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Spartan Daily, April 16, 2026, San Jose State University, School Of Journalism And Mass Communications Apr 2026

Spartan Daily, April 16, 2026, San Jose State University, School Of Journalism And Mass Communications

Spartan Daily, 2026

Volume 166, Issue 34

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Uniqueness And Nonlinear Stability Of Positive Entire Solutions In Parabolic-Parabolic Chemotaxis Models With Logistic Source On Bounded Heterogeneous Environments, Tahir Bachar Issa Apr 2026

Uniqueness And Nonlinear Stability Of Positive Entire Solutions In Parabolic-Parabolic Chemotaxis Models With Logistic Source On Bounded Heterogeneous Environments, Tahir Bachar Issa

Faculty Research, Scholarly, and Creative Activity

This paper studies the asymptotic behavior of solutions of the parabolic-parabolic chemotaxis model with logistic-type sources in heterogeneous bounded domains: (formula present) We find parameter regions in which the system has a unique positive entire solution, which is globally asymptotically stable. More precisely, under suitable assumptions on the model’s parameters, the system has a unique positive entire solution (u∗(t, x), v∗(t, x)) such that for any u0 ∈ C0(Ω̅), v0 ∈ W1,∞(Ω̅) with u0, v0 ≥ 0 and u0 ≢ 0, the global classical solution (u(t, x; t0, u0, v0), v(t, x; t0, u0, v0)) of …

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Spatiotemporal Dynamics Of Daily And Per Capita Vmt In California: A County-Level Analysis (2019–2023), Yong Lao, Bo Yang Apr 2026

Spatiotemporal Dynamics Of Daily And Per Capita Vmt In California: A County-Level Analysis (2019–2023), Yong Lao, Bo Yang

Mineta Transportation Institute

Vehicle miles traveled (VMT) is a fundamental metric for assessing mobility trends and infrastructure needs. This study examines the spatial-temporal dynamics of daily VMT (DVMT) and per capita DVMT across California counties from 2019 to 2023, covering the pre-, mid-, and post-pandemic periods via GIS mapping and k-means clustering. To identify determinants of per capita DVMT, we compared traditional linear regression approaches (OLS, Ridge, LASSO, Elastic Net) with ensemble tree-based models. Specific results include: the ensemble models estimated using 2019–2022 data delivered substantially higher accuracy, achieving R² values exceeding 0.98; meanwhile, out-of-sample performance on 2023 data remained robust (R² ≈ …

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Spartan Daily, April 9, 2026, San Jose State University, School Of Journalism And Mass Communications Apr 2026

Spartan Daily, April 9, 2026, San Jose State University, School Of Journalism And Mass Communications

Spartan Daily, 2026

Volume 166, Issue 31

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An Llm-Based Agentic Network Traffic Incident-Report Approach Towards Explainable-Ai Network Defense, Chia Hong Chou, Arjun Sudheer, Younghee Park Apr 2026

An Llm-Based Agentic Network Traffic Incident-Report Approach Towards Explainable-Ai Network Defense, Chia Hong Chou, Arjun Sudheer, Younghee Park

Faculty Research, Scholarly, and Creative Activity

Traditional intrusion detection systems for IoT networks achieve high classification accuracy but lack interpretability and actionable incident-response capabilities, limiting their operational value in security-critical environments. This paper presents a graph-based multi-agent framework that integrates ensemble machine learning with Large Language Model (LLM)-powered incident report generation via Retrieval-Augmented Generation (RAG). The system employs a three-phase architecture: (1) a lightweight Random Forest binary pre-detection, achieving 99.49% accuracy with a 6 MB model size for edge deployment; (2) ensemble classification combining Multi-Layer Perceptron, Random Forest, and XGBoost with soft voting and SHAP-based feature attribution for explainability; and (3) a ReAct-based summary agent that …

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Big Data Research In Library And Information Science: A Bibliometric Analysis In India, Dr. Prafulla Kumar Mahanta Apr 2026

