Physical Sciences and Mathematics Commons^™

Constraining The Tectono-Climatic Evolution Of The Northern Rocky Mountains: Insights From Volcanic Glass, Leaf Water, And Leaf Wax Hydrogen Isotopes, Rijumon Nandy

Earth & Environmental Sciences Dissertations

The Eocene-Oligocene period in the North American Cordillera represents a period of great intrigue with complex interactions among tectonics, climate, and surface processes. Although this period is generally associated with synorogenic extension associated with gravitational collapse of the Cordillera, the paleotopographic history remains debated. Existing studies offer conflicting interpretations, suggesting either sustained elevations or modest uplift during this time. Notably, recent reconstructions based on volcanic glass δD values have proposed renewed uplift during the early Oligocene, which needs to be tested with a more extensive and robust dataset. Concurrently, global cooling and aridification during this interval may have been amplified …

Application Paths Of Semantic Modeling In Financial Fraud Detection And Risk Identification, Victor P. Gauthier, Daniel S. Wu

Computer Science Faculty Publications

Financial fraud and risk pose significant threats to economic stability and individual well-being. Traditional detection methods often struggle to keep pace with increasingly sophisticated fraudulent schemes. Semantic modeling, which focuses on understanding the meaning and relationships within data, offers a promising avenue for enhancing fraud detection and risk identification. This review paper explores the application paths of semantic modeling in this domain. We begin with a historical overview of fraud detection techniques, highlighting the limitations of traditional approaches. Subsequently, we delve into core themes, including knowledge graph-based fraud detection and semantic rule-based inference for risk assessment. We then compare and …

Quantum Machine Learning Models: Principles, Frameworks, And Computational Challenges, K. A. Jayabalaji, S. Venkata Anand, Dineshkumar Rajendran, Prasanta Chatterjee Biswas, Sardor Omonov, Rubaid Ashfaq

Computer Science Faculty Publications

Quantum machine learning (QML) has become an optimistic avenue of harnessing quantum computation in data-driven modeling, especially of issues with high dimensionality and complicated correlations. Current methods are generally based on fixed or over-parameterized quantum circuits, and hence restricted to scalability as well as unproductive optimization in real-world hardware. This chapter introduces a hybrid quantum-classical learning system that is adaptive and provides principled quantum data encoding, architecture-conscious variational circuit design and resource-optimal optimization. The technique is based on the concepts of quantum architecture search and subspace-preserving transformations to trade expressiveness with trainability, and discretize the quantum model into a classical …

Variational Autoencoder Inverse Mapper For Extraction Of Compton Form Factors: Benchmarks And Conditional Learning, Douglas Adams, Md Fayaz Bin Hossen, Joshua Bautista, Gia-Wei Chern, Simonetta Liuti, Marie Boër, Marija Čuić, Michael Engelhardt, Gary R. Goldstein, Huey-Wen Lin, Yaohang Li

Computer Science Faculty Publications

Deeply virtual exclusive scattering processes (DVES) serve as precise probes of nucleon quark and gluon distributions in coordinate space. These distributions are derived from generalized parton distributions (GPDs) via Fourier transform relative to proton momentum transfer. QCD factorization theorems enable DVES to be parameterized by Compton form factors (CFFs), which are convolutions of GPDs with perturbatively calculable kernels. Accurate extraction of CFFs from DVCS, benefiting from interference with the Bethe–Heitler (BH) process and a simpler final state structure, is essential for inferring GPDs. This paper focuses on extracting CFFs from DVCS data using a variational autoencoder inverse mapper (VAIM) and …

The Critical Role Of Near-Surface Dynamics In Identifying Spawning Areas Of Atlantic Bluefin Tuna In The Gulf Of Mexico, Donald R. Johnson, William J. Teague, Harriet Perry, James S. Franks

Gulf and Caribbean Research

Atlantic Bluefin Tuna, Thunnus thynnus (ABFT), migrate long distances to broadcast spawn in the Gulf of Mexico (GOM) basin, the Mediterranean Sea and the Slope Sea (NW Atlantic). These areas have commonalities including a highly dynamic upper ocean eddy environment that draws nutrients from shallow thermoclines and adjacent continental shelves and can provide temporal sanctuaries from some larval predators. In the GOM basin, the Loop Current (LC) and its energetic spin—off eddies sweep weak swimming predators such as jellyfish (Pelagia noctiluca) into lines and aggregations, leaving relatively open areas as temporal sanctuaries for ABFT larvae during the vulnerable …

