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Abstract Routine high strain rate impacts from the surrounding environment can cause surface erosion, abrasion, and even catastrophic failure to many structural materials. It is thus highly desirable to develop lightweight, thin, and tough impact resistant coatings. Here, inspired by the structurally robust impact surface of the dactyl club of the peacock mantis shrimp, a silicon carbide and chitosan nanocomposite coating is developed to evaluate its impact resistance as a function of particle loading. High strain rate impact tests demonstrate that coatings with 50% and 60% SiC have optimal performance with the greatest reduction in penetration depth and damage area to the substrate. Post‐impact analysis confirms that these concentrations achieve a balance between stiffness and matrix phase continuity, efficiently dissipating impact energy while maintaining coating integrity. The addition of SiC particles helps dissipate impact energy via interphase effects, particle percolation, and frictional losses due to particle jamming. The formation of these stress paths is also modeled to better understand how the addition of particles improves coating stiffness and the stress distribution as a function of particle loading. These findings highlight the potential of bioinspired materials and their promise to promote innovation and breakthroughs in the development of resilient multifunctional materials. 

Network resource reservation systems are being developed and deployed, driven by the demand and substantial benefits of providing performance predictability for modern distributed applications. However, existing systems suffer limitations: They either are inefficient in finding the optimal resource reservation, or cause private information (e.g., from the network infrastructure) to be exposed (e.g., to the user). In this paper, we design BoxOpt, a novel system that leverages efficient oracle construction techniques in optimization and learning theory to automatically, and swiftly learn the optimal resource reservations without exchanging any private information between the network and the user. We implement a prototype of BoxOpt and demonstrate its efficiency and efficacy via extensive experiments using real network topology and trace. Results show that (1) BoxOpt has a 100% correctness ratio, and (2) for 95% of requests, BoxOpt learns the optimal resource reservation within 13 seconds.