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Three dimensional (3D) printing technology in veterinary anatomy education is an evolving area providing accurately, rapidly, and reproducibly anatomical specimens. In this study, 3D printed sheep brain models were produced using magnetic resonance imaging (MRI) scanning, and their effectiveness was compared with cadaveric materials by creating three groups from undergraduate veterinary students. The study was performed when veterinary anatomy lectures in Erciyes University were carried out via live fully online learning platforms in virtual classes like many other universities in the world due the Covid-19 pandemic. Participants were subjected to an approximately 30 minute online lecture on the external and internal anatomy of the sheep brain using cadaveric materials only (n=21, Group 1), 3D printed models only (n=20, Group 2), or a combination of cadaveric materials and 3D printed models (n=20, Group 3) as teaching aids. Online post-tests carried out following the online lectures showed no statistically significant difference between the scores of the groups. Furthermore, online questionnaires conducted after the post-tests demonstrated that 3D printed models helped students learn about sheep brain anatomy. The finding of this study suggests that 3D printed models can be considered as a supplement teaching resource to cadaveric materials in veterinary anatomy education particularly when students are supposed to learn more in a limited time regardless of whether or not the Covid-19 pandemic might end.

Quality function deployment (QFD), a key tool to convert the customer needs into product features, is generally integrated into the New Product Development (NPD) process at the design stage. Prioritizing customer needs in a QFD process leads to using the resources (time, money, and staffing) effectively by eliminating the unimportant customer needs. The overall goal of the research was to develop a textile-based optical fiber sensor for automotive seat occupancy. The findings of this paper were focused on the design of experiments in our previous publication. In this paper, a research study was conducted to better understand market demands in terms of sensor performance characteristics for automotive seat weight sensors, as a part of the QFD House of Quality (HOQ) analysis. A survey was sent to more than 20 companies operating in the field of automotive seat weight sensors, and Original Equipment Manufacturers (OEM) via e-mail. Only five companies participated in this study due to competitive concerns and confidentiality reasons. However, the companies responded to the survey were of quality relevant to the research and could be perceived as representative of the group of experts. All 5 companies participated in the survey agreed on the first 5 most important sensor characteristics: reproducibility, accuracy, selectivity, aging, and resolution, where The Analytic Hierarchy Process (AHP) was applied to prioritize the sensor characteristics.