Students-Generative Ai Interaction Patterns And Its Impact On Academic Writing, Jinhee Kim, Sang-Soog Lee, Rita Detrick, Jialin Wang, Na Li

STEMPS Faculty Publications

Considering both the transformative opportunities and challenges presented by generative AI (GenAI) in academic writing, effectively integrating GenAI into the academic setting becomes a significant need requiring prioritization. Yet, there is limited understanding regarding the nature of interactions between different types of students, what behavioral patterns students exhibit during a student-GenAI interaction (SAI) on a given task, and how these different SAI patterns relate to the actual writing task performance. This study, therefore, aimed to identify SAI patterns of academic writing tasks depending on students’ level of AI literacy and examine the differences in academic writing performance between the identified …

Students' Perception Of Generative Ai-Assisted Collaborative Argumentation, Jinhee Kim, Seongryeong Yu, Rita Detrick, Liangjie Fan, Na Li

STEMPS Faculty Publications

The rapid scaling of generative artificial intelligence (GenAI) technology presents opportunities for personalised learning experiences and facilitates collaborative learning, including collaborative argumentation (CA). However, empirical research examining students' perceptions of GenAI-assisted CA within classroom contexts remains limited. This study explored university students' experiences with GenAI-assisted CA through in-depth interviews with 36 students following a CA activity using a ChatGPT4-embedded argumentation platform developed by the research team. Findings indicate that students viewed GenAI as serving multiple roles, including tool, facilitator, teaching assistant and machine buddy. Students perceived that GenAI-assisted CA could empower task performance and create a collaborative learning environment. Meanwhile, …

Foreword, Pedagogical Innovations In Computer Science Education, Helen Crompton

STEMPS Faculty Publications

[Introduction] Computer science education sits at a defining moment. Across schools and universities worldwide, computing is no longer a niche discipline reserved for a select few. It is a foundational literacy that shapes how learners understand the world, participate in society, and imagine their futures. At the same time, the rapid pace of technological change, particularly in artificial intelligence, data systems, and intelligent tools, has placed unprecedented pressure on educators to rethink not only what we teach, but why and how we teach it. This book arrives precisely when such reflection is most needed.

Standardization Of Neuromuscular Reflex Analysis—Role Of Fine-Tuned Vision-Language Model Consortium And Openai Gpt-Oss Reasoning Llm-Enabled Decision Support System, Eranga Bandara, Ross Gore, Sachin Shetty, Ravi Mukkamala, Christopher K. Rhea, Brittany S. Samulski, Amin Hass, Atmaram Yarlagadda, Shaifali Kaushik, Malith De Silva, Andriy Maznychenko, Inna Sokolowska, Kasun De Zoysa

VMASC Publications

Background/Objectives: Accurate assessment of neuromuscular reflexes, such as the Hoffmann reflex (H-reflex), plays a critical role in sports science, rehabilitation, and clinical neurology. Conventional interpretation of H-reflex electromyography (EMG) waveforms is subject to inter-rater variability and interpretive bias, limiting reliability and standardization. This study aims to develop an automated, interpretable, and robust agentic AI–driven framework for H-reflex waveform analysis. Methods: We propose a fine-tuned Vision–Language Model (VLM) consortium combined with a reasoning Large Language Model (LLM)–enabled decision support system for automated H-reflex interpretation. Multiple VLMs were fine-tuned on curated datasets of H-reflex EMG waveform images annotated with expert clinical observations, …

Modeling Rank Distribution And The Relative Importance Factor Index In Discrete Power-Law Models: Application To Social Resilience Using The Scopus Database, Brian Llinas, Jose Padilla, Humberto Llinas, Erika Frydenlund, Katherine Palacio

VMASC Publications

Prior research on power-law distributions has primarily focused on modeling frequency patterns, with less attention given to rank distributions and how ranked positions reflect relative importance among elements. In discrete power-law distributions, frequency-based metrics often provide limited discrimination in the tail, where elements may exhibit similar counts but differ in relative dominance. These patterns are especially evident, for instance, in academic publishing, where keywords, affiliations, and citations commonly exhibit power-law behavior. To address this limitation, we introduce the Relative Importance Factor (RIF) Index, a statistical measure derived from the estimated discrete power-law rank distribution rather than an additional independent parameter. …