Big Data Research In Library And Information Science: A Bibliometric Analysis In India, Dr. Prafulla Kumar Mahanta

Library Philosophy and Practice (e-journal)

Big Data can be defined as high-volume, high-velocity, and high-variety information assets that demand cost-effective, innovative forms of information processing for improved insight and decision-making. Library and Information Centres generate and manage vast amounts of data through digital catalogs, circulation systems, user queries, and digital repositories. The main purpose of the study is to analyze big data research in Library and Information Science in India from 2015 to 2024. It conducted a quantitative analysis using the Bibliometrix tool on scientific production indexed in the Scopus database. A total of 1639 documents were retrieved and exported in CSV format. MS Office …

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Is Nostalgia The Hidden Mechanic Of Mmorpgs?, Zoee Sowders Apr 2026

Is Nostalgia The Hidden Mechanic Of Mmorpgs?, Zoee Sowders

ART 108: Introduction to Games Studies

Since the advent of online gaming, MMORPG video games have allowed players to socialize and collaborate within the bounds of a virtual world. For many, these online worlds were an escape from their daily lives, a place where they could meet people with similar interests and embark on adventures impossible in real life.1 While many of the early MMORPG games faded into obscurity after the genre lost its shiny new sparkle, there are still a handful today that maintain player counts in the millions even after nearly two decades of operation. Specifically, World of Warcraft, Runescape, and Final Fantasy XIV …

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Clinically Aligned Long-Context Transformers For Cross-Platform Mental Health Risk Detection, Aditya Tekale, Mohammad Masum Mar 2026

Clinically Aligned Long-Context Transformers For Cross-Platform Mental Health Risk Detection, Aditya Tekale, Mohammad Masum

Faculty Research, Scholarly, and Creative Activity

Social media platforms contain rich but noisy narratives of psychological distress, creating opportunities for early mental health risk detection. However, existing datasets capture heterogeneous constructs such as suicide risk severity, depression diagnosis, and DSM-5 symptom presence, and most prior models are trained and evaluated on a single corpus, limiting their clinical alignment and cross-dataset generalizability. In this study, we fine-tune a domain-specific long-document transformer, AIMH/Mental-Longformer-base-4096, for binary mental health risk detection (risk vs. no risk) using two clinically aligned Reddit datasets: the C-SSRS Reddit corpus and the eRisk 2025 depression dataset. To handle long user histories, we introduce an LLM-based …

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Analyzing The Utilisation Of Chatgpt For Academic Purpose: Exploring Student Motivations, Dr. Sankar P, Nandakumar K Mar 2026

Analyzing The Utilisation Of Chatgpt For Academic Purpose: Exploring Student Motivations, Dr. Sankar P, Nandakumar K

Library Philosophy and Practice (e-journal)

While praising ChatGPT for its ability to produce complex and human-like text, it was hailed as the best AI chatbot ever released to the public. It was also noted that the output was on par with what a competent student could do, suggesting that teachers would have significant challenges down the road. The purpose of this study was to investigate what drives students to use ChatGPT for schoolwork. This study used a descriptive research approach to describe the opinions of Arts and Science College students in the Coimbatore District. The study used a questionnaire to get data from the students' …

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Kras4a And Kras4b Show Distinct Lipid-Dependent Regulation Of Ras-Raf Membrane Dynamics, Konstantia Georgouli, Jeremy O.B. Tempkin, Liam G. Stanton, Tomas Oppelstrup, Rebika Shrestha, Timothy S. Carpenter, Fikret Aydin, Xiaohua Zhang, Harsh Bhatia, Yue Yang, Que N. Van, Pedro Andrade Bonilla, Gulcin Gulten, Debanjan Goswami, Francesco Di Natale, Joseph R. Chavez, Joseph Y. Moon, Gautham Dharuman, Nicolas W. Hengartner, For Full Author List, See Comments Below Mar 2026

