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AGED 539 Graduate Internship Report - Monache High School
No full textThe goal of this project was to create a clear and organized Introduction to Agricultural Mechanics curriculum that gives students a strong starting point within the Monache High School agricultural mechanics pathway. The curriculum now provides students with consistent expectations related to safety, basic tool use, and appropriate behavior in a shop setting that directly supports the structure and expectations of the Monache Agricultural Mechanics Program
Reimagining Carceral Spaces – A Guerilla Tour of HM Prison Pentridge.
No full textIn this paper, we reflect on our experience of running a Guerilla Walking Tour of the now decommissioned and repurposed HM Prison Pentridge. This original teaching activity, conducted with Master of Criminology students, was grounded in traditions of critical and radical pedagogy, which stem from the broad recognition that education can never be neutral or value free, but rather, that it is a political process through which students can become actively engaged in the worlds they are learning about so that they can critique and transform them. To this end, our walking tour was designed to encourage students to examine the role power plays in the (re)constitution of social memory, and to reflect on the implications this has for addressing colonial and carceral violence in contemporary institutions
Supporting Migrant Education Program Students
No full textThis project explored the impact of the EPIC engineering camp on Migrant Education Program (MEP) participants, a group often academically disadvantaged due to frequent moves, limited resources, and lack of encouragement to pursue higher education. Because no new IRB-approved data could be collected during camp, pre- and post-camp surveys that students had already completed were analyzed. I converted the data into CSV files, built SQL tables, and wrote a Python script to automate the process of filtering and comparing MEP students, Session 1, with non-MEP peers. Results showed that 28% of MEP students started camp unsure about engineering, but 11% later shifted toward considering or planning to major in engineering. Sixteen percent reported that “no one really” encouraged them to pursue engineering, yet there was a 33% increase in students who “highly agreed” they felt confident becoming engineers. These findings suggest EPIC had a meaningful impact, though further research and resources are needed to better support MEP students
TVAC Testing with STELLA
No full textThermal Vacuum Chamber (TVAC) testing is a staple in testing for spacecraft components and parts. Prior to flying in space, each spacecraft must show that their system will be able to perform while experiencing a large temperature swing from -100 to 100 C. In the fall of 2023, the AERO department’s new Thermal Vacuum Chamber (TVAC) was completed and ready to begin system checkout and testing. Over the past 1.5 years, the TVAC system has undergone short-duration tests to refine operational procedures and build general knowledge of the system. Starting this summer, the system will begin its first long-duration tests. Students in this SURP will learn how to operate the vacuum chamber, set it up for long-duration tests, and create a one-page synopsis outlining the system and its capabilities. After completing the necessary training, the tests will include an 8-hour test, a 12-hour test, and potentially a 24-hour test, depending on the results of the initial tests. **Due to current restrictions, the students applying must be US Persons*
HPC Configuration and Automated System Administration
No full textWith the exponential growth in the use of computing, there is a growing need for undergraduate students to enter the workforce with experience with more complicated computing architectures. The CFD research group in the Aerospace Engineering Department received an HPC system and related computing hardware through the Air Force Research Lab. This system consists of three components: (1) a cluster compute engine with 256 CPU cores, 3.2 TB of RAM, 4 Tesla A100 GPUs, and 200 Gbps InfiniBand network backplane; (2) a high performance storage platform with 540 TB of raw storage, 200 Gbps InfiniBand network, and BeeGFS parallel cluster filesystem, and (3) a long term file storage server. These three systems need to be stood-up, tested, and upgraded as needed to the latest versions of their operating systems. There also needs to be system administration plans developed along with a coordinated suite of automated system administration utilities to monitor and maintain these systems. Finally, there a number of research specific software applications that need to be installed on this system, including commercial computational fluid dynamics applications and custom libraries for future research activities
Performance Dashboard Design for Radiologists: Highlighting What Matters & Fostering Improvement
No full textTurboRad has developed a system with proven results that streamlines radiology report creation and improves radiologist productivity. A study performed in 2023 found that “the advanced macro-button toolbar system has great correlation for both lower report completion times and increased wRVU productivity. The benefits of the toolbar system are most evident in the simpler XR reports. Though they are simpler to read and complete reports, the greater quantity of XR reports contributes to a greater overall wRVU completion rate throughout the day as radiologists can spend more time on more complex examinations and reports” (Chow, Speece, & Holtzman, 2024). Since 2023, the system has been moved to a new platform that can integrate with various practices’ picture archiving and communication system (PACS). With the industry-wide launch of this new reporting tool coming soon, TurboRad is planning to offer their customers a total solution by understanding the performance metrics they care about the most and offering a tailored dashboard to track progress to these metrics that ties with results of using the new reporting tool. This SURP project has two parts – develop the process for collecting the voice of the customer and develop an interface that tailors a metric dashboard to the needs of the radiologist. This research project will advance student understanding of the process of continuous improvement in healthcare and provide them with an opportunity to gain experience and develop expertise in healthcare data analytics
Evaluating machine-learning models for drone-based image analysis of coastal ecosystem dynamics
No full textPrevious work from our group utilized drone-based images and a novel machine learning classification model to quickly and accurately quantify spatial variability in eelgrass in Morro Bay. In this SURP project we aim to develop and evaluate quantifiable estimates of the model’s prediction uncertainty. Our model produces a binary labeling indicating the presence or absence of eelgrass at each pixel. However, internally the model produces a probabilistic output indicating the likelihood of detection at each pixel that we propose to use to produce uncertainty estimates and a confidence interval for the cumulative extent of eelgrass detected. This would allow for more credible communication of measurements when measuring the area of eelgrass across a study area or analyzing the change in the amount of eelgrass over time. To evaluate and calibrate our uncertainty predictions, we will compare them to the empirical uncertainty observed in our ground truth annotations
Generalized Detection of Animal Behavior Using Accelerometers
No full textAnimal mounted sensors are becoming increasingly used to passively monitor both domestic and wild animals. Advances in lightweight accelerometer and GPS technology have allowed many animals to be fitted with high accuracy sensors for extended periods of time. This leads to new opportunities to study animal behavior without direct observation. However, interpreting the raw data is difficult due to the high volume and missing context of the information. Machine learning techniques excel at extracting information from raw data streams and are excellent candidates for processing the sensor data. However, due to large variance in how different animals execute the same behavior, success in this task has largely been relegated to functionality on small groups of animals in similar environments. Additionally, the bottleneck of capturing enough high quality annotations of animal behavior with mounted sensors incurs too large a cost for many use cases outside of large agricultural operations. This thesis tests a large variety of supervised machine learning techniques under a wide range of conditions and data to verify the highest performing setup. We also study transfer learning techniques to increase the generalizability of machine learning models without increasing the burden on field scientists to gather additional data for every desired animal. This work serves as a basis for modeling animal behavior that can apply to a broad range of future work