Element-Based Predictive Modeling Of Hydrothermal Liquefaction Bioproducts Derived From Corn Stover, Isamu Umeda, Meicen Liu, Yi Zheng, Jiefu Wang, Zhiwu Wang, Sandeep Kumar

Civil & Environmental Engineering Faculty Publications

The hydrothermal liquefaction (HTL) process offers an energetic advantage over pyrolysis because it does not require prior drying of the biomass feedstock. However, there are significant challenges in simultaneously estimating both the yields and characteristics of products from the HTL of biomass with theoretical support. This study developed a unique element-based kinetic model to predict the yields, higher heating values, and fuel characteristics of solid residue and heavy bio-oil, based on the temperature, residence time, solid loading, and elemental composition (C, H, N, and O) of corn stover. Furthermore, the model predicted the weights of dissolved carbon and nitrogen in …

Nonstationary Spatial Correlation Of Earthquake Ground Motions In California, Pengfei Wang, Busra Bocekli, Junhui Yang, Scott J. Brandenberg, Jonathan P. Stewart

Civil & Environmental Engineering Faculty Publications

Assessing seismic risk to spatially distributed infrastructure systems requires realistic representations of spatially correlated ground motions. Existing models for the spatial correlations of ground motions rely on strong second-order stationarity assumptions, under which the correlation structure is assumed to be invariant across space, potentially masking regional variations. Because repeatable site and path effects can vary spatially, the resulting correlation structure is likely to be nonstationary. We propose a nonstationary spatial correlation method that captures geographically varying correlation decay behavior. We compute site-to-site Pearson correlations of within-event residuals using earthquakes recorded at both sites in each site pair and model the …

Sustainable Justice: A Critical Contemplative Approach To Sustainability In Higher Education, Jessica A. Reneau

Antioch University Dissertations & Theses

The field of sustainability studies increasingly asks students to confront ecological crises alongside considerations of power, privilege, and unequal environmental harm. This work can be emotionally challenging as well as intellectually complex. Contemplative practices, such as meditation and yoga, may offer meaningful pedagogical support, helping students cultivate the attention, reflection, and emotional capacity needed to stay engaged with justice-centered sustainability learning. Although the use of contemplative pedagogies is growing in higher education, little research has examined how these practices shape justice-orientated learning outcomes in sustainability education. This dissertation addresses that gap by exploring how both student and instructor experience contemplative …

News Media Sentiment Toward Chinese Ai: A Comparative Analysis With Belt And Road Initiative Involvement And Public Opinion On China, Asya Vaisberg

Pomona Senior Theses

This study evaluates different countries' news media’s sentiment towards Chinese AI, between May 2023 and May 2024, by using Microsoft Azure NLP Sentiment Analysis. The results are then compared with the country’s public opinion on China and its involvement in the Belt and Road Initiative (BRI). For this study 12 countries have been selected which are USA, Australia, Pakistan, Peru, Russia, Romania, Italy, Greece, Portugal, Philippines, Brazil, and Egypt. For each one, GNews Application Programming Interface (API), which has access to more than 60,000 global news sources, was used to aggregate relevant news articles based on queried keywords. The collected …

High Algal Biomass Is Decoupled From Metabolism In The Gallatin River, Cora Mae Steinbach

Graduate Student Theses, Dissertations, & Professional Papers

Assessing eutrophication in rivers is difficult compared to lakes and coastal waters, because most algal biomass occurs on the riverbed and flows interact and co-vary with production (Biggs and Close, 1989; Bernhardt et al., 2018). Riverine eutrophication is typically assessed using algal biomass and water column nutrients (U.S. Environmental Protection Agency, 2000), but biomass is highly variable and labor-intensive to measure, while nutrient concentrations often underestimate enrichment due to rapid biological uptake (Dodds and Smith, 2016). Reach-scale river metabolism can help evaluate long-term functional change in rivers recovering from nutrient enrichment (Arroita et al., 2019; Jankowski et al., 2021; Diamond …

An Llm-Driven System For Doctor-Patient Simulation, Akilan Amithasagaran

Computer Science Theses

Effective physician-patient communication is fundamental to clinical competence, yet traditional simulation-based training methods using standardized patients and high-fidelity manikins are costly, resource-intensive, and difficult to scale. This dissertation presents CLiVR (Conversational Learning system in Virtual Reality), an LLM-driven system that integrates large language models and 3D avatars to simulate doctor-patient interactions for medical communication training.

