Simultaneous 3D printing of disparate materials; metals, polymers and semiconductors with device quality interfaces and at high resolution remains challenging. Moreover, the precise placement of discrete and continuous domains to enable both device performance and electrical connectivity poses barriers to current high-speed 3D-printing approaches. Here, we report filaments with disparate materials arranged in elaborate microstructures, combined with an external adhesion promoter, to enable a wide range of topological outcomes and device-quality interfaces in 3D printed media. Filaments, structured towards light-detection, are printed into fully-connected 3D serpentine and spherical sensors capable of spatially resolving light at micron resolution across its entire centimeter-scale surface. 0-dimensional metallic microspheres generate light-emitting filaments that are printed into hierarchical 3D objects dotted with electroluminescent pixels at high device resolution of 55 µm not restricted by surface tension effects. Structured multimaterial filaments provides a path towards custom three-dimensional functional devices not realizable by existing approaches.

The goal of the SunPy project is to facilitate and promote the use and development of community-led, free, and open source data analysis software for solar physics based on the scientific Python environment. The project achieves this goal by developing and maintaining the sunpy core package and supporting an ecosystem of affiliated packages. This paper describes the first official stable release (version 1.0) of the core package, as well as the project organization and infrastructure. This paper concludes with a discussion of the future of the SunPy project.

Significance Many bad decisions and their devastating consequences could be avoided if people used optimal decision strategies. Here, we introduce a principled computational approach to improving human decision making. The basic idea is to give people feedback on how they reach their decisions. We develop a method that leverages artificial intelligence to generate this feedback in such a way that people quickly discover the best possible decision strategies. Our empirical findings suggest that a principled computational approach leads to improvements in decision-making competence that transfer to more difficult decisions in more complex environments. In the long run, this line of work might lead to apps that teach people clever strategies for decision making, reasoning, goal setting, planning, and goal achievement.

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