Digital Commons Network^™

Advances In Artificial Intelligence For Glioblastoma Radiotherapy Planning And Treatment, Reid Master, Nesha Rubin, James Sampson, Kamlesh K Yadav, Shruti Pandita, Aria Sabbagh, Anika Krishnan, Patrick J Silva, Kenneth S Ramos, Vincent Gregoire, Nikos Paragios, Sunil Krishnan, Tej K Pandita

The Brown Foundation: Institute of Molecular Medicine

Glioblastoma is an aggressive central nervous system tumor characterized by diffuse infiltration. Despite substantial advances in oncology, survival outcomes have shown little improvement over the past three decades. Radiotherapy remains a cornerstone of treatment; however, it faces several challenges, including considerable inter-observer variability in clinical target volume delineation, dose constraints associated with adjacent organs at risk, and the persistently poor prognosis of affected patients. Recent advances in artificial intelligence, particularly deep learning, have shown promise in automating radiation therapy mapping to improve consistency, accuracy, and efficiency. This narrative review explores current auto segmentation frameworks, dose mapping, and biologically informed radiotherapy …

Preservice Teachers’ Preconception Of Student Voice In Their Teacher Education, Joan Belfon

Walden Dissertations and Doctoral Studies

A 2022 Jamaican education report highlighted that student-centered pedagogy is crucial to educational outcomes. A problem exists in that there is limited research on how student voice practices within student-centered pedagogy are incorporated into teacher training. Before preservice teachers’ praxis can be transformed, greater understanding of their preconceptions about student voice is essential. Guided by conceptual change theory, the purpose of this generic qualitative study was to explore the preconceptions of preservice teachers in rural central Jamaica about student voice in their teacher education. Eight preservice teachers preparing for the secondary level in three Jamaican higher education institutions were purposefully …

Exploring The Experiences Of Bahamian Men Diagnosed With Hypertension And Medication Adherence, Bianca Edwards

Walden Dissertations and Doctoral Studies

Hypertension (HTN) is associated with significant health complications, including stroke, myocardial infarction, heart failure, and kidney disease. Despite advances in the treatment and management of HTN, a lack of medication adherence contributes to the persistence of HTN-related morbidity and mortality affecting men in The Bahamas. The purpose of this qualitative descriptive phenomenological study, guided by Pender’s health promotion model and Bandura’s social cognitive theory, was to explore the lived experiences of Bahamian men diagnosed with hypertension regarding their adherence to prescribed medications. Using Giorgi’s descriptive phenomenological method, in-depth, semistructured interviews were conducted with 11 participants. Data were analyzed through repeated …

Organizational Leaders’ Effective Strategies For Using Employee Training To Mitigate Threats To Cybersecurity Compliance, Oluwaseye Adekunle Fadare

Walden Dissertations and Doctoral Studies

Organizational leaders increasingly face challenges associated with employee noncompliance with cybersecurity controls, which expose business leaders to reputational and financial risks. Addressing this business problem is critical for cybersecurity leaders and business executives who aim to strengthen digital resilience and sustain profitability. The purpose of this qualitative pragmatic inquiry was to explore effective strategies organizational leaders use to enhance employee cybersecurity compliance through training. The project was grounded in the protection motivation theory and total quality management frameworks, which provide insight into the behavioral and continuous improvement dimensions of cybersecurity management. Six cybersecurity leaders from Nigeria participated in semistructured interviews. …

Spartan Daily, November 25, 2025, San Jose State University, School Of Journalism And Mass Communications

Spartan Daily, 2025

Volume 165, Issue 39

Digital Beginnings: Exploring The Impact Of Screen Exposure On Internalizing Behaviors During Toddlerhood, Nicole Sotolongo

Electronic Theses and Dissertations 2020 - Present

The present study examines the relationship between screen exposure behaviors and toddler internalizing behaviors, with sleep behaviors being observed as a moderator. The study emphasizes the need for continued research to adapt to the ever-changing nature of the daily use of screens during toddlerhood and early childhood. Fifty-seven mothers of two-year-old children participated in one-on-one parent-report only, interviewer-based quantitative interviews discussing their child’s internalizing and sleep behaviors. Participants were also asked to fill out a questionnaire about their child’s screen exposure behaviors. It was hypothesized that (1) higher levels of screen exposure behaviors will result in increased internalizing behaviors, (2) …

