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The emergence of bio-additive manufacturing marks a crucial advancement in the field of biomedical engineering. For successful biomedical applications including bioprinted organ transplants, ensuring the quality of printed structures poses a significant challenge. Among the major challenges encountered in ensuring the structural integrity of bioprinting, nozzle clogging stands out as one of the frequent concerns in the process. It disrupts the uniform distribution of extrusion pressure, leading to the formation of defective structures. This study focused on detecting defects arising from the irregularities in extrusion pressure. To address this concern, a video-based motion estimation technique, which emerged as a novel non-contact and non-destructive technique for assessing bio 3D printed structures, is employed in this research. While other advancements, including contact-based and laser-based approaches, may offer limited performance due to the soft, lightweight, and translucent nature of bioconstructs. In this study, defective and non-defective ear models are additively manufactured by an extrusion-based bioprinter with pneumatic dispensing. Extrusion pressure was strategically controlled to introduce defective bioprints similar to those caused by nozzle malfunctions. The vibration characteristics of the ear structures are captured by a high-speed camera and analyzed using phase-based motion estimation approaches. In addition to ambient excitations from the printing process, acoustic excitations from a subwoofer are employed to assess its impact on print quality. The increase in extrusion pressure, simulating clogged nozzle issues, resulted in significant changes in the vibration characteristics, including shifts in the resonance frequencies. By monitoring these modal property changes, defective bioconstructs could be reliably determined. These findings suggest that the proposed approach could effectively verify the structural integrity of additively manufactured bioconstructs. Implementing this method along with the real time defect detection technique will significantly enhance the structural integrity of additively manufactured bioconstructs and ultimately improve the production of healthy artificial organs, potentially saving countless lives. 

Social robots can enhance deeper learning through social processes by providing companionship during typically isolated learning activities. Yet, there is limited exploration into the use of authoring tools for teachers to create and customize social robot-assisted lessons. To address this need, we present PATHWiSE, an authoring tool that utilizes teacher-in-the-loop AI-assisted verbal and non-verbal robot interaction design to customize RAL lessons to the needs and strengths of individual students and classrooms. We demonstrate the operation, AI-assist functions, and practical applications of the PATHWiSE UI. Our work underscores the need for developing tools for computing novices utilizing AI and RAL technologies 

Educational technologies can provide students with adaptive feedback and guidance, but these systems lack personal interactions that make social and cultural connections to the student's own classroom and prior experiences. Social or companion robots have a high capacity for these types of interactions, but typically require advanced levels of expertise to program. In this study, we examined teachers use of an authoring tool to enable them to leverage their classroom-based expertise to design robot-assisted homework assignments, and explore how seeing a robot enact their designs influences their work. We found that the tool enabled the teachers to create novel social interactions for homework activities that were similar to their classroom interaction patterns. These interaction designs evolved over time and were shaped by the teacher's emerging mental model of the social robot, their concept of the students' perspective of these interactions, and a shift towards informal classroom-like interaction paradigms, thus transforming their view of what they can achieve with homework. We discuss how these findings demonstrate how the context of the activity can influence initial mental models of social activities and suggest practical guidance on designing authoring tools to best facilitate the creation of computer or robot supported social activities, such as homework. 

Mechanical properties of polymers with polymer grafted nanoparticles. Role of nanoparticle morphology is studied critically