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Spartan Daily, September 4, 2025, San Jose State University, School Of Journalism And Mass Communications

Spartan Daily, 2025

Volume 165, Issue 6

Climate Change-Related Threats To Railroads: Implications For Threat, Hazard And Risk Assessment, Frances L. Edwards, Daniel C. Goodrich

Faculty Research, Scholarly, and Creative Activity

Climate change has created new challenges for railroads around the world. The rail systems operate in open environments, subjecting them to increased heat, changing locations of heavy rains, developing sea level rise and storm surge, and a longer and stronger wildfire season. This research investigates how an updated threat and hazard identification and risk assessment (THIRA) can lead to the development of climate adaptation strategies that limit damage and disruption to the railroad enterprise. The THIRA analyzes which threats are of most concern in a specific rail segment, and which climate change adaptation strategies give a beneficial return on investment.

Acoustic Wave Manipulation Through Sparse Robotic Actuation, Tristan Shah, Noam Smilovich, Feruza Amirkulova, Samer Gerges, Stas Tiomkin

Faculty Research, Scholarly, and Creative Activity

Recent advancements in robotics, control, and machine learning have facilitated progress in the challenging area of object manipulation. These advancements include, among others, the use of deep neural networks to represent dynamics that are partially observed by robot sensors, as well as effective control using sparse control signals. In this work, we explore a more general problem: the manipulation of acoustic waves, which are partially observed by a robot capable of influencing the waves through spatially sparse actuators. This problem holds great potential for the design of new artificial materials, ultrasonic cutting tools, energy harvesting, and other applications. We develop …

Human Factors For A Better World: Case Studies In Community Ergonomics, Hanna J. Barton, Andrew Thactcher, Maurita T. Harris, Wendy A. Rogers, Dan Nathan-Roberts, Leah Newman, Carlo Caponecchia

Faculty Research, Scholarly, and Creative Activity

This panel brings together global experts to explore the evolving practice of community ergonomics—an approach that applies Human Factors and Ergonomics (HFE) to co-design systems with and for marginalized communities. Building on the lessons articulated in Barton et al. (2025), panelists will present diverse case studies that highlight both the promise and complexity of community-engaged HFE work. Panelists will reflect on the epistemological shifts, methodological adaptations, and relational commitments required to move toward genuine collaboration and social impact. Attendees will leave with tangible strategies for engaging communities as co-designers, as well as deeper insight into how our assumptions as HFE …

Exploring The Use And Misuse Of Large Language Models, Hezekiah Paul D. Valdez, Faranak Abri, Jade Webb, Thomas H. Austin

Faculty Research, Scholarly, and Creative Activity

Language modeling has evolved from simple rule-based systems into complex assistants capable of tackling a multitude of tasks. State-of-the-art large language models (LLMs) are capable of scoring highly on proficiency benchmarks, and as a result have been deployed across industries to increase productivity and convenience. However, the prolific nature of such tools has provided threat actors with the ability to leverage them for attack development. Our paper describes the current state of LLMs, their availability, and their role in benevolent and malicious applications. In addition, we propose how an LLM can be combined with text-to-speech (TTS) voice cloning to create …

Deep Learning-Based Drowsiness Detection System For Driver’S Safety, Sindhu Vidyanathan Dixith, Shrikant Jadhav, Youngsoo Kim, Naveenkumar Jayakumar

Faculty Research, Scholarly, and Creative Activity

Driver drowsiness is a leading contributor to road accidents, accounting for over 100,000 crashes and approximately ~1000 fatalities each year in the United States alone, as per National Safety Council (NSC). To mitigate this urgent public-safety risk, we propose a real-time Driver Drowsiness Detection System that achieves both high accuracy and fast inference on standard hardware. Our key idea is to combine two complementary deep-learning strategies: 1) a custom Convolutional Neural Network (CNN) paired with a Support Vector Machine (SVM) classifier, and 2) a lightweight transfer-learning model built upon a pre-trained convolutional backbone. We evaluate these approaches on two datasets: …

Using Large Language Models To Investigate The Origin Of Chronic Wasting Disease, G. Kent Webb

