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The Coherence Of Navayāna Pragmatism: Ambedkar And The Pragmatist Reconstruction Of Buddhist Philosophy, Danish Hamid Jan 2026

The Coherence Of Navayāna Pragmatism: Ambedkar And The Pragmatist Reconstruction Of Buddhist Philosophy, Danish Hamid

Comparative Philosophy

This paper examines the philosophical coherence of B.R. Ambedkar’s reconstruction of his (Navayāna) Buddhism in relation to John Dewey’s pragmatism. Building on Scott Stroud’s reading of Ambedkar as a Deweyan Pragmatist (2017, 2023), it argues that Dewey’s evolutionary naturalism, his conception of growth, and pragmatic redefinition of concepts provide Ambedkar with the methodological tools for a naturalistic and socially reconstructive reinterpretation of Buddhist doctrines such as anicca (impermanence), dhamma (Buddha’s teaching), nirvāṇa (enlightenment), karuna (compassion), etc. Ambedkar transforms impermanence from a metaphysics of flux into an ethics of progressive growth, and recasts dhamma as a social morality grounded in fraternity …

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Challenging The Double-Negation Principle: An Analysis Of Strategic Theories Of Sun Zi, Carnot, And Clausewitz Through Intuitionist Logic, Antonino Drago Jan 2026

Challenging The Double-Negation Principle: An Analysis Of Strategic Theories Of Sun Zi, Carnot, And Clausewitz Through Intuitionist Logic, Antonino Drago

Comparative Philosophy

To study a subject similarly represented in Eastern and Western cultures, I examine some classical theories of war strategy according to a new analytical method. This method is based on the recognition that the most important strategic theories are organized as theories essentially not deduced from axioms according to classical logic. Such theories reason through doubly negated propositions (DNPs), whose meanings differ from those of their affirmative counterparts. This corresponds to the use of intuitionist logic instead of classical logic. An inspection of the works of the texts of Sun Zi, Lazare Carnot, and Clausewitz reveals many such propositions, and …

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Vol 17 No 1 Information Page Jan 2026

Vol 17 No 1 Information Page

Comparative Philosophy

No abstract provided.

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Implementing Riverine Biogeochemical Inputs In Ecco-Darwin: A Sensitivity Analysis Of Terrestrial Fluxes In A Data-Assimilative Global Ocean Biogeochemistry Model, Raphaël Savelli, Dustin Carroll, Dimitris Menemenlis, Jonathan M. Lauderdale, Clément Bertin, Stephanie Dutkiewicz, Manfredi Manizza, A. Anthony Bloom, Karel Castro-Morales, Charles E. Miller, Marc Simard, Kevin W. Bowman, Hong Zhang Jan 2026

Implementing Riverine Biogeochemical Inputs In Ecco-Darwin: A Sensitivity Analysis Of Terrestrial Fluxes In A Data-Assimilative Global Ocean Biogeochemistry Model, Raphaël Savelli, Dustin Carroll, Dimitris Menemenlis, Jonathan M. Lauderdale, Clément Bertin, Stephanie Dutkiewicz, Manfredi Manizza, A. Anthony Bloom, Karel Castro-Morales, Charles E. Miller, Marc Simard, Kevin W. Bowman, Hong Zhang

Faculty Research, Scholarly, and Creative Activity

Terrestrial sources of carbon and nutrients drive biogeochemical cycles in coastal regions and in the global ocean. Quantifying their impact on the spatiotemporal variability of the ocean carbon cycle is pivotal to understanding the distinctive characteristics of ocean basins dominated by riverine inflow. ECCO-Darwin is a data-constrained, global-ocean biogeochemistry model that has heretofore lacked lateral inputs of carbon and nutrients. The objective of this study is to add this new capability to ECCO-Darwin and to carry out a suite of sensitivity experiments in order to quantify the impact of these lateral fluxes on coastal- and open-ocean biogeochemistry. In this work, …

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Enhancing The Mechanical Properties Of Epoxy With Graphene Quantum Dots: A Molecular Dynamics Study, Swapnil S. Bamane, Prathamesh P. Deshpande, Folarin Erogbogbo, Ozgur Keles Jan 2026

