Poly(vinyl alcohol) (P)/alginate (A)/MMT (M) (PAM) composite aerogels was modified through interpenetrating cross-linking of methyltriethoxysilane (Ms) or γ-aminopropyltriethoxysilane (K) and calcium ion (Ca2+) as a cross-linking agent, respectively. The compressive moduli of the cross-linked PAM/MsCa and PAM/KCa aerogels greatly increased to 17.4 and 22.1 MPa, approximately 10.5- and 8.2-fold of that of PAM aerogel, respectively. The limited oxygen index (LOI) values for PAM/MsCa and PAM/KCa composite aerogels increased from 27.0% of PAM aerogel to 40.5% and 56.8%. Compared with non-cross-linked PAM aerogel, the peak heat release rate (PHRR) of PAM/MsCa and PAM/KCa composite aerogels dramatically decreased by 34% and 74%, respectively, whereas the PAM/KCa aerogel presented better flame retardancy and lower smoke toxicity than the PAM/MsCa aerogel because of the release of more inert gases and the barrier action of more compact char layer during the combustion. The highly efficient flame-retardant PAM-based composite aerogels with excellent mechanical properties are promising as a sustainable alternative to traditional petroleum-based foams.

A future high-energy muon-muon collider could greatly extend our quest for new physics, providing cleaner final states than those produced at hadron colliders. Among its possible physics program, an interesting opportunity is provided by dark matter. Although strong astrophysical evidence indicates the existence of dark matter, there is no evidence yet for its non-gravitational interactions with standard model particles. If present, these interactions can be studied at colliders and in particular at the future muon-muon collider. In this whitepaper, we present a study for heavy weakly interacting massive particle dark matter particles that are part of a new electroweak multiplet and have a high mass. In particular, we report on prospects for dark matter discovery both mono-photon and mono-Z processes.

At present, most of the causal consistency models that rely on cloud storage have problems such as high operation delays and large metadata overhead. To solve these problems, this paper proposes a causal consistency model for edge storage based on hash rings, CCESHP. The proposed model uses two hashes to map the keys and servers on the hash ring for grouping and stores a subset of the complete data set in a replica node located at the edge of the network, thereby realizing a partial geographic replication strategy in the edge storage environment. Operation latency will be reduced since the edge replica is closer to the client. At the same time, it also generates and maintains a combined timestamp to capture causality according to the update type, which can keep the amount of managed metadata in a relatively stable and low state, reduce the overhead of system management metadata, and improve system throughput. The experimental evaluation results under different workloads show that the model has better performance in throughput and operation delay when compared with the existing causal consistency model.

At present, most causal consistency models based on cloud storage can no longer meet the needs of delay-sensitive applications. Moreover, the overhead of data synchronization between replicas is too high. This paper proposes a causal consistency model of edge-cloud collaborative based on grouping protocol. The model based on the edge-cloud collaboration architecture, partitions cloud data centers and groups edge nodes by distributed hash tables, and stores a subset of the complete data set in nodes located at the edge of the network. Thereby realize partial geo-replication in edge-cloud collaboration environment. At the same time, we design a group synchronization algorithm called Imp_Paxos, so that the update only needs to be synchronized to the main group, which reduces the visibility delay of the update and decreases the data synchronization overhead. Besides, a sort timestamp is proposed in this paper, which generates different timestamps according to the type of update to track causality, keeping the amount of metadata managed in a relatively stable and low state.Threrfore, the proposed model reduces the overhead of metadata for system management, and improves throughput quantity of system. Experiments show that, our model performs well in terms of throughput, operation latency, and update visibility latency compared with existing causal consistency models.

No abstract