CLiVR addresses three key limitations in existing virtual reality medical training platforms. First, the system operates on standalone Meta Quest 3 hardware with realistic 3D patient avatars featuring synchronized lip movements and speech-based interaction. Second, CLiVR grounds LLM responses using a curated syndrome-symptom database, constraining …

Bail Reform, Large Language Model Risk And Reasoning, William Wyatt

CGU Theses & Dissertations

This dissertation contains three studies. Each asks how rules or language change the choices people and machines make when outcomes are uncertain. The first study, written with Kiran John, evaluates California’s 2020 cashless bail reform. We use propensity score matching on arrestee records from the windows before and after implementation, and we test whether the shift away from cash bail produced any effect on subsequent offending. It did not. Matched comparisons yield small, statistically insignificant differences across every window we examined. That null result cuts against both sides of the public argument. The reform did not drive a spike in …

Digital Twin Technologies For Battery Systems: Advancements, Applications, And Future Directions, Seyed Saeed Madani, Yasmin Shabeer, Michael Fowler, Satyam Panchal, Carlos Ziebert, Hicham Chaoui, François Allard

Electrical & Computer Engineering Faculty Publications

The relationships among deep learning, edge computing, artificial intelligence (AI), and the most recent advancements in digital twin (DT) technology for battery energy storage systems are discussed in this paper. The study highlights the need for improved cloud-edge coordination, AI model development, and stronger cybersecurity features by demonstrating real-world applications of digital twin technology in electric vehicles (EVs), aircraft, and grid storage. It also described DT-based structures for fault detection, real-time monitoring, and optimization through standardization and battery management system (BMS) fusion. Because DT-based solutions for distributed energy resources (DERs) offer improved energy management systems, various studies have been conducted …

Generative Ai And Llm Applications In Renewable Energy And Smart Grids: A Systematic Review For The Sustainable Energy Transition, Umit Cali, Ugur Halden, Merlinda Andoni, Ferhat Ozgur Catak, Si Chen, Benoit Couraud, Emre Kantar, Samuel Knapper, Ibrahim Kucukdemiral, Huseyin Kusetogullari, Murat Kuzlu, Yashar Mousavi, Sonam Norbu, Taha Selim Ustun, David Flynn

Engineering Technology Faculty Publications

The global energy transition toward decarbonization and digitalization requires advanced methods to manage decentralized, data-intensive cyber-physical energy systems. This systematic review analyzes 106 research studies on Generative Artificial Intelligence (GenAI) and Large Language Models (LLMs) in renewable energy and smart grids, organized into seven application clusters covering forecasting, system design, operation, reliability, data and cybersecurity, and energy markets. The review situates these applications within a Cyber-Physical-Social Systems (CPSS) framework. Results show that GANs dominate current applications (47.2%), followed by LLMs (10.4%) and VAEs (9.4%), with growing adoption of diffusion and score-based models (7.5% each). Selected studies report improved probabilistic forecasting …

Improving Medical Diagnostics With Vision-Language Models: Convex Hull-Based Uncertainty Analysis, Ferhat Ozgur Catak, Murat Kuzlu, Taylor Patrick, Michel Audette

Engineering Technology Faculty Publications

In recent years, vision-language models (VLMs) have been applied to various fields, including healthcare, education, finance, and manufacturing, with remarkable performance. However, concerns remain regarding VLMs’ consistency and uncertainty, particularly in critical applications such as healthcare, which demand a high level of trust and reliability. This paper proposes a novel approach to evaluate uncertainty in VLMs’ responses using a convex hull approach on a healthcare application for visual question answering (VQA). For any VLM, temperature refers to a sampling parameter used in probabilistic generation, which controls the randomness of the model’s output. The LLM-CXR model is selected as the medical …