Enhancing The Mechanical Properties Of Epoxy With Graphene Quantum Dots: A Molecular Dynamics Study, Swapnil S. Bamane, Prathamesh P. Deshpande, Folarin Erogbogbo, Ozgur Keles

Faculty Research, Scholarly, and Creative Activity

Polymers are the key to the next generation of high-performance materials for their applications in aerospace, defense, and energy. Thermoset polymers like epoxy are widely used as a matrix material in composites due to their ease of processability and compatibility with reinforcement materials. There have been significant efforts in the development of polymer materials properties using nanofillers such as graphene, carbon nanotubes, rubber, and metal oxides. However, graphene quantum dots (GQD), a zero-dimensional nano-scale filler material, have not been extensively explored towards improvement of the mechanical properties of polymers. The objective of this study is to understand the effects of …

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Spartan Daily, January 22, 2026, San Jose State University, School Of Journalism And Mass Communications Jan 2026

Spartan Daily, January 22, 2026, San Jose State University, School Of Journalism And Mass Communications

Spartan Daily, 2026

Volume 166, Issue 1

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The Effect Of Fluorides In The Tio2(B) Anode On The Hydrogen Evolution Reaction In Aqueous Electrolytes, Khoi-Nguyen Nguyen, Lam Hoang Nguyen, Jozel John Salvacion, Nam Huu Nhat Nguyen, Samuel Ming Tuk Yeung, Seung Woo Lee, Liat Rosenfeld, Chengyu Song, Dahyun Oh Jan 2026

The Effect Of Fluorides In The Tio2(B) Anode On The Hydrogen Evolution Reaction In Aqueous Electrolytes, Khoi-Nguyen Nguyen, Lam Hoang Nguyen, Jozel John Salvacion, Nam Huu Nhat Nguyen, Samuel Ming Tuk Yeung, Seung Woo Lee, Liat Rosenfeld, Chengyu Song, Dahyun Oh

Faculty Research, Scholarly, and Creative Activity

For aqueous lithium-ion batteries (A-LIBs), the hydrogen evolution reaction (HER) poses a significant challenge, as it competes with the primary electrochemical processes of the anode, resulting in capacity loss and reduced cycling stability. In this study, we investigate the use of fluorine-based additives in anodes to mitigate HER in LIBs with aqueous electrolytes including low or high amounts of salt (water-in-salt electrolytes (WiSE)). We synthesized and incorporated three distinct materials into TiO2(B) anodes: aluminum fluoride (AlF3), lithium fluoride (LiF), and 1H,1H,2H, 2H-perfluorooctyltriethoxysilane (FAS) using a solution-based method. Among these fluorides containing composite anodes, FAS containing anodes delayed HER onset potentials …

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Flip This Library Again! A Metaphorphosis: Evolving The School Library Into An Innovation Lab & Library Learning Commons (Illc), David Vickers Loertscher, Fran Kompar Jan 2026

Flip This Library Again! A Metaphorphosis: Evolving The School Library Into An Innovation Lab & Library Learning Commons (Illc), David Vickers Loertscher, Fran Kompar

Learning Hub

Drawing on significant reform education voices, the authors propose that credentialed school librarians add to their various library or learning commons services an innovation laboratory. Like a true laboratory, this includes physical and virtual space tests of major ideas, learning strategies and initiatives by administrators, teachers, and students as a major collaborative effort of the credentialed librarian. Such real world experiments happen in the spirit of a try before you buy strategy where some ideas will succeed, fail, or need modification before going viral in the school. It also places the library at the center of teaching and learning in …

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Marine Cold-Spells In The California Current System: Modeling Changes In Frequency And Impacts On Endangered Species Habitat, Kaila J. Frazer, Heather M. Welch, Michael G. Jacox, Nerea Lezama-Ochoa, Briana Abrahms, Mercedes Pozo Buil, Scott R. Benson, Daniel M. Palacios, L. David Smith, Thomas A. Clay, Steven J. Bograd, Elliott L. Hazen Jan 2026

Marine Cold-Spells In The California Current System: Modeling Changes In Frequency And Impacts On Endangered Species Habitat, Kaila J. Frazer, Heather M. Welch, Michael G. Jacox, Nerea Lezama-Ochoa, Briana Abrahms, Mercedes Pozo Buil, Scott R. Benson, Daniel M. Palacios, L. David Smith, Thomas A. Clay, Steven J. Bograd, Elliott L. Hazen

