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To reduce the severe health risk and the huge economic impact associated with the fomite transmission of SARS-CoV-2, an imidazolium-based zwitterionic polymer was designed, synthesized, and demonstrated to achieve contact deactivation of a human coronavirus under dry ambient conditions that resemble fomite transmission. The zwitterionic polymer further demonstrated excellent antifouling properties, reducing the adhesion of coronavirus and the formation of bacteria biofilms under wetted conditions. The polymer was synthesized using a substrate-independent and solvent-free process, leveraging an all-dry technique named initiated chemical vapor deposition (iCVD). The broad applicability of this approach was demonstrated by applying the polymer to a range of substrates that are curved and/or with high-aspect-ratio nano/microporous structures, which remained intact after the coating process. The zwitterionic polymer and the synthesis approach reported here present an effective solution to mitigate viral transmission without the need for manual disinfection, reducing the health and economic impact of the ongoing pandemic. 

The key to optimizing the performance of an anycast-based sys- tem (e.g., the root DNS or a CDN) is choosing the right set of sites to announce the anycast prefix. One challenge here is predicting catchments. A naïve approach is to advertise the prefix from all subsets of available sites and choose the best-performing subset, but this does not scale well. We demonstrate that by conducting pairwise experiments between sites peering with tier-1 networks, we can predict the catchments that would result if we announce to any subset of the sites. We prove that our method is effective in a simplified model of BGP, consistent with common BGP routing policies, and evaluate it in a real-world testbed. We then present AnyOpt, a system that predicts anycast catchments. Using AnyOpt, a network operator can find a subset of anycast sites that minimizes client latency without using the naïve approach. In an experiment using 15 sites, each peering with one of six transit providers, AnyOpt predicted site catchments of 15,300 clients with 94.7% accuracy and client RTTs with a mean error of 4.6%. AnyOpt identified a subset of 12 sites, announcing to which lowers the mean RTT to clients by 33ms compared to a greedy approach that enables the same number of sites with the lowest average unicast latency.