Pacific McGeorge School of Law
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Address by Larry Jackson to the First Graduating Class
No full texthttps://scholarlycommons.pacific.edu/callison-college-sis/1047/thumbnail.jp
America, China, Japan: A Fresh Perspective
No full texthttps://scholarlycommons.pacific.edu/callison-college-sis/1049/thumbnail.jp
Autonomic control of heart rate; examining peripheral inputs that influence power spectra components of heart rate variability
No full text
Overdose Watch: Educate, Prevent, & Respond
No full texthttps://scholarlycommons.pacific.edu/nursing-portfolios/1027/thumbnail.jp
Supporting Second-Year College Students in the Murky Middle
No full textThis study articulates the needs and expectations of an intersectional population of undergraduate college students: second-year students in the murky middle. Nearly half of all student dropouts occur after the first year of enrollment, with more than 45% occurring by mid-performing students. Despite this, institutions, like researchers, do not readily focus on the retention of this population of students; a Noel Levitz study (2013) found a disparity in the prevalence of retention practices for first-year students (94%-98%) and second-year students (20%-29%). This general qualitative study utilized a semi-structured interview protocol with nine participants to gather information about the participants’ needs and expectations in their college experience. Findings highlight participants’ experiences being a second-year student, within the classroom, with peers, and in navigating campus resources. Participants articulated an awareness of their own personal and academic needs, informed by their individual circumstances and first year experience, and choices in how they chose to engage with the institution. They identified institutional challenges with key transitional tasks, including course registration and the ability to be successful in next level coursework. Finally, they made recommendations for institutional actions that would improve their experiences, including improved access to campus resources and opportunities and scaffolded support as they transitioned into and through their second year
The Artful Aging Project
No full textAs the older adult population in Sacramento County continues to expand, addressing age-related declines in fine motor function, cognition, and social well-being has become increasingly critical. Such declines can compromise activities of daily living, reduce independence, and exacerbate risks associated with social isolation. Evidence suggests that art-based interventions may mitigate these effects by enhancing motor coordination, promoting social engagement, and stimulating cognitive processes. The Artful Aging Project was designed to evaluate the effectiveness of structured art activities in improving fine motor skills and social wellness among older adults served by the Stanford Settlement Neighborhood Center (SSNC). Grounded in community-based participatory principles, the project involved strong collaboration with SSNC leadership to develop culturally inclusive programming for a diverse and multilingual senior population. Participants engaged in four weekly sessions featuring origami, calligraphy, mosaic, and collage between July and August 2025. Pre- and post-intervention assessments utilized adapted versions of the Manual Ability Measure (MAM-36) and the Functional Status Questionnaire (FSQ) to evaluate fine motor function and social well-being, respectively.
Seven participants completed both MAM-36 surveys, demonstrating an average improvement of 1.8 points, with 85.7% showing maintained or enhanced scores. Six participants completed both FSQ surveys, with 66.7% exhibiting improved post-intervention scores and an average increase of 4.2 points.
These findings indicate that structured, culturally responsive art interventions can effectively preserve or enhance fine motor abilities and social wellness in older adults, supporting the integration of art-based programming within community health initiatives for aging populations.https://scholarlycommons.pacific.edu/nursing-portfolios/1030/thumbnail.jp
Advancing cancer diagnosis and treatment: Integrating molecular biomarkers and emerging technologies
No full textCancer biomarkers can be derived from tumor cells or neighboring cells within the tumor microenvironment. Over the past few decades, various molecular markers, including DNA (mutations, copy number variations), RNA (mRNA, microRNA, circular RNA), proteins, and metabolites, have been identified with the aid of rapidly evolving technologies. Some of these markers have demonstrated potential clinical utility, while others have provided new insights into the deregulation of normal molecular and cellular processes that lead to tumorigenesis. Publications in this special issue of the Biomedical Journal introduce contemporary approaches aimed at enhancing cancer diagnosis, and monitoring of cancer and treatment options, with the ultimate goal of reducing mortality. These studies highlight the importance of integrating advanced technologies with clinical strategies for treatment of cancer
Letter from Henry C. Robinette to Brother, 1863 April 12
No full textHenry Clay Robinette, attended the Delaware Military Academy (1857-1860) and joined the Union Army at the outset of the Civil War. H.C. Robinette fought at the battles of Corinth and Vicksburg (1862) and was later on the General Grant\u27s staff (1864-1865). After the war he was court-martialed for cursing an officer in a barroom brawl (1867)but his father petitioned President Andrew Johnson on his behalf with the result that his sentence was commuted and he was promoted to brevet major for gallant and meritorious services at the Battle of Corinth and the siege of Vicksburg.https://scholarlycommons.pacific.edu/civil-war/1024/thumbnail.jp
Autonomous Trash Pickup and Sorting Robot
No full textThis project presents the design and deployment of a partially autonomous robot that integrates embedded systems, computer vision, and multi-actuator systems to detect and interact with litter in real time. The goal is to build a cost-effective, modular, and scalable system capable of identifying and mechanically removing litter using a deep-learning-enhanced, Raspberry Pi–controlled robotic platform. While fully autonomous navigation is beyond the scope of this implementation, the system demonstrates a distributed framework with real-time perception and coordinated actuation.
The robotic system is based on a Raspberry Pi 5, which handles all system functions, including isochronous servo control for robotic arm actuation, and control of a mecanum wheel drive system. The Pi performs on-board, real-time video capture, and streams it to an external processing computer over HTTP and drives a PCA9685 servo controller to actuate multi-joint end effectors. These end effectors can execute predefined motion sequences such as opening, lowering, gripping, lifting, and releasing objects defined in an external JSON configuration file. This overall design allows behavior adjustments without modifying the control code, emphasizing modularity.
All computer vision and AI processing are executed externally on a laptop, allowing the use of heavier models without the constraints of embedded hardware. The laptop runs a pretrained TensorFlow object detection model trained on the TACO (Trash Annotations in Context) dataset. Incoming video frames from the Pi are decoded, analyzed and classified into TACO’s 60 waste categories, then broken down into broader classes such as Recyclable, Organic, or General Trash. The detection results consisting of labels, confidence scores, and bounding boxes are served as lightweight JSON via a Flask API.
The Raspberry Pi continuously queries this API to retrieve updated detection outputs and selects the appropriate servo preset or movement pattern based on the classification. For example, detecting a recyclable item triggers a specific sequence of arm motions and wheel positioning relative to the target. Although the robot does not perform autonomous navigation or environmental mapping, the system supports controlled demonstration of pickup sequences activated by AI detection. This command-response architecture allows for seamless scaling into future autonomous versions.
A key contribution of the project is the creation of a robust real-time communication pipeline between the robot hardware and the remote AI processor. By decoupling perception from actuation, the system maintains flexibility, reduces computational load on the Pi and supports future upgrades such as multi-camera input, additional sensors, or integration with ROS2. The HTTP streaming and JSON prediction interface further support rapid debugging, remote monitoring, and modular component swapping. This architecture ensures the system can be easily expanded as more complex perception control modules are developed.
Experimental results show that the platform can perform reliable trash classification at approximately 10–18 FPS, depending on input resolution and lighting conditions. The servo-actuated pickup mechanism consistently responds to classification outputs, validating the effectiveness of the distributed perception-control system. While autonomous navigation remains a target for future development, this iteration successfully delivers the core components of a functional trash-handling robot: real-time detection, classification, communication, and mechanical interaction