Faculty Research, Scholarly, and Creative Activity

Marine cold-spells are an understudied phenomena which can both negatively impact marine wildlife and provide thermal refugia for species displaced by climate change. To develop forward-looking and climate-ready management schemes, it is critical to examine how marine species respond to cold-spells, how long-term warming will affect marine cold-spells over the next century, and how these future cold-spells will in turn affect species of conservation concern, particularly in marine protected areas. To this end, we detect marine cold-spells across the California Current System, a productive Eastern Boundary Upwelling System, relative to a fixed baseline (1980–2009) and to a detrended time series …

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Archeota, Fall/Winter 2025, Audie Robinson, Vanessa Sztym, Emily Stockton, Heather Arnold, Niamh Tangney, Gianna Elena Brassil Jan 2026

Archeota, Fall/Winter 2025, Audie Robinson, Vanessa Sztym, Emily Stockton, Heather Arnold, Niamh Tangney, Gianna Elena Brassil

Archeota

Archeota is a platform for SJSU iSchool students to contribute to the archival conversation. It is written BY students, FOR students. It provides substantive content on archival concerns and issues and promotes professional development in the field of archival studies. Archeota upholds the core values of the archival profession. Contents: Unrecorded Legacies: Why Masonic Archives Matter by Audie Robinson, Book Review: Decolonial Archival Futures by Vanessa Sztym, Passports to the Past: Following the Trail of Sacramento’s Archives Crawl by Emily Stockton, Volunteering at the Jack Mason Museum of West Marin History: Beginnings and Belonging by Heather Arnold, Images From The …

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Hybridsense-Llm: A Structured Multimodal Framework For Large-Language-Model–Based Wellness Prediction From Wearable Sensors With Contextual Self-Reports, Cheng Huan Yu, Mohammad Masum Jan 2026

Hybridsense-Llm: A Structured Multimodal Framework For Large-Language-Model–Based Wellness Prediction From Wearable Sensors With Contextual Self-Reports, Cheng Huan Yu, Mohammad Masum

Faculty Research, Scholarly, and Creative Activity

Wearable sensors generate continuous physiological and behavioral data at a population scale, yet wellness prediction remains limited by noisy measurements, irregular sampling, and subjective outcomes. We introduce HybridSense, a unified framework that integrates raw wearable signals and their statistical descriptors with large language model–based reasoning to produce accurate and interpretable estimates of stress, fatigue, readiness, and sleep quality. Using the PMData dataset, minute-level heart rate and activity logs are transformed into daily statistical features, whose relevance is ranked using a Random Forest model. These features, together with short waveform segments, are embedded into structured prompts and evaluated across seven prompting …

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Intelligent Prediction Model For Icing Of Asphalt Pavements In Cold Regions Oriented To Geothermal Deicing Systems, Junming Mo, Ke Wu, Jiading Jiang, Lei Qu, Wenbin Wei, Jinfu Zhu Jan 2026

Intelligent Prediction Model For Icing Of Asphalt Pavements In Cold Regions Oriented To Geothermal Deicing Systems, Junming Mo, Ke Wu, Jiading Jiang, Lei Qu, Wenbin Wei, Jinfu Zhu

Faculty Research, Scholarly, and Creative Activity

To address traffic safety hazards from asphalt pavement icing in Xinjiang’s cold regions and inefficiencies of conventional deicing and imprecise geothermal deicing systems, this study focused on local asphalt surfaces. Using “outdoor qualitative screening and indoor quantitative verification”, key variables were identified via controlled tests and their coupling effects on the time to complete icing were quantified through an L16(44) orthogonal test (a 4-factor, 4-level design encompassing 16 test groups). A Backpropagation (BP) neural network model (3 inputs, 5 hidden neurons, and a learning rate of 0.7) optimized with 64 datasets was established to predict the time to …

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Generative Algorithms For Wildfire Progression Reconstruction From Multi-Modal Satellite Active Fire Measurements And Terrain Height, Bryan Shaddy, Brianna Binder, Agnimitra Dasgupta, Haitong Qin, James Haley, Angel Farguell, Kyle Hilburn, Derek V. Mallia, Adam Kochanski, Jan Mandel, Assad A. Oberai Jan 2026

