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Mentoring in the undergraduate classroom and beyond: Creating a feminist praxis
No full textIn the following critical commentary, it is discussed how mentoring--- in the classroom and in academia---can be a feminist practice that comprises an essential part of being a feminist educator. Mentorship encompasses a process in which the mentor disseminates knowledge as well as psychosocial support that are perceived by the mentee as essential for professional or personal development (Sedlacek et al., 2007). Mentorship has been specifically found to fulfill professional needs for women, while enhancing their self-worth and sense of competence, and providing instances of role modeling and social affirmation (Meschitti & Lawton Smith, 2017)
Transformative Pedagogy: Going Beyond An Inclusive Curriculum
No full textThis critical commentary argues that approaches toward inclusion in higher education, particularly within curriculum design, are insufficient for creating true systemic change. While efforts to include marginalized voices in humanities curricula indicate some progress, the foundational frameworks continue to center privileged perspectives, relegating marginalized voices to mere additions rather than centralizing them. Drawing on examples from history, social studies, and anti-violence work, this commentary contends that higher education must move beyond inclusion and toward a reimagining of curriculum that critically examines and dismantles the power structures underpinning knowledge production. The commentary emphasizes the need for a curriculum that not only includes diverse perspectives but centralizes them in new narratives of knowledge
2025 Annual Hackathon Redesigning Safety Fall Protection
No full textThe Verifying Everyone’s Safety Together (VEST) Hackathon is an annual event organized by construction management and social science students at California Polytechnic State University, San Luis Obispo. Its purpose is to redesign a specific piece of personal protective equipment (PPE) to improve usability, comfort, and inclusivity, with a focus on providing equitable safety for all workers. This hackathon centered on fall protection, specifically safety harnesses, which are essential for protecting workers at height but often lack comfort and user-friendliness. Students, faculty, and industry guests gathered to examine current harness designs and develop practical, worker-informed improvements. The event space was outfitted with tables, materials, and food to support team collaboration. Participants formed groups and began “hacking” existing harnesses—evaluating them firsthand, identifying pain points, and building improved prototypes. One team created a comfort-minded solution, while others focused on achievable upgrades that could be implemented with current industry standards. By the event’s conclusion, a shared realization emerged: fall protection equipment, especially safety harnesses, is overdue for redesign to better serve the physical and ergonomic needs of the workforce. The hackathon highlighted the importance of user-centered design in PPE and the power of collaboration to drive safety innovation on construction sites
A Review of Work-Life Balance Among Recent Cal Poly Graduates
No full textWork-life balance has been a heavily debated topic in the construction industry, especially post-COVID. Due the popularization of the topic as well as the introduction of “hybrid” work schedules among multiple industries the idea of work-life balance within the construction industry has become prevalent. This paper aims to gather data about work-life balance and stress among the construction management industry. A survey was distributed to recently graduated construction management students from California Polytechnic State University – San Luis Obispo to gather quantitative data regarding work-life balance, commuting, work hours, stress, and mental health. This survey also features a voluntary section to collect qualitative data regarding different firm’s ideals on work life balance and how it has affected the participants lives as a while. The findings from this survey are meant to inform prospective graduates about what to expect regarding work-life balance in their full-time roles and how they might help to improve it within their companies
AS-990-25 Resolution on Semester Curriculum Unit Limits for Cooperative Education and Internship Courses
No full textResolves that a maximum of 12 semester units (10% of 120 units) of combined cooperative education and internship course credit be applied towards baccalaureate program requirements; and it further resolves that senior projects with an internship component count toward this 12-unit total limit for baccalaureate programs; and it further resolves that a maximum of 6 semester units (20% of 30 units) of combined cooperative education and internship course credit be applied toward graduate programs requirements; it also resolves that to aid in tracking these unit limits, the words \u27internship\u27 or \u27cooperative education\u27 must appear in the course title for these types of courses; it further resolves that the senate request the language on the Academic Programs and Planning Academic Policies website on Cooperative Experiences section on Criteria be updated to read \u27Undergraduate students may receive up to 12 semester units of credit on completion of a full semester’s cooperative education/internship course and submission of an acceptable final report. A maximum of 12 semester units of combined cooperative education and internship course credit may be applied towards baccalaureate program requirements.\u27 \u27Graduate or post baccalaureate students may earn up to 6 semester units of cooperative education credit toward the graduate degree.\u2
