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Serum complement C1q is an independent risk factor for acute outbreak of ischemic stroke, whose level is closely related to the outbreak and infarct size and neurological function impairment.

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A new breed of nanocomposite-based spray-on sensor is developed for in-situ active structural health monitoring (SHM). The novel nanocomposite sensor is rigorously designed with graphene as the nanofiller and polyvinylpyrrolidone (PVP) as the matrix, fabricated using a simple spray deposition process. Electrical analysis, as well as morphological characterization of the spray-on sensor, was conducted to investigate percolation characteristic, in which the optimal threshold (~0.91%) of the graphene/PVP sensor was determined. Owing to the uniform and stable conductive network formed by well-dispersed graphene nanosheets in the PVP matrix, the tailor-made spray-on sensor exhibited excellent piezoresistive performance. By virtue of the tunneling effect of the conductive network, the sensor was proven to be capable of perceiving signals of guided ultrasonic waves (GUWs) with ultrahigh frequency up to 500 kHz. Lightweight and flexible, the spray-on nanocomposite sensor demonstrated superior sensitivity, high fidelity, and high signal-to-noise ratio under dynamic strain with ultralow magnitude (of the order of micro-strain) that is comparable with commercial lead zirconate titanate (PZT) wafers. The sensors were further networked to perform damage characterization, and the results indicate significant application potential of the spray-on nanocomposite-based sensor for in-situ active GUW-based SHM.

SummaryConventional simultaneous localization and mapping (SLAM) has concentrated on two-dimensional (2D) map building. To adapt it to urgent search and rescue (SAR) environments, it is necessary to combine the fast and simple global 2D SLAM and three-dimensional (3D) objects of interest (OOIs) local sub-maps. The main novelty of the present work is a method for 3D OOI reconstruction based on a 2D map, thereby retaining the fast performances of the latter. A theory is established that is adapted to a SAR environment, including the object identification, exploration area coverage (AC), and loop closure detection of revisited spots. Proposed for the first is image optical flow calculation with a 2D/3D fusion method and RGB-D (red, green, blue + depth) transformation based on Joblove–Greenberg mathematics and OpenCV processing. The mathematical theories of optical flow calculation and wavelet transformation are used for the first time to solve the robotic SAR SLAM problem. The present contributions indicate two aspects: (i) mobile robots depend on planar distance estimation to build 2D maps quickly and to provide SAR exploration AC; (ii) 3D OOIs are reconstructed using the proposed innovative methods of RGB-D iterative closest points (RGB-ICPs) and 2D/3D principle of wavelet transformation. Different mobile robots are used to conduct indoor and outdoor SAR SLAM. Both the SLAM and the SAR OOIs detection are implemented by simulations and ground-truth experiments, which provide strong evidence for the proposed 2D/3D reconstruction SAR SLAM approaches adapted to post-disaster environments.

An environment can be searched far more efficiently if the appropriate search strategy is used. Because of the limited individual abilities of swarm robots, namely, local sensing and low processing power, random searching is the main search strategy used in swarm robotics. The random walk methods that are used most commonly are Brownian motion and Lévy flight, both of which mimic the self-organized behavior of social insects. However, both methods are somewhat limited when applied to swarm robotics, where having the robots search repeatedly can result in highly inefficient searching. Therefore, by analyzing the characteristics of swarm robotic exploration, this paper proposes an improved random walk method in which each robot adjusts its step size adaptively to reduce the number of repeated searches by estimating the density of robots in the environment. Simulation experiments and experiments with actual robots are conducted to study the effectiveness of the proposed method and evaluate its performance in an exploration mission. The experimental results presented in this paper show that an area is covered more efficiently using the proposed method than it is using either Brownian motion or Lévy flight.

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