This study explores undergraduate engineering and education students’ perspectives on their interdisciplinary teams throughout the rapid transition to online learning and instruction from a face-to-face to a virtual format. In this qualitative study, students’ reflections and focus groups from three interdisciplinary collaborations were analyzed using the lens of Social Cognitive Theory. COVID-19 created a dramatic change in the environment such that the most immediate and direct impact on students’ experiences was on the environmental aspects of Bandura’s triadic reciprocal determinism model, which then triggered behavioral and personal responses to adapt to the new environment. Subsequent evidence of reciprocal effects between environmental, behavioral, and personal factors took place as students continued to adapt. Results suggest that the modifications made to transition the project fully online were meaningful experiences for students’ learning and teaching of engineering through teams. This interdisciplinary partnership provided both pre-service teachers and undergraduate engineering students with the opportunity to learn and practice content and professional skills that will be essential for success in future work environments.

In this letter, an approach is presented to estimate the stiffness characteristics of elastically deformable objects, while being processed in a robotic cleaning system. Building on our previous work [J. D. Langsfeld, A. M. Kabir, K. N. Kaipa, and S. K. Gupta, “Robotic bimanual cleaning of deformable objects with online learning of part and tool models,” in Proc. IEEE Int. Conf. Autom. Sci. Eng., 2016, pp. 626-632.], significant extensions are made in this letter by presenting new methods that lead to an overall improvement in the results. The robot system plans the actions of both the grasping and cleaning arms using its current model of the part deformation behavior, and attempts to minimize the overall cleaning time by finding plans that use a small number of grasping positions on the part. A finite-element method approach is used to model the part deformation and a general update algorithm is presented that can modify the stiffness parameters of the model to match observed deformation data collected during cleaning. This allows the system to very quickly discover the correct locations to grasp the part so as to minimize deformation. The methods described in this lettter are shown to achieve better results than the ones from our previous work: The results are much smoother, along with a twofold reduction in parameter-estimation error, and notable faster convergence.

Segmentation of the thoracic region and breast tissues is crucial for analyzing and diagnosing the presence of breast masses. This paper introduces a medical image segmentation architecture that aggregates two neural networks based on the state-of-the-art nnU-Net. Additionally, this study proposes a polyvinyl alcohol cryogel (PVA-C) breast phantom, based on its automated segmentation approach, to enable planning and navigation experiments for robotic breast surgery. The dataset consists of multimodality breast MRI of T2W and STIR images obtained from 10 patients. A statistical analysis of segmentation tasks emphasizes the Dice Similarity Coefficient (DSC), segmentation accuracy, sensitivity, and specificity. We first use a single class labeling to segment the breast region and then exploit it as an input for three-class labeling to segment fatty, fibroglandular (FGT), and tumorous tissues. The first network has a 0.95 DCS, while the second network has a 0.95, 0.83, and 0.41 for fat, FGT, and tumor classes, respectively. Clinical Relevance—This research is relevant to the breast surgery community as it establishes a deep learning-based (DL) algorithmic and phantomic foundation for surgical planning and navigation that will exploit preoperative multimodal MRI and intraoperative ultrasound to achieve highly cosmetic breast surgery. In addition, the planning and navigation will guide a robot that can cut, resect, bag, and grasp a tissue mass that encapsulates breast tumors and positive tissue margins. This image-guided robotic approach promises to potentiate the accuracy of breast surgeons and improve patient outcomes.