Generative Algorithms For Wildfire Progression Reconstruction From Multi-Modal Satellite Active Fire Measurements And Terrain Height, Bryan Shaddy, Brianna Binder, Agnimitra Dasgupta, Haitong Qin, James Haley, Angel Farguell, Kyle Hilburn, Derek V. Mallia, Adam Kochanski, Jan Mandel, Assad A. Oberai

Faculty Research, Scholarly, and Creative Activity

Highlights: What are the main findings? Conditional generative algorithms trained on simulations of historic wildfires may be used to effectively reconstruct the early-time progression of wildfires given satellite active fire measurements and terrain height data. When applied to real wildfires, generated fire progression estimates compare favorably to ground-truth high resolution infrared perimeters measured via aircraft, with the ability to gather additional information about model uncertainty from generated samples. What are the implications of the main findings? Once obtained, fire progression estimates may be used to perform data assimilation, wherein the estimated fire state is used to initialize subsequent wildfire spread …

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Evaluating Impact Of Occupational Exoskeletons On Physical Fatigue Using Wearable Sensors And Deep Learning, Hardik Vora, Fatemeh Davoudi Kakhki, Armin Moghadam Jan 2026

Evaluating Impact Of Occupational Exoskeletons On Physical Fatigue Using Wearable Sensors And Deep Learning, Hardik Vora, Fatemeh Davoudi Kakhki, Armin Moghadam

Faculty Research, Scholarly, and Creative Activity

Musculoskeletal disorders remain a leading source of occupational injury in manufacturing and logistics industries, often resulting from repetitive lifting and load transfer tasks. While back-support exoskeletons have shown promise in alleviating physical strain, their biomechanical impact under realistic task conditions and their integration with intelligent monitoring systems require further exploration. This study investigates the efficacy of a passive lumbar-support exoskeleton in reducing muscle fatigue during manual material handling tasks by combining high-resolution surface electromyography (sEMG) data with a deep learning-based classification framework. Ten participants performed lifting and rotational (twisting) tasks with and without exoskeleton assistance, while EMG signals were collected …

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21st-Century Mangrove Expansion Along The Southeastern United States, Lucia I.A. Enes Gramoso, Dustin Carroll, Kyle C. Cavanaugh, Remi Bardou, Michael J. Osland, Tom Van Der Stocken Jan 2026

21st-Century Mangrove Expansion Along The Southeastern United States, Lucia I.A. Enes Gramoso, Dustin Carroll, Kyle C. Cavanaugh, Remi Bardou, Michael J. Osland, Tom Van Der Stocken

Faculty Research, Scholarly, and Creative Activity

Warming winter temperatures are driving range expansion of tropical, cold-sensitive mangroves into temperate ecosystems. Along the Atlantic coast of North America, the mangrove range limit is particularly sensitive to climate variability and historical data demonstrate that the mangrove-salt marsh ecotone on this coast has shifted recurrently during recent centuries. However, a comprehensive understanding of how this mangrove-salt marsh ecotone may shift in the future remains lacking. Here, we combine ensemble forecasting of mangrove distribution for the next century with high-resolution oceanographic dispersal simulations, phenological observations, and historical hurricane data to project future mangrove-salt marsh dynamics at the rapidly changing range …

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Development And Characterization Of Compression-Molded Chitosan Composite Films Reinforced With Silica And Titania Fillers, Bhuvaneswari Koti, Vuong Do, Yanika Schneider, Vimal Viswanathan Jan 2026

Development And Characterization Of Compression-Molded Chitosan Composite Films Reinforced With Silica And Titania Fillers, Bhuvaneswari Koti, Vuong Do, Yanika Schneider, Vimal Viswanathan

Faculty Research, Scholarly, and Creative Activity

This study investigates the development of biodegradable chitosan-based films as a sustainable alternative to conventional non-biodegradable food packaging materials. Chitosan, a naturally occurring polymer, possesses inherent film-forming ability and biodegradability; however, its limited mechanical and thermal properties constrain its practical applications. In this work, chitosan films were fabricated via compression molding, and their thermo-mechanical performance was systematically evaluated. The incorporation of fillers, such as titanium dioxide and silica, resulted in a 50% enhancement in tensile strength and an 86% improvement in flexibility. Further optimization of dual-filler compositions led to an additional 45% increase in elasticity, demonstrating the potential of synergistic …

