Pei-Yu
Chao
Sponsor: Ali Moghimi, Ph.D. Biological & Ag Engineering With consumer diets becoming more healthy, the demand for mushrooms has increased due to the excellent health benefits mushrooms provide. Despite mushrooms being a multi billion dollar industry, mushroom farmers struggle to find workers amidst the declining agriculture workforce. To combat this, robotics are being implemented across all areas of agriculture, including harvesting, but automated harvesting of mushrooms is under- explored, with current solutions often resulting in a lower quality product. Our research investigated the integration of mushroom- specific end effector and fine-tuned a computer vision model to the FarmBot, an open-source CNC (Computer Numerical Control) farming robot. After evaluating different end effectors for FarmBot integration, a pneumatic suction cup proved optimal. A vacuum system was found to minimize mushroom bruising during picking. The fine-tuning of our computer vision model involves a two- stage process which significantly improves processing time and accuracy: first, applying image segmentation technique using deep learning model, YOLOv8, to locate mushrooms in real time, followed by classification using MobileNet to determine mushroom ripeness. In conclusion, our research evaluated a cost effective robotic mushroom harvesting system that can ease the labor shortage while maintaining a similar quality of product to manual harvesting. Beyond the Swipes! Social Media Usage and Student Mental Wellbeings
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Pei-Yu Chao
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In the contemporary landscape, social media has become an integral part of individuals' lives, significantly influencing daily routines. This research project investigates the multifaceted relationship between social media usage patterns and their impact on mental well-being, with a focus on self-esteem, identity, comparison, and depressive symptoms. The study examines variables such as the time spent on social media, types of activities engaged in (e.g., passive scrolling, active posting), and social identity among college students. Approximately 100 UC Davis undergraduates participated in a 15- minute survey, providing insights into perceived and actual social media usage, self-esteem levels, and depressive symptoms. The research employs linear regressions and mediation analyses to unravel the intricate interplay between social media habits, self- esteem, and depression symptoms. The main hypothesis posits that increased passive social media use will exhibit positive correlations with depressive symptoms and lower self-esteem. By delving into these variables, the study aims to contribute valuable insights into the nuanced dynamics between social media engagement, social identity, and mental well-being among college students. The findings may inform strategies for promoting healthier social media habits and ultimately enhance the overall mental health of this demographic. Dietary Factors and Prevalence of Cardiovascular Disease in the Punjabi Sikh Community Kiran Chauhan
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UC Davis / Psychology / 2024
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Pei-Yu Chao