Faculty Research, Scholarly, and Creative Activity

This use case reports on the impressive output, hallucinations, instability, and limitations of three Large Language Models (LLMs): ChatGPT, Gemini, and Grok. The LLMs were prompted in an investigative sequence and responses checked. The collected information supports the common theory that chronic wasting disease (CWD) in North America originated in 1967 at a research facility in Fort Collins, Colorado, where deer were reported to have been exposed to sheep with a similar disease - scrapie. Findings include that: no sheep with scrapie were detected in the area around Fort Collins prior to 1967; domestic sheep reportedly exposed to scrapie were …

Directional Vibrotactile Takeover Requests On A Wrist-Worn Device: Effects Of Age, Pattern Type, And Urgency In Automated Driving, Wei Hsiang Lo, Gaojian Huang

Faculty Research, Scholarly, and Creative Activity

Drivers are still required to perform the takeover task in highly automated vehicles. This task, which is cognitively and physically demanding, may present challenges for older adults due to general age-related declines in perception and cognition. Tactile modalities that may not be occupied by many non-driving-related tasks could serve as a potential solution for delivering takeover requests. Among these, directional vibrotactile stimuli presented via a wrist-worn device represent a promising approach. However, the effects of the two common types of directional vibrotactile patterns, dynamic patterns that vibrate sequentially at different locations and static patterns that vibrate at fixed locations, are …

Spartan Daily, August 28, 2025, San Jose State University, School Of Journalism And Mass Communications

Spartan Daily, 2025

Volume 165, Issue 4

Accelerating The Fusion Workforce In The Usa, Carlos Paz-Soldan, Eva Belonohy, Troy Carter, Laleh E. Coté, Evdokiya Kostadinova, Calvin Lowe, Subash L. Sharma, Sybil De Clark, Jaydeep Deshpande, Kate Kelly, Bobbi Makani, David A. Schaffner, Kathreen E. Thome

Faculty Research, Scholarly, and Creative Activity

The fusion energy research and development landscape has seen significant advances in recent years, with important scientific and technological breakthroughs and a rapid rise of investment in the private sector. The workforce needs of the nascent fusion industry are growing at a rate that academic workforce development programs are not currently able to match. This paper presents the findings of the Workforce Accelerator for Fusion Energy Development Conference held in Hampton, Virginia, United States of America (USA), on 29-30 May 2024, which was funded by the National Science Foundation of the USA. A major goal of the conference was to …

Poster: Development Of Situation Awareness Measurement For Cybersecurity Professionals, David Schuster, Crystal Fausett, Maiyi Huang, Sabina M. Patel, Jenna Korentsides, Joseph R. Keebler, Elizabeth H. Lazzara

Faculty Research, Scholarly, and Creative Activity

Better understanding of the implications of many aspects of human behavior, especially that of cybersecurity professionals, can help develop the cybersecurity workforce. Despite efforts aimed at documenting and understanding cybersecurity professionals' knowledge, understanding of how cognition supports human performance in specific cybersecurity tasks remains limited. Specifically, understanding of the elements of situation awareness (SA), defined as goal-directed knowledge, is necessary to support human-centered evaluation, selection, training, and recruitment strategies. In this poster, we propose a framework for developing measures of situation awareness for cybersecurity professionals and a method of capturing initial effectiveness data that does not rely on access to …

Poster: Cloudsweeper: Leveraging Large Language Models To Personalize Sensitive Archive Search, Victor Escuerdo, Sergio Talavera, Gautam Santhanu Thampy, Ivan Torres, Daniel Vega Lojo, Chris Kanich, Magdalini Eirinaki

Faculty Research, Scholarly, and Creative Activity

As cyber threats continue to evolve, overlooked or neglected files stored in cloud services can pose significant risks to personal privacy and data security. In this paper we present Cloudsweeper, a system that aims to improve cloud storage security by creating tools that help users identify and manage sensitive or unwanted files. Cloudsweeper leverages Large Language Models (LLMs) with a Retrieval-Augmented Generation (RAG) architecture to develop a personalized and privacy-focused archive search system. Cloudsweeper represents an innovative approach to secure archive management, balancing user control and privacy in cloud storage environments.