Kras4a And Kras4b Show Distinct Lipid-Dependent Regulation Of Ras-Raf Membrane Dynamics, Konstantia Georgouli, Jeremy O.B. Tempkin, Liam G. Stanton, Tomas Oppelstrup, Rebika Shrestha, Timothy S. Carpenter, Fikret Aydin, Xiaohua Zhang, Harsh Bhatia, Yue Yang, Que N. Van, Pedro Andrade Bonilla, Gulcin Gulten, Debanjan Goswami, Francesco Di Natale, Joseph R. Chavez, Joseph Y. Moon, Gautham Dharuman, Nicolas W. Hengartner, For Full Author List, See Comments Below

Faculty Research, Scholarly, and Creative Activity

KRAS4a and KRAS4b are important regulators of signaling, and their interactions with the plasma membrane are dynamic and influenced by lipid composition. KRAS 4a and 4b have nearly identical globular domains but differ in their membrane-associated hyper variable region (HVR). The functional distinctions between these isoforms remain unclear, particularly with regards to their dependence on specific lipids and the membrane environment. Previous work showed that the membrane orientation of KRAS4b affects its ability to bind to RAF kinase RBDCRD and that the KRAS–RBDCRD complex adopts different poses on the membrane as well as influences the size and composition of the …

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Development Of A Framework For Identifying Asphalt Pavement Cracking Distresses Using Machine Learning, Dingxin Cheng Mar 2026

Development Of A Framework For Identifying Asphalt Pavement Cracking Distresses Using Machine Learning, Dingxin Cheng

Mineta Transportation Institute

Asphalt pavement cracking is one of the most critical distresses affecting pavement performance and service life. When pavement deteriorates, it can lead to safety hazards, higher vehicle maintenance costs, and expensive repairs for cities and states—making early detection essential for everyone who relies on the roadway system. To address this challenge, the research team developed a prototype cracking identification system that integrates a customized machine learning model with computer vision algorithms. High-resolution images collected from drones or ground-based cameras are processed within the system to automatically detect and classify major cracking types. The core of the framework utilizes the You …

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Thermodynamically Consistent Incorporation Of The Langmuir Adsorption Model Into Compressible Fluctuating Hydrodynamics, Hyun Tae Jung, Hyungjun Kim, Alejandro L. Garcia, Andrew J. Nonaka, John B. Bell, Ishan Srivastava, Changho Kim Mar 2026

Thermodynamically Consistent Incorporation Of The Langmuir Adsorption Model Into Compressible Fluctuating Hydrodynamics, Hyun Tae Jung, Hyungjun Kim, Alejandro L. Garcia, Andrew J. Nonaka, John B. Bell, Ishan Srivastava, Changho Kim

Faculty Research, Scholarly, and Creative Activity

For a gas–solid interfacial system where chemical species undergo reversible adsorption, we develop a mesoscopic stochastic modeling method that simulates both gas-phase hydrodynamics and surface coverage dynamics by coupling the Langmuir adsorption model with compressible fluctuating hydrodynamics. To this end, we derive a thermodynamically consistent mass–energy update scheme that accounts for how the mass and energy variables in the gas and surface subsystems should be updated according to the changes in the number of molecules of each species in each subsystem due to adsorption and desorption events. By performing a stochastic analysis for the ideal Langmuir model and the full …

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Preferred Panethnic Terms Among Latina/O And Hispanic Sexual And Gender Minority People, Alexis Ceja, Nguyen K. Tran, Juan M. Peña, David J. Kinitz, Devin Hursey, Ramon Ramirez, Lilia Cervantes, Micah E. Lubensky, Juno Obedin-Maliver, Annesa Flentje, Mitchell R. Lunn Feb 2026

Preferred Panethnic Terms Among Latina/O And Hispanic Sexual And Gender Minority People, Alexis Ceja, Nguyen K. Tran, Juan M. Peña, David J. Kinitz, Devin Hursey, Ramon Ramirez, Lilia Cervantes, Micah E. Lubensky, Juno Obedin-Maliver, Annesa Flentje, Mitchell R. Lunn