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Ga2o3 Tcad Mobility Parameter Calibration Using Simulation Augmented Machine Learning With Physics-Informed Neural Network, Edric Khai Jieh Ong, Le Minh Long Nguyen, Matthew Eng, Yuhao Zhang, Hiu Yung Wong Jan 2026

Ga2o3 Tcad Mobility Parameter Calibration Using Simulation Augmented Machine Learning With Physics-Informed Neural Network, Edric Khai Jieh Ong, Le Minh Long Nguyen, Matthew Eng, Yuhao Zhang, Hiu Yung Wong

Faculty Research, Scholarly, and Creative Activity

In this article, we demonstrate the feasibility of performing automatic technology computer-aideddesign (TCAD) parameter calibration and extraction using machine learning (ML), with the machine trained solely by TCAD-simulation data. The methodology is validated using experimental data. Schottky barrier diodes (SBDs) with different effective anode workfunctions (WF) are fabricated with emerging ultrawide bandgap material, Gallium Oxide (Ga2O3), and are measured at various temperatures (T). Their current-voltage (I–V) curves are used for automatic Ga2O3 Philips unified mobility (PhuMob) model parameters calibration. Five critical PhuMob parameters (µmax, µmin, Nref, α, and θ) were calibrated. The machine consists of an autoencoder (AE) and a …

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A Recurrent Neural Network For Forecasting Dead Fuel Moisture Content With Inputs From Numerical Weather Models, Jonathon Hirschi, Jan Mandel, Kyle Hilburn, Angel Farguell Jan 2026

A Recurrent Neural Network For Forecasting Dead Fuel Moisture Content With Inputs From Numerical Weather Models, Jonathon Hirschi, Jan Mandel, Kyle Hilburn, Angel Farguell

Faculty Research, Scholarly, and Creative Activity

This paper proposes a recurrent neural network (RNN) model of dead 10 h fuel moisture content (FMC) for real-time forecasting. Weather inputs to the RNN are forecasts from the High-Resolution Rapid Refresh (HRRR), a numerical weather model. Geographic predictors include longitude, latitude, and elevation. Forecast accuracy is estimated in a study that utilizes a spatiotemporal cross-validation scheme. The RNN is trained on HRRR forecasts and observed FMC from weather station sensors within the Rocky Mountain region in 2023, then used to forecast FMC at new locations for all of 2024. The model is evaluated using a 48 h forecast window. …

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Scoring Equitable Transit: A Data-Driven Framework For Affordable Transit-Oriented Development In California, Ahoura Zandiatashbar, Anton Rozhkov, Stephanie Nemet, Atticus Washington, Mounashree Prasanna Jan 2026

Scoring Equitable Transit: A Data-Driven Framework For Affordable Transit-Oriented Development In California, Ahoura Zandiatashbar, Anton Rozhkov, Stephanie Nemet, Atticus Washington, Mounashree Prasanna

Mineta Transportation Institute

Transit-Oriented Development (TOD) is a cornerstone of California’s climate and land use policy, promising walkable, compact neighborhoods near high-quality transit. However, these benefits are not always distributed equitably. This study introduces a scalable framework for identifying and scoring Affordable Transit-Oriented Development (A-TOD) across the state’s High-Quality Transit Areas (HQTAs). The goal is to equip policymakers, planners, and housing agencies with tools to evaluate station areas based on their physical form, affordability, and equity outcomes. Using a 1.5-mile network-based pedestrian buffer around over 66,000 transit stations, the research team developed a three-stage clustering and scoring system. Station areas were classified by …

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Numerical Modeling Of Climate Change Impacts On Transportation Embankments, Amr Morsy, Mamata Sapkota, Emma Varela, Odalys Portillo Jan 2026

Numerical Modeling Of Climate Change Impacts On Transportation Embankments, Amr Morsy, Mamata Sapkota, Emma Varela, Odalys Portillo

