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A Holistic Inquiry of Intelligent Speed-Assist Technology: Safety Impacts, Technology Implementation, and Challenges
Get PDFSpeeding is a leading contributor to roadway fatalities in the United States, and California consistently ranks among states with the highest number of speed-related crashes. Intelligent Speed Assistance (ISA) technology has emerged as a solution aimed at mitigating this issue by notifying drivers of speed limits and, in some cases, intervening and lowering the speed to the posted speed limit. This work presents a comprehensive investigation into ISA system safety benefits, implementation challenges, and public perception, with a focus on California drivers. This study utilized a multi-method approach. A literature review explores the history, regulatory barriers, and international trails of ISA systems. A large-scale quantitative analysis was conducted on over two million consumer complaints and nearly 300,000 recall records from the National Highway Traffic Safety Administration. Filtering for ISA-related issues revealed over 100,000 relevant complaints and 6,000 related recalls, uncovering recurring themes including system malfunctions, override limitations, sensor and mapping errors, and unintended acceleration. An original survey of 286 licensed California drivers was administered to assess public awareness, behavioral tendencies, and attitudes towards ISA technology. While a majority of participants acknowledged the potential safety benefits of ISA, many expressed concerns regarding the loss of driver autonomy, system reliability, and data privacy. Drivers favored advisory or supportive ISA systems that provide feedback without fully controlling vehicle speed. The finding suggests that while ISA systems are well-positioned to reduce speeding and enhance road safety, their success hinges on thoughtful design, user trust, and supportive policy. As California and other states consider broader implementation, aligning driver preferences with technological capabilities and regulatory frameworks will be essential to using ISA to improve safety
Spartan Daily, November 4, 2025
Get PDFVolume 165, Issue 31https://scholarworks.sjsu.edu/spartan_daily_2025/1074/thumbnail.jp
Spartan Daily, April 17, 2025
Get PDFVolume 164, Issue 34https://scholarworks.sjsu.edu/spartan_daily_2025/1033/thumbnail.jp
Quantifying Precancerous Colonic Neoplasia Dynamics and Optimizing Surveillance Policies for Improved Patient Outcomes
No full textColorectal cancer (CRC) is the third leading cause of cancer-related deaths among men and the fourth leading cause among women in the United States. This study aims to 1) investigate the dynamics of precancerous colonic neoplasia and their progression to colorectal cancer, and 2) develop colorectal cancer surveillance strategies to optimize patient health outcomes. To achieve these goals, we analyze colonoscopy reports from over 40,000 patients collected from four major VA hospitals. Statistical models are developed to capture the dynamics of CRC progression. These insights are then integrated into a reinforcement learning framework to design personalized CRC surveillance policies, ultimately enhancing patients quality of life.https://scholarworks.sjsu.edu/uss/1065/thumbnail.jp
Collaborative Governance in Practice: Evaluating Intergovernmental Relations through the Santa Clara County Healthy Cities Initiative
Get PDFIn the United States, intergovernmental relations (IGR) is rooted in the principles of American federalism, which focuses on the constitutional power dynamics between national, state, and local governments. While federalism outlines the structural framework, IGR specifically focuses on how federal, state, and local governments interact administratively, financially, and politically within the federal system. Beginning in the 1960’s, governments across the U.S. began to rely on non-governmental and private sector organizations for program implementation (Boyd & Fauntroy, 2000). This shift gave rise to the concept of collaborative governance, which expanded the scope of IGR by incorporating traditionally excluded groups from the governmental process. Today, a reliance on collaboration between governmental and non-governmental actors has become standard practice in public administration, therefore, understanding how best to use IGR as a tool for effective governance is critical for public administrators and policy makers nationwide.
This study will focus on the policy to eliminate youth access to tobacco, as it has high levels of engagement from the other cities in the County. As of 2025, five of the thirteen cities (excluding Monte Sereno and Los Altos Hills) in Santa Clara County have implemented a tobacco retail license ordinance that aligns with the County ordinance. This research will examine how the intergovernmental relationships between the County and each of the thirteen cities affects policy adoption and implementation using a model framework on collaborative governance. The framework, developed by Emerson, Nabatchi, and Balogh (2012), outlines a set of components they propose as necessary for successful collaborative governance and policy implementation, including 10 propositions about what leads to collaboration and effective outcomes. Using the framework and the propositions, this study will analyze the SCC HCI program, using data from the County’s online dashboard and publicly available documents, to compare cities in three different categories; 1) those who have fully implemented the policy, 2) those who have started but not finished, and 3) those who have not yet started. Information was collected from city council documents, news articles, and city webpages to indicate whether specific elements were present in each city\u27s policy adoption process. These findings will show whether or not cities followed all or some of the propositions from the Emerson et al. (2012) model and the analysis will identify whether or not patterns arise from this review that can be recommended to the SCC HCI program
Nontraditional Students Enrolling in Online Education: Assessing Outcomes and Barriers to Success
Get PDFOnline education has significantly reshaped how colleges and universities define and evaluate academic achievement. In March 2020 during the height of the Covid-19 pandemic, the U.S. government enforced social distancing regulations that affected operational continuity for higher education institutions. The need to transition from traditional classroom instruction to online was unavoidable. Institutions were not prepared, however, and their shortcomings in providing high-quality online learning for students were exposed. The inadequacy can be due to higher education institutions decade-long mindset of seeing online education as a less credible alternative. As a result, institutions are more eager than ever before to invest in resources for expanding online education. Traditional structure that historically thrived in higher education was changing in response to the rising demand for online education.