Adaptive Electric Vehicle Routing And Charging With Deep Reinforcement Learning, Mandana Farhang Ghahfarokhi, Hyoshin Park, Venktesh Pandey, Gyugeun Yoon

Engineering Management & Systems Engineering Faculty Publications

As electric vehicles (EVs) gain popularity, efficient routing and charging solutions remain challenging due to time-dependent travel variability, sparse charging infrastructure, and heterogeneous user preferences. To address these challenges, this paper introduces a decision-support system that integrates three complementary methods: Temporal Multimodal Multivariate Learning (TMML) for real-time characterization of travel time uncertainty, Time-Dependent Shortest Path (TDSP) for reliability-aware route choice, and Deep Q-Network (DQN) reinforcement learning for adaptive charging decisions in sparse infrastructure environments. TMML updates link-level travel time distributions in real-time through Bayesian inference with cluster-based propagation, reducing uncertainties across the network. TDSP leverages these updated distributions to estimate …

Multiple Changepoint Detection For Non-Gaussian Time Series, Robert Lund, Thomas J. Fisher, Norou Diawara, Michael Wehner

Mathematics & Statistics Faculty Publications

This article combines methods from existing techniques to identify multiple changepoints in non‐Gaussian autocorrelated time series. A transformation is used to convert a Gaussian series into a non‐Gaussian series, enabling penalized likelihood methods to handle non‐Gaussian scenarios. When the marginal distribution of the data is continuous, the methods essentially reduce to the change of variables formula for probability densities. When the marginal distribution is count‐oriented, Hermite expansions and particle filtering techniques are used to quantify the scenario. Simulations demonstrating the efficacy of the methods are given and two data sets are analyzed: 1) the proportion of home runs hit by …

Adaptive Multi-Grade Deep Learning For Highly Oscillatory Fredholm Integral Equations Of The Second Kind, Jie Jiang, Yuesheng Xu

Mathematics & Statistics Faculty Publications

This paper studies the use of Multi-Grade Deep Learning (MGDL) for solving highly oscillatory Fredholm integral equations of the second kind. We provide rigorous error analyses of continuous and discrete MGDL models, showing that the discrete model retains the convergence and stability of its continuous counterpart under sufficiently small quadrature error. We identify the DNN training error as the primary source of approximation error, motivating a novel adaptive MGDL algorithm that selects the network grade based on training performance. Numerical experiments with highly oscillatory (including wavenumber 500) and singular solutions confirm the accuracy, effectiveness and robustness of the proposed approach.

A New Parallel-In-Time Direct Inverse Method For Nonlinear Differential Equations, Nail K. Yamaleev, Subhash Paudel

Mathematics & Statistics Faculty Publications

We propose a new method for parallelization of the first-order backward difference discretization (BDF1) of the first-order time derivative in nonlinear partial differential equations, such as conservation law equations. The time derivative term is discretized by using the method of lines based on the implicit BDF1 scheme, while the inviscid and viscous terms are approximated by conventional 2nd-order central discretizations of the 1st- and 2nd-order derivatives in each spatial direction. The global system of nonlinear discrete equations in the space-time domain is solved by the Newton method for all time levels simultaneously. For the BDF1 discretization, this all-at-once system at …

An Ensemble Classifier For Ordinal Outcomes In High-Dimensional Genomics Data, Heranga K. Rathnasekara, Sinjini Sikdar

Mathematics & Statistics Faculty Publications

Analysis of genomics data for predicting disease outcomes is a fast-growing field in medical research. There often exist categorical, specifically, ordinal outcomes that need to be predicted based on genomic profiles. This has led to recent development of some high-dimensional ordinal classification methods that can address the large dimensionality of the genomic covariate set. These high-dimensional ordinal models tend to vary widely in their performance depending on the data they are applied to and the evaluation criteria used. In this article, we outline an ensemble ordinal classifier that integrates different ordinal modeling approaches through bootstrap-based model evaluation, multi-metric performance assessment, …

Testing The Limits Of Provenance Analysis From Basaltic Fluvial Sediment Near Sandvatn, Iceland, As A Mars Analog, Audrey R. Putnam, Kirsten L. Siebach, Michael T. Thorpe, Valerie M. Tu, Elizabeth B. Rampe, Candice C. Bedford, Gelu Costin, Joseph J. Tamborski