Poster: Threat Intelligence & Modeling Practices For Iomt Devices Using Wi-Fi Communications, Eva Wilson, Cody Ourique, Bernardo Flores

Faculty Research, Scholarly, and Creative Activity

The integration of internet connectivity into medical devices, known as the Internet of Medical Things (IoMT), is revolutionizing modern healthcare through remote monitoring and advanced diagnostics. Among these devices, the Continuous Positive Airway Pressure (CPAP) machine plays a critical role in managing patients with Sleep Apnea. However, the security of CPAP devices presents significant concerns due to potential vulnerabilities that could lead to critical failures, such as device shutdowns. An analysis was made of the device's connectivity interface, to identify potential exploit points and vulnerabilities. Additionally, we evaluated whether existing threat modeling frameworks - namely the MITRE Adversarial Tactics, Techniques, …

Context-Aware Natural Language Processing For Malware Detection, Helen Liu, Summer Mccune, Quang Duy Tran, Fabio Di Troia, Younghee Park

Faculty Research, Scholarly, and Creative Activity

As malware continues to evolve and cyber attacks become increasingly prevalent, it is critical to develop effective malware classification techniques for the detection and prevention of such malicious attacks. In our research, we approach malware classification from a natural language processing perspective to explore how various tokenization techniques on malware opcode features can enhance classification accuracy and combat malware obfuscation and evolution techniques with a deep learning approach, previously unaddressed by existing research. We bridge this gap by conducting extensive hyperparameter tuning experiments that examine the effects of five common tokenization methods, that is, White Space Separation, Top Single Words, …

Synthetic Malware Image Generation Based On Generative Models Against Zero-Day Attacks, Arjun Sudheer, Ayesha Ahmed, Fabio Di Troia, Younghee Park

Faculty Research, Scholarly, and Creative Activity

Malware detection is a critical task for protecting our assets from attacks. However, traditional approaches to malware detection often struggle with limited datasets, which hinder the effectiveness of machine learning models. In this paper, we propose a malware image generation system designed to craft high-quality synthetic malware image samples, addressing the challenges posed by small datasets in malware detection. The proposed system utilizes two popular generative models, WGAN-GP and Diffusion, to generate synthetic malware images. It converts malware binary files into image files using four different color spaces: monochrome, grayscale, RGB, and CMYK. These images are then evaluated based on …

Increasing And Improving Diversity And Hiring Of Individuals With Disabilities In The Workplace, Joshua Zhang

McNair Research Journal SJSU

This paper is a literature review of several literature on disability, diversity,

human resource/human resource management, and hiring in the

workplace/organization. The research question is how can employers

improve and increase hiring of individuals with disabilities in their

organizations? The objectives of this literature review paper are 1. Research

and analyze existing literature on disability, diversity, and workplace hiring,

2. Identify any limitations in gaps in research literature, and 3. Propose and

suggest areas and topics for future research. Each of the academic sources

in this paper are presented and discussed along with analysis. Additionally,

recommendations on how to best …

Exploring The Moderating Effect Of Boundary Management Strategy In A Remote Work Environment, Brandon M. Vasquez

Master's Theses

Remote work has evolved significantly, especially during the COVID-19 pandemic, and continues to influence employee experiences in both positive and negative ways. Managing the separation between work and personal life is of importance. More specifically, using a boundary management strategy of segmentation may be more effective than an integration strategy because those who separate the two domains of work and family/personal life may be less prone to conflict in their lives. The present study aimed to examine the moderating effect of boundary management strategy on the relationship between remote work and job satisfaction, work-life balance, and well-being. Specifically, it was …

Dynamics And Lipid Membrane Coupling Of The Ras-Raf Complex Revealed Via Multiscale Simulations, Timothy S. Carpenter, Fikret Aydin, Chris Neale, Que N. Van, Xiaohua Zhang, Harsh Bhatia, Jason W. Sidabras, Peter H. Frank, Konstantia Georgouli, Jeremy O.B. Tempkin, Violeta Burns Casamayor, Gulcin Gulten, Rebika Shrestha, Debanjan Goswami, Francesco Di Natale, Joseph R. Chavez, Liam G. Stanton, For Full Author List, See Comments Below

Faculty Research, Scholarly, and Creative Activity

To gain molecular and mechanistic insights into initiation of the RAS-RAF signaling cascade, we developed and used a combination of multiscale simulation and experimental approaches. The influence and impact of the membrane on RAS and RAF proteins is a factor we are just beginning to understand and appreciate in more detail. Molecular simulation is an ideal methodology to further study this complicated relationship between the membrane and associated proteins. Our previous work using Multiscale Machine-learned Modeling Infrastructure investigated different lipid compositions solely around the KRAS4b protein and the interplay between protein behavior and these membrane environments. Multiscale Machine-learned Modeling Infrastructure …