Mineta Transportation Institute

Embankments are essential components of transportation infrastructure, providing crucial support for long stretches of highways, railways, and other routes in California and around the world. Clay embankments are susceptible to weather-related deterioration processes that can gradually compromise their stability and, in some cases, lead to unexpected failures. Climate change, along with the associated shifts in weather patterns, is projected to adversely impact the weather-related deterioration processes, leading to exacerbated failures and/or shorter service life. Additionally, climate change is projected to increase the frequency of extreme precipitation events, leading to an increase in embankment failure potential. This study evaluated (1) the …

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Memory Effects In Many-Body Systems, Jeffrey Beckstrand Jan 2026

Memory Effects In Many-Body Systems, Jeffrey Beckstrand

Master's Projects

This thesis investigates memory effects in many-body systems through the MoriZwanzig Formalism for projected dynamics of a Hamiltonian System which yields the Generalized Langevin Equation (GLE). The GLE is a stochastic differential equation (SDE) that studies the dynamics of observables under the effects of many other observables in the system. Although satisfying, the GLE has a term called the Memory Kernel that encodes the past of the system and introduces a computational challenge by introducing a non-Markovian property to the equation. The kernel is often approximated by introducing a delta function, which simplifies the computation, but at the loss of …

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Impact Of Cigarette Smoke On Immune And Stromal Cells, Justin Wong Jan 2026

Impact Of Cigarette Smoke On Immune And Stromal Cells, Justin Wong

Master's Projects

Cigarette smoke is a plight on human health, being a major contributor to lung diseases. The aryl hydrocarbon receptor (AHR) directs immune regulation and metabolic adaptation in response to environmental toxins. This study investigates transcriptomic changes between wild-type and AHR knockout mouse lung tissue under smoke and air conditions. Single-cell RNA sequencing data were generated by the Butcher Lab and analyzed in Seurat for differential gene expression (DGE) analysis. A two way ANOVA framework was used with DGE analysis to classify genes as smoke induced or suppressed in an AHR dependent or independent manner, and these gene sets were analyzed …

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Safe-R2r: A Safety-Aware And Budget-Optimized Retrieval Controller For Rag Systems, Mandar Sunil Gondane Jan 2026

Safe-R2r: A Safety-Aware And Budget-Optimized Retrieval Controller For Rag Systems, Mandar Sunil Gondane

Master's Projects

Retrieval-Augmented Generation (RAG) improves factual grounding in large language models by incorporating external evidence during inference. However, most RAG systems rely on fixed retrieval strategies that ignore query difficulty, computational cost, and prediction uncertainty. This project introduces SAFE-R2R (Safety-Aware and Budget-Optimized Reason-to-Retrieve), a retrieval controller that treats retrieval as a query-dependent decision problem. SAFE-R2R organizes retrieval into a multi-rung ladder ranging from no retrieval to deeper retrieval with reranking. At each rung, the system generates an answer, computes reliability signals, and combines them into a risk score used within a conformal calibration framework to decide whether to accept the answer …

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Multi-Agent Path Planning And Optimization Using Q-Learning, Chirag Rudrish Jan 2026

Multi-Agent Path Planning And Optimization Using Q-Learning, Chirag Rudrish

Master's Projects

Robot navigation in a multi-agent setting requires a balance between safety and efficiency, especially in dense environments. In these two-dimensional spaces, the scope for geometric errors is much less and could lead to collisions or immobility. This project proposes to address the navigation task using a two-phase path planning pipeline that combines reinforcement learning and convex optimization in a scalable and robust manner. The first phase consists of generating diverse collision-free paths using a Q-learning agent that is trained on a visibility graph representation of the environment. The discretization of the environment using waypoint-based graphs allows the agent to train …

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Cost-Aware Predictive Routing For Vision-Language Models, Sahil Sait Naveed Jan 2026

Cost-Aware Predictive Routing For Vision-Language Models, Sahil Sait Naveed

Master's Projects

Vision-language models (VLMs) are increasingly used for multimodal tasks, but their inference costs vary widely across model tiers. This work presents a predictive routing framework that assigns each query to the most cost-effective VLM by using multimodal embeddings, clustering, and per-cluster model error estimates. Evaluated on six commercial VLMs, the selected router reduces average cost by about 49 percent relative to the quality-first setting on both validation and test. This comes with accuracy losses of 0.4 percentage points on validation and 0.9 on held-out test data. It also outperforms K-NN and ZeroRouter routing baselines while providing lowercost operating points than …

