the manifold, and associate a graph in terms of data on the fixed point set. We show in the case of isolated fixed points that if an abstract graph satisfies the Atiyah–Hirzebruch formula, then there exists a corresponding 4-dimensional oriented S1-manifold.

2026 Viewbook, Office Of Admissions

Viewbook

At IMSA, you will be part of a community of over 650 diverse students passionate about learning. Located in suburban Aurora, IMSA’s campus consists of seven residence halls spread across 97 acres. While living and learning on campus, you’ll make new friends, encounter new ideas, and learn to live independently. You will also have the opportunity to explore over 85 clubs, organizations, and IHSAsponsored co-curricular and athletic programs. IMSA provides a safe, nurturing environment. Professional Resident Counselors (RCs) live in each hall and will help you navigate your home away from home through the Residential Life Curriculum. Through this curriculum, …

Development Of Feature Tokenizer Deep Learning Model For Fault Diagnosis In Marine Propulsion System, Pratik Anand Deshpande, J. Preetha Roselyn, Prabha Sundaravadivel

Electrical Engineering Faculty Publications and Presentations

The fault diagnostics in Brushless Direct Current (BLDC) motor drive system is critical for operational safety and system lifespan in propulsion system applications. However, signature parameters such as currents, voltages, speed, and torque have provided nonlinear behavior, which limits the usefulness of traditional model-based approaches. This research provides a deep learning based intelligent system to monitor the failures in marine propulsion system. Each signal feature is represented as a structured token, with a specific class token used to collect global contextual information. The proposed model captures both local temporal dynamics and global inter-feature interdependence multi-layer self-attention processes, allowing for the …

A Reconstruction Method For The Anisotropic Electrical Conductivity In A Hybrid Planar Inverse Problem, Athanasios Dimitriadis

Graduate Studies Theses and Dissertations 2026

Some single-physics medical imaging methods fail to reliably discriminate benign from malignant biological tissue. In partial response, a new class of inverse problems employ multi-physics phenomena, coupling methods of high resolution with methods of high contrast. In this thesis we are concerned with one such problem in which an anisotropic electrical conductivity is to be recovered from some internal knowledge of the current density field generated by maintaining a fixed boundary voltage. This internal data is obtained by the method of Current Density Impedance Imaging (CDII), which combines Maxwell's equations with Magnetic Resonance Imaging (MRI) measurements. The anisotropy class is …

Curriculum Approaches To Strengthening Student Employability And Improving Graduate Outcomes In The Stm Disciplines, Denise Jackson, Michael D. O’Connor

Research outputs 2022 to 2026

In Australia and elsewhere, curriculum improvements are needed to improve the development of Science, Technology and Mathematics (STM) graduates for future work and careers. Several studies document significant STM student attrition, poor graduate preparedness for work, relatively weak employment rates post-graduation, and mismatched expectations of graduate capabilities by employers. The inclusion of work-integrated learning (WIL) activities within curricula can better prepare students for relevant employment by bridging discipline-specific teaching with work practices. Traditionally, access to WIL has been limited for STM Bachelor students and a better understanding of WIL’s benefits and impact is needed to refine and extend STM WIL …

Ranked Choice Voting At The State And Local Level, Khel Gordhan

Res Publica - Journal of Undergraduate Research

Many people across the political spectrum are frustrated with many of the problems that come with simple plurality voting, also known as first-past-the-post, which is implemented in the lion’s share of elections across the United States. To solve many of these problems, some activists and other political organizations advocate for cities and states to adopt Ranked Choice Voting (RCV) as a viable alternative. However, many people, both Republicans and Democrats, claim that implementing this system would be too complicated for people to understand. Due to this, there would be high rates of ballot exhaustion, which would disenfranchise vulnerable minorities who …

The Value Of Saltbush Revegetation For Biodiversity In A Highly Fragmented Landscape, Robert A. Davis, Michael D. Craig, Tim S. Doherty, Jonathan D. Majer, Eddie J.B. Van Etten