Botulinum Toxin Combined With Robot-Assisted Therapy For Post-Stroke Spasticity: A Systematic Review, Salvatore Facciorusso, Stefania Spina, Mirko Filippetti, Rajiv Reebye, Gerard E Francisco, Andrea Santamato

Faculty, Staff and Student Publications

Background: Post-stroke spasticity limits motor recovery and independence. Combining botulinum toxin type-A (BoNT-A) injection with intensive, task-specific robot-assisted therapy (RAT) might enhance neuroplasticity and functional gains, but its additive effect and optimal timing are uncertain. (

Methods: We systematically searched major medical databases and trial registries up to April 2025 for randomized controlled trials in adults with post-stroke spasticity comparing botulinum toxin type-A injection plus RAT with toxin injection plus conventional therapy, or RAT alone with RAT combined with toxin injection. Risk of bias was assessed using the RoB 2 tool, and findings were synthesized narratively.

Results: Seven trials ( …

Exploring Future Educators’ Roles In Classroom Aac Use: Insight Into Knowledge, Skills, And Attitudes, Katrina M. King

Honors College Theses

The United States public school system requires the provision of a free and appropriate public education (FAPE) in the least restrictive environment (LRE) for qualifying students with disabilities (U.S. Department of Education, n.d.). Speech, language, and hearing related disabilities are among the qualifying categories (U.S. Department of Education, n.d.). Students with these disabilities may have complex communication needs (CCN), or significant limitations in their speech, language, and communication abilities (Communication Rights Australia, 2018). Augmentative and alternative communication (AAC), a type of assistive technology, offers support for these students, however, several barriers inhibit full communicative participation (American Speech-Language-Hearing Association [ASHA], n.d.-a). …

Re: Conditional Approval Letter For The Final Butte Mine Waste Repository Geotechnical Investigation Work Plan (Dated November 14, 2025), Emma Rott

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Simulating Realistic Lyman-𝛼 Emitters Including The Effect Of Radiative Transfer, Hasti Khoraminezhad, Shun Saito, Max Gronke, Chris Byrohl

Research Data

We present an empirical yet physically motivated simulation of realistic Lyman-𝛼 emitters (LAEs) at 𝑧 ∼ 2 − 3, crucial for ongoing and forthcoming cosmological LAE surveys. We combine an empirical UniverseMachine galaxy-halo model with a simple spherical expanding shell model for the Lyman-𝛼 radiative transfer, calibrating only three free parameters to simultaneously reproduce the observed Lyman-𝛼 luminosity function and the angular clustering. Our LAE model is further supported by its consistency with other observables such as the Lyman-𝛼 equivalent width distribution, the Lyman-𝛼 escape fraction as a function of stellar mass and dust reddening, and the systemic velocity offsets. …

Intro To Ai Literacies Discussion, Miranda Rectenwald

Generative AI Teaching Activities

Students consider and dicuss key literacies when using generative AI tools.

Experiences Of First-Generation College Students Engaging In Online Learning Amid A Global Pandemic, Anthony Capers Jr

Walden Dissertations and Doctoral Studies

The global pandemic, COVID-19, had an impact on the academic experiences of all college students. First-generation college students who often experience special challenges in adjusting to academia may have faced additional challenges given the transition to online learning. However, there has been little research focused on the impact of COVID on their adjustment to college. The purpose of this study was to explore the experiences of an unanticipated learning transition among first-generation college students pursuing higher education at a 4-year school, during a global pandemic. The theoretical framework for this study was based on an interactionalist theory focused on the …

Divorcees’ Experiences Of Hubristic Pride In Communication In Previous Marital Relationships, Amanda Harms

Walden Dissertations and Doctoral Studies

Poor communication practices have significantly strained marital relationships, contributing to increased divorce rates in the United States. As a self-conscious emotion, hubristic pride often leads to poor communication, which in turn exacerbates marital discord and contributes to the breakdown of families. The purpose of this basic qualitative study was to explore how divorcees perceive the impact of hubristic pride on communication in their previous marriages. The theoretical framework underpinning this study was Homans’s social exchange theory. Data were obtained from eight divorced participants not actively involved in ongoing custody proceedings through semistructured interviews and analyzed using Saldaña’s descriptive coding methodology. …

A Critical Analysis Of Women’S Experiences Of Sport Participation In Saudi Arabia, Nouf Bamuhair, Richard Giulianotti, Carolynne Mason, Amer Alajmi