Faculty Research, Scholarly, and Creative Activity

Importance Latino and Hispanic individuals in the US represent a diverse and growing population, including a significant number of sexual and gender minority (SGM) individuals whose identities challenge the use of a single panethnic (ie, ethnicity) label. Little is known about SGM individuals’ preferences for panethnic terms. Objective To understand Latino and Hispanic SGM individuals’ preferences and rationales for panethnic terms. Design, Setting, and Participants This cross-sectional study used survey data from SGM-identifying Latino and Hispanic adults in The PRIDE Study, an online, community-engaged cohort of SGM adults in the US completing 2021 or 2022 annual questionnaires, with qualitative analysis …

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Exploring Music Representation Learning For Detection Of Finer-Grained Details, Vishnu Pendyala, Samhita Konduri, Kriti Pendyala Feb 2026

Exploring Music Representation Learning For Detection Of Finer-Grained Details, Vishnu Pendyala, Samhita Konduri, Kriti Pendyala

Faculty Research, Scholarly, and Creative Activity

The foundational tonic in Indian classical music presents a crucial element for algorithmic understanding. This research investigates the efficacy of modern machine learning techniques for detecting this fine-grained musical attribute from diverse audio representations. Addressing limitations in prior work that used spe-cialized techniques, this study pioneers the integration of audio representations with non-linear dimensionality reduction and machine learning classifiers for tonic classification. The methodology employs various machine learning algorithms on music represented by audio features such as Mel-frequency cepstral coefficients and Mel spectrograms. Spectral analysis using Uniform Manifold Approximation and Projection (UMAP) offers novel insights into music representation learning. The …

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Explainable Multi-Modal Deep Learning For Transparent Cancer Diagnosis: Integrating Radiology, Clinical Features, And Decision Visualization, Sital Dash, Laxmi Bewoor, Yashwant Dongre, Amol Bhosle, Kailas Patil, Shrikant Jadhav, Banani Mohapatra, Bhavnish Walia Feb 2026

Explainable Multi-Modal Deep Learning For Transparent Cancer Diagnosis: Integrating Radiology, Clinical Features, And Decision Visualization, Sital Dash, Laxmi Bewoor, Yashwant Dongre, Amol Bhosle, Kailas Patil, Shrikant Jadhav, Banani Mohapatra, Bhavnish Walia

Faculty Research, Scholarly, and Creative Activity

Introduction: Although artificial intelligence–based cancer diagnostic models have demonstrated strong predictive performance, their lack of transparency and reliance on single-modality data continue to limit clinical trust and adoption. Effectively integrating multi-modal data with interpret-able decision-making remains a key challenge. Methods: We propose an explainable multi-modal deep learning framework that integrates radiological imaging and structured clinical features using attention-based fusion. Image-level explanations are generated using Grad-CAM++, while SHAP is employed to quantify clinical feature contributions, enabling unified and cross-modal aligned interpretation rather than independent uni-modal explanations. The framework was evaluated on publicly available datasets, including CBIS-DDSM mammography, Duke Breast Cancer MRI, …

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Exploring K–12 Teacher Motivation To Engage With Ai In Education, Ethel Tshukudu, Katharine Childs, Gaokgakala Alogeng, Emma R. Dodoo, Douglas R. Case, Tebogo Videlmah Molebatsi Feb 2026

Exploring K–12 Teacher Motivation To Engage With Ai In Education, Ethel Tshukudu, Katharine Childs, Gaokgakala Alogeng, Emma R. Dodoo, Douglas R. Case, Tebogo Videlmah Molebatsi

Faculty Research, Scholarly, and Creative Activity

While global interest in K–12 AI and ML education grows, many African education systems lack foundational computing education beyond basic computer literacy. This creates unique challenges for AI integration in countries where computer science isn’t part of the K–12 curriculum. Teachers are central to this effort, but little is known about what motivates them to engage with these technologies or how they use them. This study examined what motivates K–12 teachers to engage with AI and ML in Botswana. Using a mixed-methods approach, we surveyed 59 teachers using an adapted version of the Motivation to Teach Computer Science (MTCS) scale …

