Purpose
The purpose of this study is the generation of a thorough generic heart model optimized for direct 3D printing with silicone elastomers.
Design/methodology/approach
The base of the model design is segmentation of CT data, followed by a generic adaption and a constructive enhancement. The model is 3D printed with silicone. An evaluation of the physical model gives indications about its benefits and weaknesses.
Findings
The results show the feasibility of a generic design while maintaining anatomical correctness and the benefit of the generic approach to quickly derive a multiplicity of healthy and pathological versions from one single model. The material properties of the silicone model are sufficient for simulation, but the results of the evaluation indicate possible improvements, as for most anatomical features, the used silicone is too hard and too stretchable.
Originality/value
Previous developments mostly focus on patient-specific heart models. In contrast, this study sets out to explore the possibility and benefits of a generic approach. Standardized validated models would allow comparability in surgical simulation.
Several systems have illustrated the concept of interactive fabrication, i.e. rather than working through a digital editor, users make edits directly on the physical workpiece. However, so far the interaction has been limited to turn-taking, i.e., users first perform a command and then the system responds with physical feedback. In this paper, we present a first step towards interactive fabrication that changes the workpiece continuously while the user is manipulating it. To achieve this, our system FormFab does not add or subtract material but instead reshapes it (formative fabrication). A heat gun attached to a robotic arm warms up a thermoplastic sheet until it becomes compliant; users then control a pneumatic system that applies either pressure or vacuum thereby pushing the material outwards or pulling it inwards. Since FormFab reshapes the workpiece continuously while users are moving their hands, users can interactively explore different sizes of a shape with a single interaction.
The adoption and acceptance of protective measures are crucial for containing the ongoing coronavirus disease (COVID-19) pandemic. In a recent article in this journal, Dohle et al. (https://doi.org/10.32872/spb.4315) investigated the influence of risk perceptions and trust in politics and science on those constructs in March/April 2020. Since then, the pandemic has undergone several dynamic changes. We analyzed longitudinal data (N = 800) to investigate whether trust and risk were relevant predictors for pandemic measures at a later stage (October 2020/January 2021). The concept of risk perception was supplemented by risk attitude and affective risk to produce a more comprehensive picture of the risk component. We found that greater trust in science at time point 1 predicted less rejection and more adoption of measures at time point 2. Moreover, trust in politics predicted less rejection of measures. From all aspects of cognitive risk perception, only higher severity predicted lower rejection. All other cognitive aspects were non-significant. However, affective risk was shown to be a major predictor: the more the coronavirus was perceived as frightening and worrisome, the lower the rejection and more frequent the adoption of measures. Also, the higher the risk attitude related to health topics, the less frequent the implementation of measures. We replicated the analysis with predictors from time point 2 and deviations are discussed. Our results indicate that affective risk and general attitude toward health risk are more predictive of taking up measures in the context of COVID-19 than cognitive risk.
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