This means that, once wounds have formed, the soft tissues face direct threats of pathogens during the long reconstruction process, [2] and risks of infection are particularly elevated in chronic wounds, such as burns and diabetic ulcers. [3] Although traditional antibiotic therapy has been the mainstream clinical method for preventing infection for several decades, the rapid development of bacterial drug resistance has demonstrated its limitation. [4] It should also be noted that bacterial infection, rather than the virus itself, has become the leading cause of COVID-19-related death, [5] and more challenges to the healing of infected wounds should be expected in the struggle against coronavirus. [6a] This highlights the significance and urgency of finding nonantibiotic therapies for avoiding drug resistance. [6b] In recent years, inspired by nature and life, physical effects have been increasingly involved in constructing efficient sterilization methods. For example, biomimetic surfaces based on the microstructures of plants or insect wings can destroy bacterial membranes through mechanical forces, [7a,b] and light has been introduced and combined with photothermal conversion materials (e.g., graphene oxide (GO), MoS 2 ) to denature the proteins on

Social sensing helps realize the real-time prediction of bus routes by perceiving social events and evaluating their influence on the time when a bus passes through a road section. This paper proposes a social sensing enhanced real-time bus service that integrates sensing ability and social networks to better understand and measure social events that influence vehicle velocity. The establishment of the real-time service revolves around the PT service quality attributions PEAs and the road condition attributions PRCAs. The processes that collect bus relevant events and further categorize them into PEA events or PRCA events were proposed respectively. A method of scoring PEAs for travelers according to social events was discussed. An artificial neural network based prediction model was proposed to estimate the bus arrival time by analyzing PRCA events.

In-memory databases (IMDBs) have been the backbone of modern systems that demand high throughput and low latency. Because of the cost and volatility of DRAM, IMDBs become incompetent when dealing with workloads that require large data volume and strict durability. The emergence of non-volatile memory (NVM) brings new opportunities for IMDBs to tackle this situation. However, it is non-trivial to build an NVM-based IMDB, due to performance degradation, NVM programming complexity, and other challenges. In this paper, we present Tair-PMem , an NVM-based enterprise-strength database atop Redis, the most popular IMDB. Tair-PMem adopts a well-controlled data layout and a log-as-user-data design to mitigate NVM overheads. It eases the NVM programming complexity by providing a hybrid memory programming toolkit. To better leverage the enterprise-strength features and implementations from Redis, Tair-PMem retrofits it in a less intrusive way to achieve full compatibility and stability, while retaining its advanced features. With all of the above techniques elaborately implemented, Tair-PMem satisfies full durability, high throughput, and low latency at the same time. Tair-PMem has now been publicly available as a cloud service on Alibaba Cloud. To the best of our knowledge, Tair-PMem is the first cloud service that makes good use of the persistence capability of NVM.

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