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Is Buddhist View of Mental States in Consonance with the Foundational Tenets of their System?: Investigating Nyāya and Buddhist Debate on Perception in Jayanta Bhatta’s Nyāyamanjari
Get PDFThe first noble truth in Buddhism points toward a purely phenomenal experience. Though purely qualitative in nature, the experience of suffering has an existential aspect. As stated in the second noble truth, suffering has a cause that relates to our situatedness in the world. Notwithstanding, the cause of suffering has to be something external to the mental state of suffering itself. Some of the more recent studies on mental states suggest two different positions on the nature of mental states. One may view mental states as self-referential, reflexive and purely qualitative in nature; alternatively, one could view them as transitive as pointing towards something external, beyond and other than themselves in terms of their cause or the like. The former of these positions is known as internalism, while the latter is named externalism. Nyāya school of Classical Indian Philosophy presents a unique position in this regard. They view pain and pleasure as objects of cognition rather than their content. As such, an act of perception becomes a mere transitive act, a mere operator or the lowest common denominator of all mental acts without having content in themselves. Buddhist logician, however, views mental states as pure qualitative states. As such, they seem to embrace internalist position regarding mental states. This paper attempts to demonstrate that this position if prescribed to Buddhism, renders their second noble truth problematic
Spartan Daily, April 29, 2025
Get PDFVolume 164, Issue 38https://scholarworks.sjsu.edu/spartan_daily_2025/1037/thumbnail.jp
Models of Inquiry and the Learner: A Fresh Look
Get PDFThis article re-examines models of inquiry through a learner-centered lens, challenging traditional notions of how information fluency is taught in school libraries. Drawing from decades of inquiry research and current developments like artificial intelligence, design thinking, and student motivation science, the authors advocate for a more personalized, visual, and student-driven approach to inquiry instruction. Key examples include student-generated inquiry models and visual representations of learning journeys as tools for assessment and reflection. The article also explores the librarian’s role in embedding inquiry across disciplines and collaborating with educators to foster critical, creative, and self-directed learners. Practical proposals include the use of visual artifacts as longitudinal indicators of learning sophistication and the integration of motivation research into inquiry-based pedagogy. The authors call for a shift in focus from institutional outcomes to individual learner growth, positioning librarians as vital contributors to personalized education and long-term learner development
CCA Analysis using Computer Vision Techniques
No full textCoral reefs are an essential part of the marine ecosystem. They perform a wide variety of tasks, some directly and others indirectly. They can produce oxygen, absorb carbon dioxide, along with supporting ocean habitat. Crustose Coralline Algae (“CCA”) plays an important role in helping provide structural support to Coral Reef ecosystems. However, global warming is causing ocean water to become more acidic resulting in coral bleaching. This is leading to changes in coral environments and causing coral deaths at alarming rates. Object detection using computer vision techniques, specifically deep learning, can help to monitor coral reef health and identify CCA in reef structures over time. This project has performed CCA analysis using Mask R-CNN and findings conclude that a balanced solution is better at CCA detection than one that only focuses on high precision. Therefore, this project evaluated unique compositions of various Mask R-CNN models and found that a large training dataset with various ecosystems contributes to a more optimal and balanced CCA analysis
Optimizing Integrated Access and Backhaul Topology using Monte Carlo Tree Search
No full textIntegrated Access and Backhaul (IAB) plays a central role in enabling scalability and high-throughput in wireless networks, especially in areas where wired backhaul is not feasible. Optimizing the topology of IAB networks is a complex task, involving trade-offs between path loss, node capacity, and link quality. To address this, this project investigates a Monte Carlo Tree Search (MCTS)-based approach to improve overall network performance by maximizing the capacity of network, considering Signal-to-Noise Ratio (SNR) on wireless links. MCTS provides a guided search mechanism to construct topologies efficiently under the constraints and is evaluated against baseline topologies. Experimental results show that our MCTS-based approach consistently produces topologies with higher minimum capacity and better SNR under a range of node constraints and network configurations
RUL Estimation of N-CMAPSS Turbofan Engines using Deep Learning with Customized Penalty and Expanded Sensors
