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Measurements of Spinal Posture During Common Strawberry Picking Positions Using Inertial Measurement Units
No full textPhysically demanding professions, such as agriculture, put people at an increased
risk for experiencing back pain. This is due to the awkward and harmful postures sustained
throughout their workday. While studies have been done demonstrating that back pain is
prevalent amongst this community, most do not collect motion capture (MoCap) data,
leaving the specifics of the kinematics, such as the amount of time spent in harmful
postures, unknown. Unfortunately, MoCap data is typically limited to that of a lab or
facility where the cameras are housed. Therefore, this study aimed to mitigate the
inaccessibility of traditional MoCap by validating that inertial measurement units (IMUs)
can be used as a mobile alternative. This was done by recruiting 30 Cal Poly, San Luis
Obispo students, faculty, and staff to mimic movements typically performed in farm work
whilst wearing MoCap markers and two inertial measurement unit sensors. Results of this
study showed that there were statistically significant differences between MoCap and the
IMUs for most metrics assessed, likely due to MoCap marker occlusion causing
measurement inaccuracies, but that the IMUs serve as a reasonable alternative. These
results, in addition to occupational health survey data from farm working women, were
used to outline a future study where IMUs will be placed on farm workers to wear during
their workday, after which their data will be analyzed to assess extent of harmful postures
and correlations with relevant survey-reported measures
Gene Expression of Anthocyanin Pigments in the California Native Plant Leptosiphon parviflorus: Interactions Between Flower Color Morphology and Abiotic Stressors
No full textFor plants to survive in harsh conditions imposed by abiotic stressors, they must develop mechanisms of adaptation. One of the common types of adaptation seen in plants is the production of anthocyanin pigments, which have been shown to protect plants from UV radiation, metabolic stress, and drought. In this study, we used the flower color polymorphic species Leptosiphon parviflorus to investigate the role of anthocyanin pigments in protecting plants from abiotic stress. To accomplish this, RNA samples were taken from flower, leaf and root tissue of different flower color morphs of Leptosiphon parviflorus (Polemoniaceae) grown in high magnesium and water limitation conditions to mimic the stressors seen in their natural environments. Gene expression analysis was conducted through the use of quantitative polymerase chain reaction (qPCR) performed on five genes relating to the anthocyanin biosynthesis pathway. Results show that anthocyanin biosynthesis genes are differentially expressed in the different flower color morphologies of L. parviflorus, with pigmented morphs having between 5 and 10 times higher absolute RNA concentration than non-pigmented morphs in flower, leaf and root tissue. Furthermore, expression is affected by soil treatment, with both high magnesium and low water stress generally increasing the expression of anthocyanin biosynthesis genes. These results ultimately provide understanding for the adaptation and evolution for plants to harsh soils, specifically the role that anthocyanin pigments play in the response to abiotic stressors in L. parviflorus
OWL Integrations Soil Monitor
No full textThis paper examines the design process and results concerning the OWL Integrations soil monitor. Disaster relief relies heavily on digital communications, especially in environments where conventional methods (e.g. Wi-Fi, cellular, fiber optics) are unusable. To help emergency services assess disaster-stricken areas, a soil monitoring system was developed using LilyGo T-Beams (ESP32), programmed with specialized firmware known as the ClusterDuck Protocol to collect data pertaining to moisture and temperature. The project is sponsored by OWL Integrations, with help from the Cal Poly Electrical Engineering (EE) Department. Results show positive prospects, with the product able to make 4 km transmissions, operate for approximately 18 hours, demonstrate IP67 weather resistance, almost measure moisture and temperature with a ±5% error margin, and establish end-to-end communication between it and a cloud network
Ecological Footprint Analysis of ChatGPT (GPT-3)
No full textClimate change is an escalating crisis that demands immediate action from all sectors, including the rapidly advancing field of artificial intelligence (AI). While AI offers climate solutions, its own environmental impact raises concerns. Unfortunately limited research due to rapid development, system complexity, and lack of standardized methodologies hinders our understanding of AI’s environmental consequences. This project aims to conduct a comprehensive ecological footprint analysis of OpenAI’s GPT-3 model that is used to power ChatGPT, establishing guidelines for assessing AI systems’ environmental impact and proposing a framework for improvement. Going beyond tracking carbon emissions, this project will outline the broader lifecycle of GPT-3, from hardware components used, through the energy-intensive training process, to ongoing maintenance and eventual decommissioning. The purpose of the ecological footprint is to quantify the total environmental resources required to support GPT-3’s operation and the corresponding area of productive land and water ecosystems needed to supply these resources and assimilate the resulting waste. This comprehensive assessment will help identify key areas of environmental concern and opportunities for sustainability improvements in AI systems. Ultimately, the findings from this project could inform future regulations and support the development and research of energy-efficient AI technologies, contributing to the broader goal of creating a sustainable tech industry. This project will create a user-friendly visual so consumers and buyers can easily understand the impacts of ChatGPT. By providing a replicable framework for AI ecological footprint analysis, this research aims to encourage transparency, promote sustainable practices in AI development, and contribute to the responsible advancement of AI technologies in the face of climate change
Creating Feminist Icons, Engaging with Feminist Concepts via AI
No full textThe authors describe an undergraduate media diversity course assignment that integrates artificial intelligence (AI) to explore gender representation in media. Students engage in an activity where they use AI text-to-image generators to create fictional feminist icon characters. This activity is designed to complement discussions on AI bias and gender norms in media, prompting students to critically examine how AI tools reinforce or challenge stereotypes. By linking their experiences with AI-generated images to feminist theory, students gain a deeper understanding of intersectionality, representation, and media bias. The assignment is part of a class that traces feminist movements through pop culture media content such as Wonder Woman, Barbie, Buffy the Vampire Slayer, and Abbott Elementary. Through this hands-on engagement, students reflect on the role of AI in shaping cultural narratives about women while developing a more nuanced perspective on feminism and media representation
Building Bridges, Bridging the Gap: Measuring Engineering Confidence Across SES
No full textLearning from failure is an essential component of both learning and practicing engineering. However, failure is often stigmatized and avoided in engineering education. This project aims to better understand how to support students throughout their engineering education to help them learn from their failures, rather than become frustrated or discouraged by them. The project will build on prior research in students’ responses to failure experiences to specifically analyze students who respond to failure in different ways and build on these experiences to help students in similar situations. During the SURP project, the student will use qualitative methods to analyze interview data from students at Cal Poly who have experienced failure and persisted in engineering. The interview questions focus on students’ experiences both in and out of the classroom that have supported them through learning from failure and we will use qualitative data analysis methods to analyze students’ experiences. The aim is to compile the results of the analysis to present to students and instructors at Cal Poly, as well as prepare a conference paper to report the findings to a larger audience. These results have the potential to support students to persist through failure experiences and to develop ways for instructors and institutions to support learning from failure
Building Pathways to Computer Science Careers for Latinx Students Through Multilingual Collaborative Block-Based Programming
No full textThe underrepresentation of Latinx students in computer science highlights the need for innovative and inclusive educational approaches. This project addresses challenges such as limited access to educational resources and the demand for multilingual learning tools by developing a co-located, collaborative, game-based programming environment. Designed for use on phones, tablets, and laptops, this tool supports English, Spanish, and Mixtec, facilitating broader engagement. By promoting peer collaboration and interactive learning, our approach challenges traditional notions of solitary programming and reinforces the idea that expertise is shared, fostering an inclusive and equitable learning environment