ZnO nanoparticles and graphene oxide (GO) thin film were deposited on gold interdigital electrodes (IDEs) in sequence via simple spraying process, which was further restored to ZnO/reduced graphene oxide (rGO) bilayer thin film by the thermal reduction treatment and employed for ammonia (NH3) detection at room temperature. rGO was identified by UV-vis absorption spectra and X-ray photoelectron spectroscope (XPS) analyses, and the adhesion between ZnO nanoparticles and rGO nanosheets might also be formed. The NH3-sensing performances of pure rGO film and ZnO/rGO bilayer films with different sprayed GO amounts were compared. The results showed that ZnO/rGO film sensors exhibited enhanced response properties, and the optimal GO amount of 1.5 ml was achieved. Furthermore, the optimal ZnO/rGO film sensor showed an excellent reversibility and fast response/recovery rate within the detection range of 10–50 ppm. Meanwhile, the sensor also displayed good repeatability and selectivity to NH3. However, the interference of water molecules on the prepared sensor is non-ignorable; some techniques should be researched to eliminate the effect of moisture in the further work. The remarkably enhanced NH3-sensing characteristics were speculated to be attributed to both the supporting role of ZnO nanoparticles film and accumulation heterojunction at the interface between ZnO and rGO. Thus, the proposed ZnO/rGO bilayer thin film sensor might give a promise for high-performance NH3-sensing applications.

This paper presents an open-source, parallel AI environment (named OpenGraphGym) to facilitate the application of reinforcement learning (RL) algorithms to address combinatorial graph optimization problems. This environment incorporates a basic deep reinforcement learning method, and several graph embeddings to capture graph features, it also allows users to rapidly plug in and test new RL algorithms and graph embeddings for graph optimization problems. This new opensource RL framework is targeted at achieving both high performance and high quality of the computed graph solutions. This RL framework forms the foundation of several ongoing research directions, including 1) benchmark works on different RL algorithms and embedding methods for classic graph problems; 2) advanced parallel strategies for extreme-scale graph computations, as well as 3) performance evaluation on real-world graph solutions.

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The k-means clustering method is one of the most widely used techniques in big data analytics. In this paper, we explore the ideas of software blocking, asynchronous local optimizations, and heuristics of simulated annealing to improve the performance of k-means clustering. Like most of the machine learning methods, the performance of k-means clustering relies on two main factors: the computing speed (per iteration), and the convergence rate. A straightforward realization of the software-blocking synchronization-reducing clustering algorithm, however, sees sporadic slower convergence rate than the standard k-means algorithm. To tackle the issues, we design an annealing-enhanced algorithm, which introduces the heuristics of stop conditions and annealing steps to provide as good or better performance than the standard k-means algorithm. This new enhanced k-means clustering algorithm is able to offer the same clustering quality as the standard k-means. Experiments with real-world datasets show that the new parallel implementation is faster than the open source HPC library of Parallel K-Means Data Clustering (e.g., 19% faster on relatively large datasets with 32 CPU cores, and 11% faster on a large dataset with 1,024 CPU cores). Moreover, the extent to which the program performance improves is largely determined by the actual convergence rate of applying the algorithm to different datasets.

Because of the increasing importance and dependencies of infrastructure networks and the potential for massive cascading failures in real-world network systems, maintenance optimization to effectively reduce system performance loss caused by diverse disruptions is of significant interest among researchers and practitioners. In this work, a new recovery framework was developed to rapidly identify important system components for maintenance to improve network resilience against cascading failures. This work provides distinct advantages to determine an optimal maintenance priority by combining real-time network structure importance with other maintenance prioritization based on customer preference. This approach adopts structural graph embedding and deep reinforcement learning to extract real-time network topology information (such as minimum vertex cover) to update the maintenance priority during the recovery process. Based on the case studies on synthetic networks and a US airport network, the proposed recovery framework with real-time network topology awareness shows better performance than other maintenance prioritization strategies regarding resilience enhancement. This work improves the understanding of how the changing network structure influences maintenance effects. It also provides insights of the practical usefulness of advanced deep learning on helping optimal maintenance prioritization to effectively reduce the intensity and extent of cascading failures.