Journal of Global South Sport Studies

This study investigates Saudi women's experiences in sports participation within the context of the country's evolving social reforms. It addresses a major research gap on participation and policy issues, alongside a dearth of in-depth empirical studies on women and sport in Saudi Arabia. Traditionally, cultural norms, policies, and conservative interpretations of Islam have restricted women's involvement in sports in Saudi Arabia. However, recent reforms under Vision 2030, aim to enhance women's participation in sport. Using an intersectionality framework, the study examines how age, socioeconomic status, and geographical location shape women's sports experiences. Through semi-structured interviews with women in Riyadh and …

Fraud Detection And Explanation In Medical Claims Using Gnn Architectures, Reem Muhammad, Dina Tbaishat, Amril Nazir, Seif Yacoub, Mustafa Abdulrazek, Mohamed Ahmed Abo El-Enen, Ahmed T. Sahlol

All Works

This paper addresses the critical challenge of fraud detection in medical insurance claims-a pervasive issue causing significant financial losses in healthcare-using Graph Neural Networks (GNNs). Given the intricate nature of healthcare data, traditional fraud detection methods do not inherently capture the complex relationships and patterns among different entities. We explore the potential of GNNs to effectively identify fraudulent claims by modeling the interactions among various entities-such as patients, healthcare providers, diagnoses, and services-as a heterogeneous graph. We employ two state-of-the-art heterogeneous GNN architectures, HINormer (Heterogeneous Information Network Transformer) and HybridGNN, along with a modified homogeneous GNN, RE-GraphSAGE (GraphSAGE Graph Sample …

The Kaczmarz Algorithm In Hilbert C*-Modules, Daniel Alpay, Chad Berner, Eric S. Weber

Mathematics, Physics, and Computer Science Faculty Articles and Research

The Kaczmarz algorithm in Hilbert spaces is a classical iterative method for stably recovering vectors from inner product data. In this paper, we extend the algorithm to the setting of Hilbert C*-modules and establish analogues of its effectiveness in both finite-dimensional and stationary cases. Consequently, we demonstrate that continuous families of elements in a Hilbert space can be uniformly recovered using the Kaczmarz algorithm. Additionally, we develop a normalized Cauchy transform for continuous families of measures and use it to provide sufficient conditions under which standard frames in Hilbert C(X)-modules can be generated by the Kaczmarz …

Comparison Of Gluteus Medius Muscle Activation In Women With And Without Patellofemoral Pain, Michelle N. Samuel, Szu-Ping Lee, Andrew Hooyman, Kevin C. Miller, Daniel L. Young, Neal R. Glaviano

Physical Therapy Faculty Articles and Research

Context: Women with patellofemoral pain (PFP) commonly have hip muscle weakness in comparison with women without PFP. One underlying mechanism for this muscle weakness is inhibition. Although the presence of muscle weakness is well documented in women with PFP, few authors have investigated gluteus medius inhibition in this population. Women are twice as likely to suffer from PFP when compared with men; therefore, we focused on the female population.

Objective: To compare voluntary activation of the gluteus medius between women with and without PFP.

Design: Case-control study.

Setting: Laboratory.

Patients or Other Participants: Twenty-eight female …

Turning Patients' Open-Ended Narratives Of Chronic Pain Into Quantitative Measures: Natural Language Processing Study, Raquel Norel, Jennifer Gewandter, Zhengwu Zhang, Anika Tahsin, Chadi G Abdallah, John Markman, Zhiyao Duan, Guillermo Cecchi, Paul Geha

Faculty, Staff and Students Publications

Background: Subjective report of pain remains the gold standard for assessing symptoms in patients with chronic pain and their response to analgesics. This subjectivity underscores the importance of understanding patients' personal narratives, as they offer an accurate representation of the illness experience.

Objective: In this pilot study involving 20 patients with chronic low back pain (CLBP), we applied emerging tools from natural language processing (NLP) to derive quantitative measures that captured patients' pain narratives.