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Stepwise Sudoku Reasoning Training Using Transformers, Lok Man Chu Jan 2026

Stepwise Sudoku Reasoning Training Using Transformers, Lok Man Chu

Master's Projects

Sudoku is a constraint satisfaction problem that serves as a testbed for studying reasoning and stepwise deduction. While many solvers can produce correct solutions, they often fail to generate human interpretable sequences of logically consistent steps. This study investigates whether a transformer trained on stepwise deduction traces can learn to solve Sudoku puzzles through sequential, logically deducible moves. Results show that solve accuracy improves significantly with more training data, reaching approximately 78% at 1500k samples. Stepwise analysis indicates that the model effectively learns simple strategies, achieving near-perfect performance. However, performance on more complex strategies remains limited. Overall, while the model …

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Adaptive Real-Time Fraud Detection Using Online Learning And Explicit Concept-Drift Detection, Purva Govind Tugaonkar Jan 2026

Adaptive Real-Time Fraud Detection Using Online Learning And Explicit Concept-Drift Detection, Purva Govind Tugaonkar

Master's Projects

Real-time credit card fraud detection faces challenges such as extreme class imbalance, delayed feedback, and concept drift in transaction streams. This project implements and evaluates an adaptive streaming fraud detection framework based on three methodologies: (1) online learning with incremental updates, (2) explicit conceptdrift detection using statistical monitoring, and (3) separate models for immediate and delayed supervision, combined with cost-sensitive learning and anomaly detection. The system processes the credit card fraud dataset in a batched streaming fashion, uses multiple online learners and ensembles. Experiments show that online, driftaware models maintain high recall on frauds while controlling false positives under imbalanced …

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Multi-Agent Path Planning And Optimization Using Q-Learning, Chirag Rudresh Jan 2026

Multi-Agent Path Planning And Optimization Using Q-Learning, Chirag Rudresh

Master's Projects

Robot navigation in a multi-agent setting requires a balance between safety and efficiency, especially in dense environments. In these two-dimensional spaces, the scope for geometric errors is much less and could lead to collisions or immobility. This project proposes to address the navigation task using a two-phase path planning pipeline that combines reinforcement learning and convex optimization in a scalable and robust manner. The first phase consists of generating diverse collision-free paths using a Q-learning agent that is trained on a visibility graph representation of the environment. The discretization of the environment using waypoint-based graphs allows the agent to train …

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Detection And Characterization Of Cancer Using Cfdna Fragmentomic Analysis, Guneet Bhogal Jan 2026

Detection And Characterization Of Cancer Using Cfdna Fragmentomic Analysis, Guneet Bhogal

Master's Projects

Cell-free DNA (cfDNA) fragmentomics has emerged as a promising non-invasive approach for detecting and characterizing cancer by analyzing DNA fragmentation patterns. These fragment patterns are unique and can be used to distinguish between tumor and healthy DNA. This paper uses machine learning models to detect cancer by evaluating Shannon entropy of fragment lengths at the first coding exon 1 regions and correlating them to targeted cancer genes. This approach was applied to two datasets consisting of samples with prostate, breast, and lung cancers. Our results indicated that exon 1 fragmentation entropy captures biologically relevant differences in cancer-specific chromatin organization and …

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Exploring Parents’ Perspective Of Outdoor Occupational Therapy Sessions, Harold Ho, Michelle Huynh, Christina Le, Amy Morrison Jan 2026

Exploring Parents’ Perspective Of Outdoor Occupational Therapy Sessions, Harold Ho, Michelle Huynh, Christina Le, Amy Morrison

Master's Projects

Introduction: Outdoor play is recognized as critical for supporting children’s development across physical, cognitive, and social-emotional domains (Firby & Raine, 2022; Walker & Ray, 2024; Zhu et al., 2024). The current literature suggests that outdoor play strengthens executive functioning, early learning skills, and enhances social-emotional competency (Dinkel et al., 2019; Walker & Ray, 2024; Zhu et al., 2024). Furthermore, accessibility to outdoor play is shaped by contextual factors, including parental perceptions of safety, environmental barriers, and broader societal influences (Cox et al., 2025; Faulkner et al., 2025; Jelleyman et al., 2019). The research suggests that common parental concerns have emerged …

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