a b s t r a c tThe two-dimensional thermoelastic sliding frictional contact of functionally graded material (FGM) coated half-plane under the plane strain deformation is investigated in this paper. A rigid punch is sliding over the surface of the FGM coating with a constant velocity. Frictional heating, with its value proportional to contact pressure, friction coefficient and sliding velocity, is generated at the interface between the punch and the FGM coating. The material properties of the coating vary exponentially along the thickness direction. In order to solve the heat conduction equation analytically, the homogeneous multi-layered model is adopted for treating the graded thermal diffusivity coefficient with other thermomechanical properties being kept as the given exponential forms. The transfer matrix method and Fourier integral transform technique are employed to convert the problem into a Cauchy singular integral equation which is then solved numerically to obtain the unknown contact pressure and the in-plane component of the surface stresses. The effects of the gradient index, Peclet number and friction coefficient on the thermoelastic contact characteristics are discussed in detail. Numerical results show that the distribution of the contact stress can be altered and therefore the thermoelastic contact damage can be modified by adjusting the gradient index, Peclet number and friction coefficient.

Event extraction is a particularly challenging information extraction task, which intends to identify and classify event triggers and arguments from raw text. In recent works, when determining event types (trigger classification), most of the works are either pattern-only or feature-only.However, although patterns cannot cover all representations of an event, it is still a very important feature. In addition, when identifying and classifying arguments, previous works consider each candidate argument separately while ignoring the relationship between arguments. This paper proposes a Regularization-Based Pattern Balancing Method (RBPB). Inspired by the progress in representation learning, we use trigger embedding, sentence-level embedding and pattern features together as our features for trigger classification so that the effect of patterns and other useful features can be balanced. In addition, RBPB uses a regularization method to take advantage of the relationship between arguments. Experiments show that we achieve results better than current state-of-art equivalents.

Abstract:Water distribution networks (WDNs) represent a class of critical infrastructure networks. When a disaster occurs, component failures in a WDN may trigger system failures that result in larger-scale reactions. The aim of the paper is to evaluate the evolution of system reliability and failure propagation time for a WDN experiencing cascading failures, and find the critical pipes which may reduce system reliability dramatically. Multiple factors are considered in the method such as network topology, the balance of water supply and demand, demand multiplier, and pipe break isolation. The pipe-based attack with multiple failure scenarios is simulated in the paper. A case WDN is used to illustrate the method. The results show that the lowest capacity gets stronger when a WDN is short of supply, becoming the dominant factor that decides the evolution of system reliability and failure propagation time. The valve ratio (VR) and system reliability present a flattened S curve relationship, and there are two turning points in VR. The critical pipes can be identified. With the fixed 5% valves, a WDN can improve system reliability and resist cascading failures effectively. The findings provide insights into the system reliability and failure propagation time for WDNs experiencing cascading failures. It is proven to be useful in future studies focused on the operation and management of water services.

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Mercury ion (Hg) is one of the most toxic heavy metals that has severe adverse effects on the environment and human organs even at very low concentrations. Therefore, highly sensitive and selective detection of Hg is desirable. Here, we introduce plasmonic micropinball constructed from Au nanooctahedron as a three-dimensional surface-enhanced Raman spectroscopy (SERS) platform, enabling ultrasensitive detection of trace Hg ions. Typically, strong SERS signals could be obtained when the single-stranded DNA structure converts to the hairpin structure in the presence of Hg ions, due to the formation of thymine (T)-Hg-T. As a result, the detection limit of Hg ions is as low as 1 × 10 M, which is far below compared to that reported for conventional analytical strategies. Moreover, to achieve rapid multiple detection, we combine the micropinball sensors with microflow tube online detection. Our platform prevents cross-talk and tube contamination, allowing multiassay analysis, rapid identification, and quantification of different analytes and concentrations across separate phases.