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The monitoring of data streams with a network structure have drawn increasing attention due to its wide applications in modern process control. In these applications, high-dimensional sensor nodes are interconnected with an underlying network topology. In such a case, abnormalities occurring to any node may propagate dynamically across the network and cause changes of other nodes over time. Furthermore, high dimensionality of such data significantly increased the cost of resources for data transmission and computation, such that only partial observations can be transmitted or processed in practice. Overall, how to quickly detect abnormalities in such large networks with resource constraints remains a challenge, especially due to the sampling uncertainty under the dynamic anomaly occurrences and network-based patterns. In this paper, we incorporate network structure information into the monitoring and adaptive sampling methodologies for quick anomaly detection in large networks where only partial observations are available. We develop a general monitoring and adaptive sampling method and further extend it to the case with memory constraints, both of which exploit network distance and centrality information for better process monitoring and identification of abnormalities. Theoretical investigations of the proposed methods demonstrate their sampling efficiency on balancing between exploration and exploitation, as well asmore »the detection performance guarantee. Numerical simulations and a case study on power network have demonstrated the superiority of the proposed methods in detecting various types of shifts. Note to Practitioners —Continuous monitoring of networks for anomalous events is critical for a large number of applications involving power networks, computer networks, epidemiological surveillance, social networks, etc. This paper aims at addressing the challenges in monitoring large networks in cases where monitoring resources are limited such that only a subset of nodes in the network is observable. Specifically, we integrate network structure information of nodes for constructing sequential detection methods via effective data augmentation, and for designing adaptive sampling algorithms to observe suspicious nodes that are likely to be abnormal. Then, the method is further generalized to the case that the memory of the computation is also constrained due to the network size. The developed method is greatly beneficial and effective for various anomaly patterns, especially when the initial anomaly randomly occurs to nodes in the network. The proposed methods are demonstrated to be capable of quickly detecting changes in the network and dynamically changes the sampling priority based on online observations in various cases, as shown in the theoretical investigation, simulations and case studies.« less

Combining experimental and computational studies of nanocomposite interfaces is highly needed to gain insight into their performance. However, there are very few literature reports, combining well-controlled atomic force microscopy experiments with molecular dynamic simulations, which explore the role of polymer chemistry and assembly on interface adhesion and shear strength. In this work, we investigate graphene oxide (GO)-polymer interfaces prevalent in nanocomposites based on a nacre-like architectures. We examine the interfacial strength resulting from van der Waals and hydrogen bonding interactions by comparing the out-of-plane separation and in-plane shear deformations of GO-polyethylene glycol (PEG) and GO-polyvinyl alcohol (PVA). The investigation reveals an overall better mechanical performance for the anhydrous GO-PVA system in both out-of-plane and in-plane deformation modes, highlighting the benefits of the donor-acceptor hydrogen bond formation present in GO-PVA. Such bond formation results in interchain hydrogen bond networks leading to stronger interfaces. By contrast, PEG, a hydrogen bond acceptor only, relies primarily on van der Waals inter-chain interactions, typically resulting in weaker interactions. The study also predicts that water addition increases the adhesion of GOPEG but decreases the adhesion of GO-PVA, and slightly increases the shear strength in both systems. Furthermore, by comparing simulations and experiments, we show that themore »CHARMM force field has enough accuracy to capture the effect of polymer content, water distribution, and to provide quantitative guidance for achieving optimum interfacial properties. Therefore, the study demonstrates an effective methodology, in the Materials Genome spirit, toward the design of 2D materials-polymer nanocomposites system for applications demanding mechanical robustness.« less

The futureRicochetexperiment aims at searching for new physics in the electroweak sector by providing a high precision measurement of the Coherent Elastic Neutrino-Nucleus Scattering (CENNS) process down to the sub-100 eV nuclear recoil energy range. The experiment will deploy a kg-scale low-energy-threshold detector array combining Ge and Zn target crystals 8.8 m away from the 58 MW research nuclear reactor core of the Institut Laue Langevin (ILL) in Grenoble, France. Currently, theRicochetCollaboration is characterizing the backgrounds at its future experimental site in order to optimize the experiment’s shielding design. The most threatening background component, which cannot be actively rejected by particle identification, consists of keV-scale neutron-induced nuclear recoils. These initial fast neutrons are generated by the reactor core and surrounding experiments (reactogenics), and by the cosmic rays producing primary neutrons and muon-induced neutrons in the surrounding materials. In this paper, we present theRicochetneutron background characterization using$$^3$$${}^3$He proportional counters which exhibit a high sensitivity to thermal, epithermal and fast neutrons. We compare these measurements to theRicochetGeant4 simulations to validate our reactogenic and cosmogenic neutron background estimations. Eventually, we present our estimated neutron background for the futureRicochetexperiment and the resulting CENNS detection significance. Our results show that depending on the effectiveness ofmore »the muon veto, we expect a total nuclear recoil background rate between 44 ± 3 and 9 ± 2 events/day/kg in the CENNS region of interest, i.e. between 50 eV and 1 keV. We therefore found that theRicochetexperiment should reach a statistical significance of 4.6 to 13.6 $$\sigma $$$\sigma$for the detection of CENNS after one reactor cycle, when only the limiting neutron background is considered.

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