In response to the growing demand for online education and the emergence of new enrollment pathways, the researcher investigated two questions: (1) what challenges affect nontraditional students\u27 academic achievement, and (2) does online education support degree completion for nontraditional students? To find the answers, the researcher examined nontraditional students enrolled in online undergraduate programs at California\u27s public university system, California State University (CSU), using data from a student survey and staff interviews. Five hypotheses were then analyzed to see whether there is a correlation between data variables and if these could potentially answer the research questions. The first hypothesis states that students who indicate a higher connection with their peers at school are less likely to experience academic challenges. The second hypothesis states that students who are more likely to utilize student support services are less likely to experience academic challenges. The third hypothesis states that students who perceive their school as supportive are more likely to enroll in more online programs. The fourth hypothesis suggests that students who faced early financial challenges as a barrier to pursuing higher education are more likely to support online programs. The last hypothesis states that students who choose degree programs based on affordability typically experience better online learning outcomes
Landslide Prediction Using Time-Series Decomposition, Reinforcement Learning-based Feature Selection and ML Models
No full textLandslides pose significant risks to human life, the community, and the environment, yet their prediction remains a complex and unexplored challenge. Existing prediction models often rely on surface measurements and satellite images, neglecting the critical role, in providing deeper insights into landslide analysis. The literature review highlights a lack of research in time series decomposition techniques, despite their potential to improve prediction accuracy. Similarly, feature selection methods that enhance model robustness and precision have not been adequately addressed. This study presents a novel approach to predicting landslide displacement by combining feature selection through reinforcement learning techniques with advanced time-series machine learning modeling techniques. Reinforcement learning is used to dynamically select impactful features, optimizing the input for prediction models. These insights guide the development of advanced and hybrid machine learning models trained and tested on comprehensive datasets, aiming to enhance prediction accuracy and efficiency. In addition, this research emphasizes the importance of weighted evaluation methodologies to prioritize essential data points, ensuring robust predictions. This work sets a new benchmark for predictive modeling and operational strategies in landslide monitoring by addressing key field gaps
Combining ESM models with Experimentally Derived Structural Stability to Identify Functional Missense Mutations
No full textMissense mutations can impact protein function and structure, yet their effects on protein function are difficult to predict. In this study, I compared two deep learning models, ESM1v and ESM1b, by evaluating their mutation predictions against experimental structural stability data. ESM1v showed a stronger correlation with experimental structural stability scores compared to ESM1b. A sigmoid curve was fitted to explore this relationship further. Over 100,000 mutations were identified where experimental stability differed significantly from model predictions. Many mutations that remained structurally stable experimentally but were predicted as harmful by the ESM models were frequently found at known functional sites. Structural analysis using AlphaFold2 and clustering with DBSCAN showed these mutations often grouped closely in 3D space. For example, position 188 in protein A1X283, located in a peptide-binding region, highlighted this functional significance despite structural stability. These findings demonstrate that comparing ESM1v predictions with experimental data can uncover important functional mutation sites which dosen’t necessarily affect structural stability. This integrated approach provides valuable insights for future protein engineering and disease research
Simple vs. Complex Human Activity Classification via Hybrid Machine Learning Models
No full textIn-depth understanding of the complexity of daily human activities is crucial for building responsive health monitoring and assistive technologies. However, limited research has focused on distinguishing activities based on their involvement level, as most existing work classifies only the type of activity performed. In this thesis, we address this gap by proposing a method to classify human activities as either simple or complex using sensor data from the Opportunity dataset. We define complex activities as those involving object interactions or multiple coordinated movements (e.g., drinking from a cup, cleaning a table), and simple activities as static or low-effort postures (e.g., standing, sitting). Based on domain-specific heuristics, we label each time window and extract both time-domain and frequency-domain features. We evaluate multiple models, including a hybrid deep learning architecture combining Convolutional Neural Networks (CNNs) and Transformers, trained on multi-sensor data across varied window lengths. To ensure robustness and generalization, we apply Leave-One-User-Out (LOUO) and Leave-One-Episode- Out (LOEO) evaluation. Our results show that the proposed approach reliably distinguishes between simple and complex activities, outperforming traditional classifiers and offering new directions for fine-grained activity recognition in real-world environments
PhotoProof: A Mobile Application for Verifying the Authenticity of Images
Get PDFThe easy access to artificial intelligence (AI) technologies, such as deepfakes and generative adversarial networks (GANs), has facilitated the creation of highly realistic artificial images, thereby undermining the authenticity of photos in today’s digital age. Misinformation and manipulation are key dangers to digital content due to this advancement. Therefore, the need for reliable methods of photo verification and authentication has become increasingly important. This report presents a decentralized iOS app that uses blockchain to ensure photo authenticity. The app leverages Ethereum smart contracts and cryptographic hashing to securely log image metadata. When a user takes a photo, the app hashes the image along with key metadata like time, location, and device details—and stores the hash on the blockchain. This creates a permanent, tamper-proof record. Users can later verify the authenticity of the image by re-uploading it to the app. The system then recalculates the hash and compares it with the one saved in the blockchain, verifying if the image has been tampered with. The app also employs Merkle Trees to support partial metadata validation, providing flexibility where some of the metadata may not be available. The solution is very cost-effective to deploy. It is inexpensive, approximately 0.00025 SEPETH per hash($0.0000326 USD), and near-instantaneous verification with no fee. These findings show that the solution is feasible and scalable, offering a cost-effective approach to maintaining photo integrity with minimal cost