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There have recently been calls to consider the development of student empathy within engineering coursework. We argue that this goal may be reached by infusing more traditional engineering coursework with humanities. Our Humanities-Driven Science, Technology, Engineering, and Mathematics (HDSTEM) curriculum uses a humanities format as a context to discuss science and engineering advancement. The foundation of an HDSTEM curriculum is that it would reassert the importance of humans and human impact in science and engineering, while recognizing the social, political, and cultural catalysts and outcomes of technological innovation. Therefore, we hypothesize that through an HDSTEM curriculum, students will not only develop technically accurate solutions to problems posed in an engineering curriculum but will also question their ideas' impact on society. For this project, we draw on the case of an HDSTEM course, “World War II and Technology,” taught at Texas Tech University (TTU) and Rochester Institute of Technology (RIT). Specifically, we will present the analysis of linking specific problem-solving exercises and assignments that embed empathy with the delivery of the courses following an HDSTEM instruction modality. The problem-solving exercises and assignments incorporate the traditional Six Sigma define, measure, analyze, implement, and control (DMAIC) process. In these assignments, students were asked to reverse engineer technical, scientific, and logistical problems seen during World War II. In a more straightforward means to elicit empathy, students were assigned an additional empathize step with the DMAIC (EDMAIC) during two of these assignments. The empathize step was generic, asking students to take the perspective of the creators, users, and others affected by the problem and consider the societal needs and constraints of the time. Students completed four of these assignments (2 DMAICs bookending 2 (EDMAICs) throughout the course. Combining HDSTEM instruction modality and empathy problem-solving assignments, preliminary discourse analysis of assignments, which looks deeply at the language students used to create empathetic dispositions/identities within their work, revealed that students integrated empathy into technology design at various levels at both TTU and RIT. These disposition levels in empathy were observed and subjectively quantified using common rubrics. These outcomes result even from delivery at pre- and post-pandemic timeframes and at two institutions (i.e., the course was offered at TTU in the fall of 2019 and at RIT in the fall of 2022). In this consideration, the HDSTEM curriculum and empathy-embedded assignments have shown a cultivation of empathetic disposition among students. Further, based on these differing implementations, we will also present and comment on the experience of implementing the TTU course treatment at a new institution, RIT, to serve as a protocol in the future. These courses will be offered again in the fall of 2023 year to offer a comprehensive comparison between first-time (or one-off) in contrast to a sustained delivery of an HDSTEM curriculum.more » « lessFree, publicly-accessible full text available June 25, 2025
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Abstract A recent discovery in neuroscience prompts the need for innovation in image analysis. Neuroscientists have discovered the existence of meningeal lymphatic vessels in the brain and have shown their importance in preventing cognitive decline in mouse models of Alzheimer’s disease. With age, lymphatic vessels narrow and poorly drain cerebrospinal fluid, leading to plaque accumulation, a marker for Alzheimer’s disease. The detection of vessel boundaries and width are performed by hand in current practice and thereby suffer from high error rates and potential observer bias. The existing vessel segmentation methods are dependent on user-defined initialization, which is time-consuming and difficult to achieve in practice due to high amounts of background clutter and noise. This work proposes a level set segmentation method featuring hierarchical matting, LyMPhi, to predetermine foreground and background regions. The level set force field is modulated by the foreground information computed by matting, while also constraining the segmentation contour to be smooth. Segmentation output from this method has a higher overall Dice coefficient and boundary F1-score compared to that of competing algorithms. The algorithms are tested on real and synthetic data generated by our novel shape deformation based approach. LyMPhi is also shown to be more stable under different initial conditions as compared to existing level set segmentation methods. Finally, statistical analysis on manual segmentation is performed to prove the variation and disagreement between three annotators.more » « less