Silk protein is being increasingly introduced as a prospective material for biomedical devices. However, a limited locus to intervene in nature-oriented silk protein makes it challenging to implement on-demand functions to silk. Here, we report how polymorphic transitions are related with molecular structures of artificially synthesized silk protein and design principles to construct a green-lithographic and high-performative protein resist. The repetition number and ratio of two major building blocks in synthesized silk protein are essential to determine the size and content of β-sheet crystallites, and radicals resulting from tyrosine cleavages by the 193 nm laser irradiation induce the β-sheet to α-helix transition. Synthesized silk is designed to exclusively comprise homogeneous building blocks and exhibit high crystallization and tyrosine-richness, thus constituting an excellent basis for developing a high-performance deep-UV photoresist. Additionally, our findings can be conjugated to design an electron-beam resist governed by the different irradiation−protein interaction mechanisms. All synthesis and lithography processes are fully water-based, promising green lithography. Using the engineered silk, a nanopatterned planar color filter showing the reduced angle dependence can be obtained. Our study provides insights into the industrial scale production of silk protein with ondemand functions.
Recently, along with interest in autonomous vehicles, the importance of monitoring systems for both drivers and passengers inside vehicles has been increasing. This paper proposes a novel in-vehicle monitoring system the combines 3D pose estimation, seat-belt segmentation, and seat-belt status classification networks. Our system outputs various information necessary for monitoring by accurately considering the data characteristics of the in-vehicle environment. Specifically, the proposed 3D pose estimation directly estimates the absolute coordinates of keypoints for a driver and passengers, and the proposed seat-belt segmentation is implemented by applying a structure based on the feature pyramid. In addition, we propose a classification task to distinguish between normal and abnormal states of wearing a seat belt using results that combine 3D pose estimation with seat-belt segmentation. These tasks can be learned simultaneously and operate in real-time. Our method was evaluated on a private dataset we newly created and annotated. The experimental results show that our method has significantly high performance that can be applied directly to real in-vehicle monitoring systems.
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