design framework
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STEAM education enables the cross-curricular study of subjects based on their naturally occurring relationships through holistic and integrated methods. Narratives are enablers of STEAM learning environments, something that is evident in the exploration of narrative learning from pre-recorded history until present. Narrative Digital Game-Based Learning (DGBL) use narratives to drive the game. The extended Ludo Narrative Variable Model (the Variable Model) is a narratological model for categorization of narrative DGBL. Empirical evidence from categorizing narrative DGBL on the Variable Model shows that there is a particular set of categories that incur positive effects on engagement, motivation, and learning. This article introduces the eLuna co-design framework that builds on these categories and empowers educators to participate alongside game developers in multidisciplinary design and development of narrative DGBL. eLuna comprises 1) a four-phase co-design method, and 2) a visual language to support the co-design and co-specification of the game to a blueprint that can be implement by game developers. Idun’s Apples, a narrative DGBL co-designed, co-specified, and implemented into a prototype using eLuna, is presented to illustrate the use of the method and visual language. Arguing that narrative DGBL are vessels for STEAM learning, seven eLuna co-designed games are examined to illustrate that they support STEAM. The article concludes that narrative DGBL co-designed using the eLuna framework provide high opportunity and potential for supporting STEAM, providing educators and game developers with a STEAM co-design framework that enforces positive effects on engagement, motivation, and learning.

Abstract Tire tread patterns have played an important role in the automotive industry because they directly affect automobile performances. The conventional tread pattern development process has successfully produced and manufactured many tire tread patterns. However, a conceptual design process, which is a major part of the whole process, is still time-consuming due to repetitive manual interaction works between designers and engineers. In the worst case, the whole design process must be performed again from the beginning to obtain the required results. In this study, a deep generative tread pattern design framework is proposed to automatically generate various tread patterns satisfying the target tire performances in the conceptual design process. The main concept of the proposed method is that desired tread patterns are obtained through optimization based on integrated functions, which combine generative models and tire performance evaluation functions. To strengthen the effectiveness of the proposed framework, suitable image pre-processing, generative adversarial networks (GANs), 2D image-based tire performance evaluation functions, design generation, design exploration, and image post-processing methods are proposed with the help of domain knowledge of the tread pattern. The numerical results show that the proposed automatic design framework successfully creates various tread patterns satisfying the target tire performances such as summer, winter, or all-season patterns.