Riparian Restoration Increases Soil Microbial Biomass In Marin County Rangelands, But Soil Carbon Stock Is Less Responsive, Sherilyn Kathryn Reinhart

Master's Theses

Land use change has caused the degradation of 70% of the Earth’s soils, directly altering soil microbial communities and carbon cycling. Interest in carbon farming practices is growing, but studies that document the nuances of soil carbon flux in specific bioregions are still too few. In this study, I examined how one practice, riparian restoration, alters soil carbon stock and microbial community structure over time. Using a paired sampling time series design, I collected soil samples from twelve riparian forests and adjacent rangelands near Marin County, California. Young and mature sites ranged from five to 29 years since restoration, and …

Deep Learning For The Early Detection Of Invasive Ductal Carcinoma In Histopathological Images: Convolutional Neural Network Approach With Transfer Learning, Naga Shreya Chilumukuru, Pragya Priyadarshini, Yawo Ezunkpe

Faculty Research, Scholarly, and Creative Activity

Background: Invasive ductal carcinoma (IDC) is considered the most common form of breast cancer, accounting for a significant percentage of mortality worldwide. Therefore, its early detection is vital to further improve patients’ outcomes and survival rates. However, conventional diagnostic methods in the form of manual histopathological examinations are time-consuming, subjective, and prone to errors. Therefore, there is an urgent need to develop automated solutions for accurate IDC detection in histopathology images to assist pathologists in clinical decision-making. Objective: We aim to develop and validate a convolutional neural network (CNN) model for early detection of IDC by analyzing histopathological images. The …

Spartan Daily, August 20, 2025, San Jose State University, School Of Journalism And Mass Communications

Spartan Daily, 2025

Volume 165, Issue 1

The Sketches Of Infinite Data And Algorithms For Real-Time Data Insights, Vishnu S. Pendyala

Open Educational Resources

How are machine learning algorithms able to answer questions from any nook and corner of the World Wide Web? How are trending hashtags from the near infinite microblog posts, unique visitors and other distinct counts in the near infinite website traffic determined? How do blogging websites avoid recommending articles a user has previously read? In general, how can we answer complex queries about enormous data streams without storing them entirely, in real-time? The answer often lies in clever approximation algorithms and data "sketches" that capture essential properties using vastly reduced space. The relentless flow of data in modern systems indeed …

Learning Structure With Multivariate Information Bottleneck And Exploration Of New Methods In Sequential Decision Making, Volodymyr Makarenko

Master's Theses

Research in useful information extraction has been motivated by the increasing demand to extract insights from unstructured data, and by the need to store and transmit great volumes of information, often originating in unstructured data such as videos. Research in rate distortion and information bottleneck paved the path for understanding and guiding the design of lossy encoders, capable of extracting relevant information. Independently, research in deep representation learning has enabled numerous applications for unstructured high-dimensional data such as images. However, the interpretability of the deep learning methods remained limited. Several desired properties of learned representations have been suggested, including disentanglement. …

The Effectiveness Of Kolmogorov–Arnold Networks In The Healthcare Domain, Vishnu S. Pendyala, Nivedita Venkatachalam

Faculty Research, Scholarly, and Creative Activity

Kolmogorov–Arnold Networks (KANs) have recently emerged as a powerful alternative to traditional Artificial Neural Networks (ANNs), offering superior accuracy and interpretability, which are two critical requirements in healthcare applications. This study investigates the effectiveness of KANs across a range of clinical tasks by applying them to diverse medical datasets, including structured clinical data and time-series physiological signals. Compared with conventional ANNs, KANs demonstrate significantly improved performance, achieving higher predictive accuracy even with smaller network architectures. Beyond performance gains, KANs offer a unique advantage: the ability to extract symbolic expressions from learned functions, enabling transparent, human-interpretable models—a key factor in clinical …

Solving A Nonhomogeneous Differential Equation And Error Analysis, Tayler Braga, Maryam Khazaei Pool

Faculty Research, Scholarly, and Creative Activity

Linear nonhomogeneous differential equations appear in many physical, biological, and engineering systems where input or forcing terms act on otherwisse linerar dynamics. Solving such equations helps model real-world phenomena, ranging from population growth under external influencse to the otion of damped systems with external forcing. The ability to compare exact olutions with numerical and matrix-based approximations is crucial for understanding th accuracy and limitations of each approach.