Research outputs 2022 to 2026

Secondary salinization is a significant global issue affecting up to 1 billion hectares of land, impacting biodiversity and particularly vegetation. One form of revegetation that can provide a return to landholders is saltbush (Atriplex spp.), which provides forage for a wide range of livestock. The effectiveness of saltbush plantings in providing useful habitat for fauna in agricultural landscapes suffering from secondary salinity is poorly understood. We aimed to address this knowledge gap by surveying bird, ant, and spider communities, and quantifying vegetation structure and floristic composition in saltbush and native woodlands in agricultural landscapes affected by secondary salinization in Western …

Protomix: Unified Prototype Learning For Semantic Change Detection In Remote Sensing, Hengzhi Chen, Kun Hu, Patrick Filippi, Wei Xiang, Thomas Francis Bishop, Zhiyong Wang

Research outputs 2022 to 2026

As an emerging topic, semantic change detection (SCD) in remote sensing aims to both identify the locations of changes before and after events and determine the land cover (LC) classes of changed regions. While deep learning has demonstrated its great potential for SCD, the following two fundamental challenges remain: large variations among the same LC class as well as close similarity between different LC classes, and the severe underutilization of massive unchanged regions, which dominate remote sensing scenes but lack detailed semantic annotations. To address these issues, we propose a novel prototype learning framework for SCD, namely ProtoMix, by leveraging …

En Ritmo: A Praxis In Decolonizing Traditional Dance Spaces, Megan Curet

School of Art, Design and Architecture Theses

En Ritmo: Praxis in Decolonizing Traditional Dance Spaces is an investigation that explores the intersection of bomba, a traditional Puerto Rican song, music and dance form, and North American contemporary dance. This PhD research aims to decolonize culturally hegemonic dance spaces through a cultural syncretism of contemporary dance and the elements of bomba as foundational principles for training both professional and non-professional dancers. This practice challenges traditional Western dance training paradigms. Therefore it is crucial to clarify that this research is not positioned within the realm of pedagogy; rather, it critically interrogates and deconstructs the distinctions between dance practice and …

Diagnosing Long-Term Divergence In Build Back Better Outcomes In Low- And Lower-Middle-Income Communities, E. A. Opabola, M. Baiguera, A. Raby, T. Rossetto, A. Pomonis, E. Meilianda, Y. Idris, S. Nurdin, K. Adams, D. J. Mcgovern, N. Acikgoz, H. Mistry, F. Marafini

School of Engineering, Computing and Mathematics

Low-income communities in disaster-prone regions face persistent challenges in translating post-disaster recovery investments into sustained resilience, as they navigate trade-offs among engineering objectives, policy constraints, and evolving socio-economic needs. Despite the widespread adoption of Build Back Better (BBB) principles, evidence on the long-term sustainability of outcomes remains limited, particularly in low- and lower-middle-income contexts. This study examines the sustainability of BBB outcomes in two Indonesian case studies: Banda Aceh (approximately 18 years after the 2004 Indian Ocean tsunami) and Central Sulawesi (approximately 4 years after the 2018 earthquake, liquefaction, and tsunami). Drawing on structured housing assessments, household surveys, semi-structured interviews, …

Educating The Next Generation Of Engineers: Spatial Thinking Is Key To Their Success, Sheryl Sorby, Gavin Duffy, Jack Parkinson, Gibin Raju, John Lynch, Muhammad Asghar

Research Outputs: 2025-Present

This paper attempts to answer questions regarding the link between engineering success and spatial skills through three themes. The previous research of the authors in examining the questions surrounding each theme is the focus of this paper; we acknowledge that others around the world may have conducted similar research, but the specific findings of the authors are the main focus of this paper. In the first theme, we examine data from several author-led studies where positive correlations have been found between spatial skill levels and proficiency in tasks that are a part of what it means to be an engineer. …

Fault Tolerant Adaptive Control Under Actuator Saturation For Robot Manipulators, D. J. López-Araujo, N. Alvarez-Jarquin, P. Borja, A. T. Becker