No full textAccurate prediction of Remaining Useful Life (RUL) for aircraft engines is important to enhance maintenance efficiency and flight safety. For this project, a solution to RUL prediction on NASA\u27s N-CMAPSS data set, mimicking realistic engine degradation under simulated full-flight scenarios, is being proposed. For addressing the high-dimensional noisy sensor data challenge, a new feature engineering pipeline was utilized. Models trained on healthy data predict normal sensor behavior, and the discrepancy between these predictions—referred to as residual features—is a measure of degradation. To handle the size and computational demands of the dataset, training was conducted on Google Cloud Platform using GPU-supported virtual machines and cloud storage. The residual features were used to train a Bidirectional Long Short-Term Memory (Bi-LSTM) network to learn temporal relationships in engine behavior. The model was trained with NASA\u27s asymmetric scoring function, which penalizes overestimation more severely to make safer, more conservative RUL predictions. Testing on unseen engine units revealed the model to be close to simulating actual RUL patterns throughout degradation periods. Accuracy during early life stages remains limited due to the absence of useful degradation signals. Overall, the approach offers a robust framework for data-driven, condition-based maintenance in aviation and comparable industries
AR Circuits: Augmented Reality for Electrical Education
No full textThis research introduces ”AR Circuits,” an innovative educational tool utilizing Augmented Reality (AR) to simulate and visualize complex electrical circuits interactively. Traditional methods of teaching electrical concepts often struggle with conveying three-dimensional and dynamic structures effectively. AR Circuits addresses this challenge by leveraging AR technology, allowing users to view the real world augmented with digital content related to electrical components. The system employs fiducial markers representing circuit elements, enabling users to build, modify, and visualize circuits in real time. The integration of OpenSceneGraph for 3D graphics, AR Toolkit for marker tracking, and GnuCap for circuit analysis form the foundation of AR Circuits. The user\u27s ability to control the circuit layout through marker placement and receive real-time feedback on voltage and current enhances the learning experience. The research discusses the system\u27s goals, limitations, and potential improvements, highlighting the need for further work to improve user interaction, scalability, and educational effectiveness. As Augmented Reality continues to gain prominence in education, AR Circuits provide a glimpse into the future of interactive and immersive learning experiences for electrical engineering students
Evaluating User Interaction and Feedback Mechanisms in a Robotic Bartender: A Study on Smartini’s Social Interaction, Cocktail Preparation, and Customer Engagement
No full textCreation of a cocktail-making robot called Smartini, which is both interactive and capable of learning. Our research involved the development of the Smartini Cocktail Robot, which aimed to address the challenges of human-robot interaction and modern cocktail-making using cutting-edge technology. To achieve this, we incorporated various modes of communication, such as eye contact, gestures, and speech, and also included an entertainment system that offered news and jokes. Furthermore, Smartini was designed to learn customer preferences and adjust its recipes accordingly. Customer feedback revealed high satisfaction levels with the cocktail-making process, scoring 4.23/5. However, Smartini\u27s movements, eye contact, and ease of communication were rated 3.54/5, possibly due to limitations in the iCub\u27s speed and our computer\u27s computational power. To create a truly functional and enjoyable Smartini robot, these areas need further improvement
Are You My Counselor? A Call to Action How Latinx General Counselors Can Support Latinx Student Retention Rates at California Community College Hispanic-Serving Institutions
Get PDFLatinx students comprise the largest population within the California Community College (CCC) system, but experience disproportionately lower retention rates. This dissertation used a phenomenological qualitative design and testimonios collected from Latinx general counselors to explore factors influencing Latinx student retention. Sixteen tenured Latinx general counselors from ten Northern California Hispanic-serving community colleges served as participants. Guided by two research questions, the study explored counselors’ perspectives on the factors influencing Latinx community college students’ retention and the counseling strategies, services, and resources that General Counseling Departments should provide to support Latinx student retention. Grounded in Latinx Critical Race Theory, the study integrated a community cultural wealth and servingness framework to generate a comprehensive call to action. Findings are organized across four levels—individual counselors, general counseling departments, institutions, and systemic structures—offering actionable recommendations to transform counseling practices and advance Latinx student success. This work contributes to the field of transformative educational leadership by demonstrating how intentional, culturally responsive counseling can improve retention outcomes for Latinx students in the California Community College System. This system consists of a majority of Hispanic-serving institutions