Moral And Ethical Nature In Confucian Liang-Zhi And Islamic Fitra: Comparative Perspectives On Innate Morality, Yanrong Shi

Comparative Philosophy

This article offers a comparative philosophical analysis of Confucian liang-zhi (innate moral knowledge) and Islamic fitra(primordial nature), focusing on their accounts of innate morality and moral epistemology. Drawing on classical Confucian sources—especially the praxis-oriented interpretation of Wang Yangming—and foundational Islamic texts and commentary, the study demonstrates that both traditions posit an inborn moral faculty grounding ethical universality and responsibility. Wang Yangming’s liang-zhi is conceived as an innate moral principle that, under the imperative of zhi (to actualize or extend), is continually activated through moral practice. In parallel, fitra is understood as a God-given orientation to truth, cultivated and safeguarded through …

How Theravāda Buddhism Enriches The Language Of Thought Hypothesis: Beyond Syntax, Juanjuan Cheng, Sumanasara Sandamadulle

Comparative Philosophy

The Language of Thought Hypothesis (LOTH) posits that mental states operate through a language-like representational system physically implemented in the brain. However, this purely symbolic approach faces a significant limitation: certain mental phenomena—such as intuitions, emotions, and intentions—lack the syntactically compositional structure required by LOTH and therefore cannot be adequately explained within its symbolic framework. To address this theoretical challenge, we turn to Theravāda Buddhism (TB), an early Buddhist tradition that integrates rigorous philosophical inquiry with meditative practice. Remarkably, TB began to prefigure a solution to LOTH’s limitations over 2,300 years ago. Although LOTH and TB differ …

Understanding And Evaluating Genomic Language Models, Aadit Kapoor

Master's Theses

Large Language Models (LLMs) have shown remarkable capabilities in interpreting complex patterns across various domains, yet their application to genomic data remains limited. We see great potential in leveraging LLMs for vital biological tasks, such as predicting transcription factor binding sites and identifying antibiotic-resistant genes. This emergent behavior positions LLMs as powerful tools for enhancing our understanding of intricate biological language. LLMs trained specifically on genomic data, such as DNA sequences, operate distinctly compared to those trained on natural language. This difference is evident not only in the architectural landscape of the models but also in the methodologies employed by …

It Shucks To Be Inbred: Effects Of Small Population Sizes On Genomic Diversity Of Hatchery Reared Olympia Oysters (Ostrea Lurida), Anna S. Heasley

Master's Theses

Olympia oysters (Ostrea lurida), critical members of estuarine ecosystems along the California Current Ecosystem (CCE), have faced significant population declines due to historical overfishing and habitat deterioration. Today, managers from multiple estuaries across the CCE are focusing on conservation aquaculture breeding efforts to aid in restoration; however, the genetic implications of these efforts remain unexplored. As with other Ostrea spp, Olympia oysters have a viviparous reproductive strategy that creates unique challenges for aquaculture-mediated restoration. This study evaluates the genetic impact of current captive breeding practices on Olympia oyster cohorts produced in three California estuaries: Elkhorn Slough (EHS), Morro Bay (MB), …

Vertiport Location Selection And Optimization For Urban Air Mobility In Complex Urban Scenes, Yannan Lu, Weili Zeng, Wenbin Wei, Weiwei Wu, Hao Jiang

Faculty Research, Scholarly, and Creative Activity

Vertiports, as dedicated facilities for electric vertical takeoff and landing (eVTOL) aircraft, are essential to ensure the efficiency and sustainability of Urban Air Mobility (UAM). However, UAM infrastructure site selection has become increasingly complex due to limited land availability, complex spatial conditions, and the need to balance multiple objectives. Focusing on passenger-carrying UAM operations, this study proposes a systematic framework for vertiport site selection. First, key factors are classified into high, medium, and low levels across the safety, economic, and social dimensions, forming a modular evaluation system. A GIS-based spatial screening process is developed to identify potential vertiport locations. Subsequently, …