Digital Commons Network^™

Hemispheric Asymmetry Of Phase Partition In Mixed-Phase Clouds Based On Near Global-Scale Airborne Observations, Ching An Yang, Minghui Diao, Yang Shi, Xiaohong Liu

Faculty Research, Scholarly, and Creative Activity

Mixed-phase clouds contribute to substantial uncertainties in global climate models due to their complex microphysical properties. Former model evaluations almost exclusively rely on satellite observations to assess cloud phase distributions globally. This study investigated mixed-phase cloud properties using near global-scale in situ observation data sets from 14 flight campaigns in combination with collocated output from a global climate model. The Southern Hemisphere (SH) shows significantly higher occurrence frequencies and higher mass fractions of supercooled liquid water than Northern Hemisphere (NH) based on observations at 0.2 and 100 km horizontal scales. Such hemispheric asymmetry is not captured by the model. The …

Spartan Daily, September 11, 2025, San Jose State University, School Of Journalism And Mass Communications

Spartan Daily, 2025

Volume 165, Issue 9

Aisstream-Mcp: A Real-Time Memory-Augmented Question-Answering System For Maritime Operations, Sien Chen, Ruoxian Zhao, Jian Bo Yang, Yinghua Huang

Faculty Research, Scholarly, and Creative Activity

Ports and maritime operations generate massive real-time data streams, particularly from Automatic Identification System (AIS) signals, which are challenging to query effectively using natural language. This study proposes a prototype AISStream-MCP, a memory-augmented real-time maritime question-answering (QA) system that integrates live AIS data streaming with a Model Context Protocol (MCP) toolchain to support port operations decision-making. The system combines a large language model (LLM) with four MCP-enabled modules: persistent dialogue memory, live AIS data query, knowledge graph lookup, and result evaluation. We hypothesize that augmenting an LLM with domain-specific tools significantly improves QA performance compared to systems without memory or …

Spartan Daily, September 10, 2025, San Jose State University, School Of Journalism And Mass Communications

Spartan Daily, 2025

Volume 165, Issue 8

Spartan Daily, September 9, 2025, San Jose State University, School Of Journalism And Mass Communications

Spartan Daily, 2025

Volume 165, Issue 7

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.

Spartan Daily, September 3, 2025, San Jose State University, School Of Journalism And Mass Communications

Spartan Daily, 2025

Volume 165, Issue 5

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 …

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 …

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 …

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

Spartan Daily, 2025

Volume 165, Issue 4

Spartan Daily, August 27, 2025, San Jose State University, School Of Journalism And Mass Communications

Spartan Daily, 2025

Volume 165, Issue 3

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 …

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, …

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, …

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 …

Understanding Child Noncompliance In The Early Care Setting, Melody Mann

McNair Research Journal SJSU

This study examines how teachers respond to children’s noncompliance in

early care settings. This structured observational study will focus on the

moment-to-moment interactions occurring within the preschool classroom

between both the child and teacher. It is predicted that (1) teacher’s direct

bids to children will promote greater instances of child compliance than

indirect bids, and (2) teachers’ responsive language will promote greater

instances of child compliance than restrictive language. The results of this

study will contribute to existing knowledge about the nature of teacher

child interactions in the early care settings.

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 …

Conscious Control: A Case Study On Self-Monitoring And Interventions In Maladaptive Daydreaming, Caleb Chan

McNair Research Journal SJSU

Maladaptive daydreaming (MD) is a persistent mental activity

characterized by immersive, excessive daydreaming that disrupts daily

life. This study examines the effectiveness of self-regulation strategies

in managing MD, including cognitive techniques, behavioral

adjustments, technology-assisted cues, and environmental

modifications. Using a single-subject ABA design, each intervention

phase lasted one week, with MD episodes tracked through self-reported

diaries and EEG monitoring. The results demonstrated that cognitive

reframing and task switching produced the most sustained reductions in

MD episodes, while technology-assisted cues had diminishing

effectiveness over time due to adaptation. Environmental modifications,

particularly workspace reorganization, con- tributed to long-term

improvements, while lighting adjustments …

Full Issue

McNair Research Journal SJSU

No abstract provided.

Spartan Daily, August 26, 2025, San Jose State University, School Of Journalism And Mass Communications

Spartan Daily, 2025

Volume 165, Issue 2

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 …

A Closer Look At The Relationship Between Cultural Intelligence And Organizational Citizenship Behaviors, Lesli B. Quiroz

Master's Theses

As organizations—particularly in the United States—become more multicultural due to globalization, they increasingly rely on individuals' ability to succeed in complex, cross?cultural environments. Cultural intelligence (CQ), or the ability to successfully adapt to unfamiliar cultural settings, has been previously related to expatriate cross-cultural adjustment, group effectiveness, and more recently, to organizational citizenship behaviors (OCB), which are voluntary extra-role workplace behaviors that lead to positive employee and organizational outcomes. This study mainly sought to theoretically expand the limited research on the relationship between CQ and OCB with some research questions. In a sample of 93 participants based in California, results found …

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

Spartan Daily, 2025

Volume 165, Issue 1

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. …