The purpose of this study was to investigate an interdisciplinary international service learning program and its impact on student sense of cultural awareness and competence using the Campinha-Bacote’s (2002) framework of cultural competency model. Seven undergraduate and one graduate student from Human Development and Nutrition Science disciplines participated in the program. Reflections from a synthesis paper post-travel were analyzed using an inductive approach. Six themes emerged from the reflective journals and were applied to Campinha-Bacote’s cultural competency constructs. Participating and learning together while reflecting helped deepen and progress this process for ISL students. Overall, the experience proved to be an effective educational tool for sensitizing students towards cultural competency within interdisciplinary programs.

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Phase aberration arises from the speed of sound heterogeneity in the imaging environment and degrades image quality. Accurate aberration simulation is essential for developing aberration correction methods. Existing works often apply an aberration value at each channel in simulation software, but this approach introduces aberration integration error, assumes a fixed profile across all beams, and does not account for harmonic generation. We propose to address these limitations by making a PDMS aberration phantom. The manufacturing process involves (1) integrating a software-generated profile into a 3D-printed mold, (2) treating the mold with acrylic lacquer to prevent cure inhibition, (3) casting the mold with PDMS and degassing for an hour, and (4) baking at 75 °C for 4 h before demolding. The phantom is smooth and retains software-specified root mean square and full width half max of the autocorrelation function, and can be placed at the transducer to simulate aberration with higher fidelity than software. OCT data suggests that 3D-printed molds are accurate to within 21 μm of the software profiles and beamformed aberrated data exhibits a profile-dependent increase in sidelobes relative to clean data.

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