School of Engineering, Computing and Mathematics

In practice, actuators inherently have natural limitations that are often overlooked by theoretical control strategies, such as bounded output and discrepancies between calculated and actual responses. Consequently, reliable control techniques must be employed to ensure safe and dependable operation. We propose an adaptive control law for global regulation in the case of partial loss of effectiveness due to input faults. The proposed control strategy maintains the system at the desired configuration while accounting for the physical limitations of the actuators. Additionally, it is suitable for dealing with unknown parameters in the vector of gravitational forces, making it robust to these …

Angle-Based Formation Stabilization And Maneuvers In Port-Hamiltonian Form With Bearing And Velocity Measurements, Ningbo Li, Pablo Borja, Arjan Van Der Schaft, Jacquelien M.A. Scherpen

School of Engineering, Computing and Mathematics

This paper proposes a port-Hamiltonian approach for angle-based formation stabilization and maneuvers using bearing and velocity measurements with an underlying triangulated Laman graph. The corresponding port-Hamiltonian controller is designed using virtual couplings on the errors of angle constraints in angle space. Then, the angle constraints and agent actuators are mapped by the constraint Jacobian, which can be applied to other formation constraints. In addition, to avoid unavailable distance terms in the control law, an estimator is designed based on port-Hamiltonian theory and the property that energy is coordinate-free for different sensor modalities using bearing and velocity measurements, which permits our …

Usable Authentication: Are We There Yet?, Nathan Clarke, Steven Furnell

School of Engineering, Computing and Mathematics

With technology increasingly embedded in everyday life, the demand for secure and usable authentication methods has never been greater. Traditional password-based systems continue to dominate, despite well-known usability and security challenges. This paper explores the evolution of user authentication technologies, from secret knowledge and tokens to biometrics and emerging approaches such as Passkeys. It critically evaluates the extent to which usability has been achieved, identifying both successes—such as biometrics integrated into smartphones—and persistent issues, including inconsistent guidance, ecosystem dependence, and accessibility barriers. Drawing on academic and commercial developments, the discussion highlights the growing burden on users who must authenticate across …

Rapid Electrochemical Biosensor With Enhanced Accuracy And Repeatability Using Pyrene Nhs Ester Modified 3d Graphene Electrodes, S. Nazir, D. Jenkins, G. Pan

School of Engineering, Computing and Mathematics

A consistent and reproducible chemical modification of the transducer interface is essential to ensure effectiveimmobilization of bioreceptors, thereby improving the sensitivity and reproducibility of biosensors. Althoughnotable progress has been made in fabricating graphene-based biosensors, the effects of surface biofunctionalization on sensing performance remain not fully understood. This study shows that combining threedimensional (3D) graphene foam electrodes with a straightforward, single-step surface biofunctionalizationmethod is key to enhancing the performance of graphene-based biosensors. Specifically, it presents a single-stepapproach to functionalize the 3D graphene foam (Gii-Sens) with 1-pyrenebutyric acid N-hydroxysuccinimideester (Pyrene-NHS ester). Electrochemical techniques, including cyclic voltammetry (CV) and differential pulsevoltammetry (DPV), confirmed …

From Predictions To Explanations: Explainable Ai For Autism Diagnosis And Identification Of Critical Brain Regions: 18th International Joint Conference, Biostec 2025, Kush Gupta, Amir Aly, Emmanuel Ifeachor, Rohit Shankar

School of Engineering, Computing and Mathematics

Autism spectrum disorder (ASD) is a neurodevelopmentalcondition characterized by atypical brain maturation. However, the adaptationof transfer learning paradigms in machine learning for ASD researchremains notably limited. In this study, we propose a computeraideddiagnostic framework with two modules. This chapter presents atwo-module framework combining deep learning and explainable AI forASD diagnosis. The first module leverages a deep learning model finetunedthrough cross-domain transfer learning for ASD classification. Thesecond module focuses on interpreting the model’s decisions and identifyingcritical brain regions. To achieve this, we employed three explainableAI (XAI) techniques: saliency mapping, Gradient-weighted Class ActivationMapping, and SHapley Additive exPlanations (SHAP) analysis.This framework demonstrates that …