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Motivating Passion And Purpose In Grade Six Students Through Game-Based Learning, Violet H. Harada, Patricia Louis Feb 2026

Motivating Passion And Purpose In Grade Six Students Through Game-Based Learning, Violet H. Harada, Patricia Louis

Learning Hub

This article examines how a sixth-grade pathways course at Kamehameha Schools in Hawai‘i leveraged Game-Based Learning (GBL) and Indigenous knowledge to foster student agency, creativity, and environmental stewardship. Designed and taught by school librarian Patricia Louis with support from a multidisciplinary instructional team, the Nā Lawai‘a Hawai‘i (Fishing, Hawaiian Style) elective engaged students in researching the ‘Ama‘ama (Hawaiian striped mullet) and the ecological challenges it faces, then translating their learning into the design of a board game, ‘Ama‘ama Escape. Grounded in constructivist, learning-by-making principles, the course guided students through an iterative design process that included inquiry, collaboration, prototyping, and playtesting. …

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A Hydrogen Hub Blueprint For The California Supply Chain, Tyler Reeb, Barbara Taylor Feb 2026

A Hydrogen Hub Blueprint For The California Supply Chain, Tyler Reeb, Barbara Taylor

Mineta Transportation Institute

As global climate targets tighten and state regulations accelerate, hydrogen technology presents a significant opportunity to help California supply chain and transportation stakeholders comply with state emissions mandates and global goals aimed to reduce the impacts of climate change. However, while fuel cell costs and infrastructure have received attention, workforce development remains overlooked. Hydrogen fuel cell electric vehicles (FCEVs) are especially promising for diesel-reliant sectors that are hard to decarbonize without negatively impacting operational efficiency. But scaling hydrogen vehicles and infrastructure in tandem with emerging consumer and industrial markets will not be easy. State and federal policies, often conflicting, further …

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A Constructive-Engaging Analysis Of Descarrtes (European) And Oyeshile (African) On Personhood, Olanrewaju Shitta-Bey Jan 2026

A Constructive-Engaging Analysis Of Descarrtes (European) And Oyeshile (African) On Personhood, Olanrewaju Shitta-Bey

Comparative Philosophy

An examination of the nature of the human person provides a crucial foundation for understanding human life and identity. Conceptions of personhood have far-reaching implications beyond philosophy, influencing politics, ethics, religion, and epistemology. Beliefs about what constitutes a person shapes theories of rights, responsibility, and justice; moral views on duty and accountability; religious understandings of the soul and the afterlife; and epistemological reflections on the sources and limits of knowledge. Inquiry into human nature is therefore not merely abstract, but central to understanding social organization and individual conduct. This paper examines two influential accounts of human nature drawn from distinct …

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A Cross-System Analysis Of Structural Commensurability Between Axiomatic Foundations Of Set Theory (Zfc) And The Principles Of Transcendent Wisdom, Mohammadjavad Maarefvand Jan 2026

A Cross-System Analysis Of Structural Commensurability Between Axiomatic Foundations Of Set Theory (Zfc) And The Principles Of Transcendent Wisdom, Mohammadjavad Maarefvand

Comparative Philosophy

Building on observed similarities in uniqueness proofs for the empty set and the Necessary Existent (Wājib al-Wujūd), this study engages the meta-methodological issue of cross-system commensurability. It identifies four instances of structural commensurability between Zermelo-Fraenkel set theory with Choice (ZFC) and Mulla Sadra's Transcendent Wisdom (Ḥikmat al-Muta'āliyah). First, identity principles: the Axiom of Extensionality provides a formal operational criterion analogous to the philosophical principle of identity through absence of distinguishing features. Second, multiplicity mechanisms: ZFC's constructor axioms (bottom-up construction) and emanation principles (top-down explanation) address analogous problems with inverse orientations. Third, transcending limitation: the Axiom of Infinity ensuring quantitative extensional …