Methods: Patients' narratives were collected during recorded semistructured interviews in which they spoke about their lives in general and their experiences with CLBP. Given that NLP …

Identification Of Novel Tat-I24-Related Peptides With Antiviral Activities, Hanna Harant, Siegfried Hofinger, Reingard Grabherr, Zsolt Ruzsics, Hartmut Hengel

Michigan Tech Publications

To identify novel peptides with potential antiviral activities, a database search was performed based on the primary sequence of the peptide I24 (CLAFYACFC), the effective part of the antiviral peptide TAT-I24 consisting of peptide I24 and the cell penetrating TAT-peptide (amino-acids 48–60, GRKKRRQRRRPPQ). A Protein BLAST search identified several sequences with high similarity to I24 in diverse proteins, some of which are known to be involved in the interaction with nucleic acids. Selected sequences and newly designed variants of I24 were synthesized as TAT fusion peptides and tested for antiviral activity in two well-established models: baculovirus transduction of HEK293 cells …

Coordinated Transfer Of Dna Between Pol Θ And Pol Δ Resets Microhomology Choice During Double-Strand Break Repair, Yuzhen Li, Mark Returan, Adele T Guerin, April M Averill, Dorcas Oladapo, Sylvie Doublié, Richard D Wood

Faculty, Staff and Student Publications

DNA polymerase theta (Pol θ)-mediated end joining (TMEJ) initiates DNA double-strand break repair by using short homologies (microhomologies) between single-stranded DNA tails. This repair process is particularly important in cancer cells defective in homologous recombination. The exonuclease function of DNA polymerase delta (Pol δ) has been identified as an essential component for TMEJ, functioning to remove unpaired bases flanking a microhomology (MH). It is not known if the exonuclease removes all unpaired bases at once and how this removal might affect subsequent MH selection. Here, we reconstituted a functional TMEJ repair process using purified human Pol θ and Pol δ. …

Changing Perspectives In California: A Transformative Culture Exchange For Stockholm University And Sonoma State University Criminology Students, Bryan Burton, Sofie Hellmer, Vida Wåhlmark, Hope Ortiz, Diana Grant

csuglobalaction

From June 13 to 20, 2024, Sonoma State University (SSU) hosted 16 criminology students from Stockholm University (SU) for an educational and cultural exchange. Five SSU undergraduate “student ambassadors” participated, representing California and SSU while assisting the Swedish students. Surveys indicated the exchange was a meaningful academic and cultural experience for all participants. The Swedish students deepened their understanding of the U.S. criminal justice system through presentations by criminologists and discussions with practitioners on issues such as policing and prison reform. They also gained broader perspectives on California and the United States through interactions with SSU ambassadors and visits to …

Gc-0258 Safecircle:​ Ai And Micro-Radar-Based Remote Monitoring For Patients With Ad/Adrd​, Awan-Ur- Rahman, Soarov Borty, Shakib Quddus

C-Day Computing Showcase

Alzheimer's disease and related dementias (AD/ADRD) is an irreversible and degenerative neurological condition that severely impacts neurons, resulting in cognitive decline and memory loss. This study explores a mHealth system, including a SafeCircle iOS prototype, a novel solution that combines artificial intelligence with cutting-edge micro-radar technology. The platform offers a variety of features, including management of patient and caregiver profiles, real-time alerts in case of emergencies, emergency contact lists, one-touch SOS support, sharing of live locations, and recording of unusual events in video. It is a responsive and reliable care assistant that optimizes patient safety while reducing caregiver burden.

Grm-0267 Learned Heuristics For Efficient A* Search: Improving Pathfinding In Combinatorial Pathfinding Problems, Jamia Jackson

C-Day Computing Showcase

This work proposes a learned heuristic framework designed to improve planning efficiency in deterministic pathfinding tasks. Building on the classic 8-puzzle as an initial test domain, supervised models were trained to approximate heuristic values and guide node ordering during A* search. The proposed approach focuses on modifying and enhancing traditional heuristics such as Manhattan distance by incorporating learned corrections that reduce search depth and node expansions. Experimental results show that the learned heuristic consistently improves search efficiency over standard baselines. Although this evaluation began with the 8-puzzle, the framework establishes a foundation for scaling to significantly larger and more practical …

Grp-0200 Tadps: Thrashing-Aware Dynamic Priority Scheduler For Virtual Memory Systems, Nasim Ahmed

C-Day Computing Showcase

Modern operating systems use multiprogramming and virtual memory to run multiple programs efficiently. When memory demand is high, excessive page swapping causes thrashing, degrading performance. This work introduces a thrashing-aware dynamic priority scheduler (TADPS) that uses page fault frequency to guide CPU scheduling. Processes with high page faults get lower priority, allowing others to progress efficiently. Tests under different memory pressures show that this approach improves CPU use, throughput, and stability compared with baseline schedulers such as FCFS and RR. Specifically, TADPS maintained superior throughput and efficiency, and consistently lower average turnaround time than FCFS and RR.