Biocompatibility Of Silver And Hydroxyapatite Antimicrobial Nanocoatings On Titanium Alloy Dental Implants With Human Gingival Fibroblasts, S Morgan, R D Handy, V M Salih, R N Salaie, C J Tredwin, A Besinis

School of Engineering, Computing and Mathematics

OBJECTIVES: Peri-implantitis compromises soft and hard tissues around dental implants, often leading to implant failure. Antibacterial nanocoatings offer a promising infection control strategy, but maintaining biocompatibility is essential. This study investigated, through a range of assays and analytical techniques, the biocompatibility of antimicrobial silver and hydroxyapatite (HA) nanocoatings applied to titanium implants with human gingival fibroblasts (HGFs).

METHODS: Silver and hydroxyapatite nanocoatings were applied to the surface of titanium alloy specimens using electroplating, deposition and sintering techniques. The biocompatibility of the nanocoatings with HGFs was assessed by measuring cell metabolic activity (alamarBlue), lactate dehydrogenase (LDH) release, collagen production (Sircol assay), …

A Unified Framework For Evaluating Training Efficiency In Deep (Bayesian) Neural Networks: Metrics, Overtraining, Stopping Criteria, And Grokking Computer Science, Eduardo Cueto Mendoza

Doctoral

Measuring training efficiency for artificial neural networks is an open research problem, current literature reports several attempts to define measures or create reporting frameworks. Current methods lack generality as they require measurements of the hardware or software thus, comparing efficiency between different systems can be difficult. Similarly, current metrics or frameworks generally do not propose the use of the metrics to directly improve training efficiency. This thesis presents three main contributions: (1) a novel framework that quantifies the training efficiency of a neural architecture on a learning task as the average ratio of model accuracy to total energy consumption during …

Integrating Environmental Awareness In Underwater Acoustic Networks: A Comprehensive Review, Sadaf Vahabli, Iftekhar Ahmad, Quoc Viet Phung, Daryoush Habibi, Walid K. Hasan, Ruba Zaheer

Research outputs 2022 to 2026

Underwater Acoustic Networks (UANs) are critical for enabling long-range underwater communication, supporting a wide range of applications, such as environmental monitoring, resource exploration, disaster prevention and marine security. Unfortunately, UANs operate within a limited acoustic spectrum, where these spectrums often overlap with those used by marine animals for communication, navigation and foraging. Additionally, anthropogenic noise from industrial activities contributes to acoustic pollution, intensifying this problem. This frequency overlap poses significant risks to marine life. Therefore, this review highlights the urgent need to develop environmentally aware UANs that minimize harmful acoustic interference with marine mammals, fish and invertebrates. Further, it focuses …

Praxis: The Missing Theory To Practice Link In Maximising Early Childhood Science Opportunities, Pauline Roberts, Patricia R. Collins

Research outputs 2022 to 2026

Science teaching in early years contexts has been identified as deficient due to poor educator confidence, lack of content knowledge, or minimal opportunities being made available for science experiences. To identify strategies to counteract these factors, this study explored how these deficiencies are enacted in learning contexts by examining the beliefs and practices of educators across three early learning sites. The focus of this study was threefold: to gather the perception of educators in relation to early childhood science; explore the learning environments; and observe the interactions facilitated by the educators to develop scientific knowledge and skills for young children. …

Snif: Explainability-Driven Feature Selection Through State-Of-The-Art Shap–Nca Intersection Framework For Malware Detection, Sadia Nazim, Syed Shujaa Hussain, Bilal Yousuf, Jawahir Che Mustapha

Research Outputs: 2025-Present

Modern malware detection rely on Deep neural networks to achieve high detection performance by leveraging a large feature space. However, these models often suffer from high dimensionality, limited interpretability, and reduced reliability due to redundant features. As a result, they obscure the true contribution of features to the rationale for the prediction. To address these challenges, this study proposes an explainability-aware feature pruning technique to improve the performance and efficiency of deep neural networks. The key focus of this study is the development of the novel SHAP–NCA Intersection Framework (SNIF), which integrates explainability with feature selection for efficient and trustworthy …