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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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Scaling Pedestrian Crossing Analysis To 100 U.S. Cities Via Ai-Based Segmentation Of Satellite Imagery, Marcel E. Moran, Arunav Gupta, Jiali Qian, Debra Laefer Jan 2026

Scaling Pedestrian Crossing Analysis To 100 U.S. Cities Via Ai-Based Segmentation Of Satellite Imagery, Marcel E. Moran, Arunav Gupta, Jiali Qian, Debra Laefer

Faculty Research, Scholarly, and Creative Activity

Accurately measuring street dimensions is essential to evaluating how their design influences both travel behavior and safety. However, gathering street-level information at city-scale with precision is difficult given the quantity and complexity of urban intersections. To address this challenge in the context of pedestrian crossings — a crucial component of walkability — we introduce a scalable and accurate method for automatically measuring crossing distance at both marked and unmarked crosswalks, applied to America’s 100 largest cities. First, OpenStreetMap coordinates were used to retrieve satellite imagery of intersections throughout each city — totaling roughly three million images. Next, Meta’s Segment Anything …

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Modeling Bounded Count Environmental Data Using A Contaminated Beta-Binomial Regression Model, Arnoldus F. Otto, Antonio Punzo, Johannes T. Ferreira, Andriëtte Bekker, Salvatore D. Tomarchio, Cristina Tortora Jan 2026

Modeling Bounded Count Environmental Data Using A Contaminated Beta-Binomial Regression Model, Arnoldus F. Otto, Antonio Punzo, Johannes T. Ferreira, Andriëtte Bekker, Salvatore D. Tomarchio, Cristina Tortora

Faculty Research, Scholarly, and Creative Activity

Bounded count data are commonly encountered in environmental studies. This paper examines two environmental applications illustrating their relevance. The first investigates the effect of winter malnutrition on mule deer (Odocoileus hemionus) fawn mortality. The second application analyzes public perceptions of environmental issues using data from the Eurobarometer 95.1 survey (March–April 2021), which includes a question rating the perceived severity of climate change on a scale from 1 to 10. Together, these studies demonstrate the need for flexible bounded count models in environmental research. In this context, the binomial and beta-binomial (BB) models are widely used for bounded count data, with …

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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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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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Investigating Ui Based And Ai Based Interactions In Vr Environments For Human Anatomy Education, Siddharth Sondhi Jan 2026

Investigating Ui Based And Ai Based Interactions In Vr Environments For Human Anatomy Education, Siddharth Sondhi

Master's Projects

Traditional approaches to learning and simulation often struggle to convey the complexity of three dimensional structures. In anatomy education, methods such as textbooks, lectures, and cadaver based learning are used, but can make it difficult to understand the complex structure of organs within the human body. Virtual Reality (VR) offers a promising alternative by providing immersive and interactive 3D environments that allow users to explore and manipulate anatomical structures more intuitively. These environments can be further enhanced by integrating large language models (LLMs) as intelligent agents capable of guiding users through natural language interaction. This project presents a Unity-based VR …

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Emotional Patterns In Conversational Social Media Using Graph-Based Context, Sai Sanjay Yerunkar Jan 2026

Emotional Patterns In Conversational Social Media Using Graph-Based Context, Sai Sanjay Yerunkar

Master's Projects

Most social media datasets for emotion detection have not been constructed to account for conversational structure‚ so we investigate whether it carries signal for emotion prediction. Using Sentiment140 and GoEmotions Reddit threads‚ we construct their thread-based conversation graphs and compute the aggregated features of neighbors as well as the transformer and TF-IDF representations of comments. Connected comments are 4.5× more similar in emotions than expected by chance. Pairwise emotional similarity decays exponentially with geodesic distance (e.g.‚ after 2 hops). Pairs separated by a 30s timestamp difference have the highest emotional similarity. These results suggest that emotion is structured locally and …

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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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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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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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