Grp-0197 Evaluating The Ability Of Llms To Interpret, Optimize And Translate Llvm Ir, Reuven Mueller

C-Day Computing Showcase

This study investigates whether modern state-of-the-art Large Language Models (LLMs) can interpret, optimize, and translate low-level intermediate representations (IR) used in compilers and binary translation software. We evaluate LLM performance on LLVM IR across three tasks: 1) interpreting the underlying algorithmic behavior, 2) identifying missed optimization opportunities, and generating improved IR variants, 3) Translating IR between AArch64 and x86-64 targets. To ensure correctness, all LLM generated IR is checked using a validation pipeline that verifies its syntax, structural correctness, compiles it into machine code, and executes it on randomized test data. Early results show that LLMs can perform non-trivial IR …

Grp-0176 A Comparative Analysis Of Traditional Virtual Machines And Micro Virtual Machines, Tasnim Akter Onisha

C-Day Computing Showcase

Virtualization technologies form the backbone of modern cloud and serverless platforms, but the balance between performance efficiency and strong isolation remains a key research challenge. Using a comparative literature based methodology, benchmark data from empirical studies are synthesized and normalized across four metrics: startup latency, memory footprint, isolation strength, and scalability. The findings show that traditional VMs such as KVM and Xen provide robust, formally verified isolation but incur higher boot times and memory usage, whereas Micro VMs like Firecracker and Kata Containers achieve lower latency (often < 125 ms) and smaller memory footprints (5–30 MB) while preserving VM-level isolation. Overall, Micro VMs deliver near container responsiveness with VM-grade security, making them suited for serverless and edge environments. As a future direction, this study highlights the need for scalable, formally verified Micro VM architectures for next-generation cloud systems.

Grp-0165 Reinforcement Learning For Latency-Aware Priority Boosting In Linux Completely Fair Scheduler, Joseph Natter

C-Day Computing Showcase

Tail latency remains a persistent challenge in Linux’s Completely Fair Scheduler (CFS), particularly when short, latency-sensitive jobs compete with longer ones. Traditional boosting heuristics improve tail latency but require manual tuning and generalize poorly across workloads. This project evaluates whether a reinforcement-learning (RL) controller can dynamically apply priority boosts more effectively than fixed heuristics. Using a discrete-event Python simulator modeled after CFS, this project compares baseline CFS, heuristic boosting, and PPO-based learned boosting under mixed workloads. Results show that while heuristics achieve the lowest absolute latency, RL achieves competitive tail-latency reduction with significantly better fairness and adaptability. A state-space study …

Grp-0161 Predicting The Linux Scheduler’S Next Move With Transformers, Amirmohammad Naddaf Shargh

C-Day Computing Showcase

We study whether deep learning can help the Linux CFS, which makes fair scheduling decisions without using historical behavior and may preempt tasks that are near completion. We adopt a dataset and baseline LSTM from earlier work and introduce a Transformer model to explore predictive scheduling. We train both on real scheduling traces to learn the next selected task and timing trends, and evaluate them using task classification accuracy and direction accuracy. Our results show that the LSTM remains the stronger baseline and captures CFS patterns more effectively than our Transformer model in this comparison under identical conditions for fairness.

Grp-0160 Quantum Anonymous Notification Protocol, Nitin Jha, Prateek Paudel

C-Day Computing Showcase

The scalability of current quantum networks is limited due to noisy quantum components and high implementation costs, thereby limiting the security advantages that quantum networks provide over their classical counterparts. Quantum Augmented Networks (QuANets) address this by integrating quantum components in classical network infrastructure to improve robustness and end-to-end security. To enable such integration, Quantum Anonymous Notification (QAN) is a method to anonymously inform a receiver of an incoming quantum communication. Therefore, several quantum primitives will serve as core tools, namely, quantum voting, quantum anonymous protocols, quantum secret sharing, etc. However, all current quantum protocols can be compromised in the …

Grp-21187 Adaptive Sched_Deadline On Linux For Robotic-Arm Control, Zhiguo Liu

C-Day Computing Showcase

Industrial robot arms often run 500–1000 Hz control loops on general-purpose Linux. Most cycles are on time, but rare long scheduling delays can cause visible end-effector jitter, force spikes, and unstable insertion behavior. Static tuning of priorities and budgets cannot fully remove these long-tail outliers without wasting CPU. This work asks whether a lightweight adaptive layer on top of SCHED_DEADLINE can reduce high-percentile latency while keeping good utilization.