OES Faculty Publications

Detrital sediments that accumulate downstream and are preserved in sedimentary rocks can allow characterization of geologic formations that are inaccessible for spatial or temporal reasons. However, mixing, sorting, and alteration of sediment during transport may complicate reconstruction of protolith characteristics. We test the preservation of three key provenance signals in coarse fluvial sand at a Mars analog watershed in Iceland to determine whether detrital sediments capture (a) watershed magmatic chemical variation, (b) textural indicators of lava-water interaction during eruption and cooling, and (c) hydrothermal alteration. Specifically, we tested whether diagnostic variations in rock mineralogy, chemistry, and texture can be recovered …

Evaluating Species At Risk In Data-Limited Fisheries: A Productivity-Susceptibility Analysis For Marine Aquarium Fish, Gabrielle A. Baillargeon, Alice A. Wynn, Jemelyn Grace P. Baldisimo, Michael F. Tlusty, Andrew L. Rhyne

Biological Sciences Faculty Publications

The marine aquarium trade (MAT) is a significant global industry harvesting millions of wild-caught, live coral reef fishes for public and private aquaria markets in the United States and Europe annually, while supporting fisher livelihoods in the Indo-Pacific. This diverse and species-rich trade is considered data-limited, creating barriers to quantifying the current and future socio-ecological sustainability of the fishery. We present a revised and expanded productivity–susceptibility analysis (PSA) that serves as a holistic risk assessment to estimate the vulnerability of marine aquarium fish to overfishing. Our global analysis includes 306 species that are actively in trade. Improvements to the PSA …

Does Fishery Management For Groupers (Teleostei: Epinephelidae) Protect Them Effectively? Context From The Iucn's Red List Of Threatened Species, Sean T. Fennessy, Christi Linardich, Kevin Rhodes, Joao P. Barreiros, David Pollard, Eloy Sosa-Cordero, Felicia Coleman, Alfonso Aguilar-Perera, Christopher R. Malinowski, Thierry Brulé, Pedro Afonso, Kayan Ma, Min Liu, Muktha Menon, Colin Wen, Stanley K. H. Shea, Sean N. Porter, Matthew Craig, Yvonne Sadovy De Mitcheson

Biological Sciences Faculty Publications

Worldwide, groupers (Epinephelidae) are commercially valued fishes, which also play key ecological roles on tropical and subtropical reefs. In 2007 and 2016, the IUCN's Groupers and Wrasses Specialist Group assessed all 160+ grouper species, with 17 of these being identified as threatened in 2016 and the major threat factor being overexploitation. Our present study aimed to identify whether management measures (MMs) for previously assessed groupers were established, whether these measures aligned with IUCN's Red List categories, and whether they effectively protect grouper populations. Experts in grouper biology and management assigned scores per grouper species based on the extent to which …

Three Decades Of Classifying Threatened Species: Lessons Learned From And About The Iucn Red List Criteria For Quantifying Extinction Risk, H. Resit Akçakaya, E. J. Milner-Gulland, Mike Hoffmann, Helen M. Regan, Ilona Naujokaitis-Lewis, Andre E. Punt, Kevin J. Gaston, David A. Keith, Stuart H. M. Butchart, Simon N. Stuart, Nigel J. Collar, Moreno Di Marco, Monika Bohm, Alex J. Berryman, David P. Mallon, Axel Hochkirch, Nicholas K. Dulvy, Malin Rivers, Thomas M. Brooks, James R. S. Westrip, Jonathan Paul Rodriguez, Craig Hilton-Taylor, Janet Scott, Simon Tarr, Sophie H. Ledger, Christi Linardich

Biological Sciences Faculty Publications

The IUCN Red List of Threatened Species, the most widely used global system for assessing species' extinction risk, has become a foundational source of information for conservation management, policy and research. Since the adoption of quantitative extinction risk criteria more than three decades ago, the Red List has expanded substantially in scope and influence, informing decisions ranging from species conservation and protected area designation to international agreements, corporate risk assessments and global biodiversity indicators. Given its central role, maintaining scientific rigour, transparency and trust in the Red List system is essential. Feedback from users, emerging from evolving applications and scientific …

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 …