AS-992-25 Resolution to Establish the Add/Drop Timelines on Semesters
No full textResolves that the Academic Senate of California Polytechnic State University, San Luis Obispo supports the adoption of the attached policy for Add/Drop Timelines for Semester and Summer/Condensed Session Terms; and it further resolves that the following obsolete add/drop policies be rescinded: AS-74-80, AS-145-83, AS-149-83, AS-175-85; and it further resolves that the add/drop policy for semester and summer/condensed session terms will be effective beginning Fall Semester 2026; and further resolves that the Academic Senate recommended, after the integration with Cal-Maritime, that the Resolution to Establish Add/Drop Timelines on Semesters, be reviewed to assure any needs unique to Cal Poly, Solano programs are met
AS-997-25 Resolution to Establish the Academic Senate Graduate Committee
No full textResolves that the Academic Senate establish a standing Graduate Education Committee; and further resolves that the committee\u27s responsibilities in the attached revision to the Academic Senate bylaws be adopted
Deep Learning Framework For Option Pricing
No full textAccurately pricing American options with market data presents a significant challenge, as foundational models like the Black-Scholes-Merton (BSM) model rely on assumptions that deviate from real-world financial data -- such as log-normal returns, constant volatility, and no dividends -- and fail to account for the key early exercise feature of American options. While parametric models can adjust for these features, the complexity of the resulting models renders them prohibitively difficult to apply in practice for nonspecialists. In response, modern machine learning (ML) techniques provide a set of flexible and powerful alternatives, and recent research has explored the application of ML for various purposes in quantitative finance. This study investigates American option pricing methodologies using end-of-day SPY ETF data from February 2012 to May 2024, focusing on a multilayer perceptron (MLP) neural network architecture that utilizes a small number of core inputs: the underlying SPY price, strike price, time to expiry, risk-free interest rate, historical volatility, implied volatility, dividend yield, and an indicator for ex-dividend date. Through a walk-forward cross-validation approach, this research aims to determine whether MLPs can outperform BSM for pricing options, as measured by out-of-sample RMSE, using end-of-day data with midpoint option prices as the target and either realized volatility or implied volatility as inputs. It also examines the extent to which unconstrained neural networks violate fundamental no-arbitrage conditions when pricing American options. The results indicate that MLPs are more flexible in capturing option price behavior, especially when using implied volatility inputs. However, unconstrained networks tend to violate key no-arbitrage principles. This limitation underscores the importance of incorporating structural constraints to enhance robustness and relevance, but also opens the door to developing models that can support trading strategies and create an edge in the market
Optimizing Object Detection for Remote Monitoring with OWL DuckLink Radios
No full textThis project develops an AI powered object detection system integrated with OWL DuckLink radios to enable real time remote monitoring. The system benchmarks multiple AI accelerators using a Raspberry Pi 5 to determine their efficiency in low power, long range communication environments. By evaluating the Raspberry Pi 5 CPU (7.62W, 14.5 camera FPS, 3.8 inference FPS), Raspberry Pi AI Camera with Sony IMX500 (6.45W, 30 camera FPS, 9.2 inference FPS), and Raspberry Pi AI Hat with Hailo-8L chipset (7.80W, 30 camera FPS, \u3e30 inference FPS), this project identifies the most effective combination of hardware and software for edge AI applications. The system successfully demonstrates end to end functionality by transmitting object detection data through OWL\u27s LoRa based DuckLink network to the Data Management System, proving feasibility for disaster response, security, and wildlife monitoring applications. These results provide concrete performance metrics and power consumption data that will help OWL Integrations make informed hardware decisions for future AI powered remote sensing deployments, establishing a foundation for scalable real time AI implementations in resource constrained environments
