The key to leak detection and location in water supply pipelines is signal denoising and feature extraction. First, in this paper, an improved spline-local mean decomposition (ISLMD) is proposed to eliminate noise interference. Based on the ISLMD decomposition of a signal, the cross-correlation function between the reference signal and the product functions component can be obtained. And then the PF component containing the leak information can be extracted reasonably. Compared with improved local mean decomposition, the ISLMD has higher accuracy in leak location. Second, an image recognition method using a convolutional neural network for leak detection is proposed, which can better address the problem that the features of different leak apertures or locations are highly similar to each other. The images from the conversion of the reconstructed signals are used as the input of the AlexNet model, which is capable of adaptive extraction of leak signal features. The trained AlexNet model can effectively detect different leak apertures. Finally, the signal time-delay between the upstream and downstream pressure transmitters caused by the leak and propagation of negative pressure wave is determined according to generalized crosscorrelation analysis, and thereby, the leak location is obtained. The experimental results show that the proposed method is effective for leak detection and location. INDEX TERMS Local mean decomposition, convolutional neural network, generalized cross-correlation, leak detection and location, fault detection.

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Agricultural production is substantially affected by the variations in global weather patterns, particularly by the El Niño–Southern Oscillation (ENSO). Thus, incorporating the forecast of imminent ENSO phases can enhance the effectiveness of crop insurance and mitigate the adverse impacts of weather on agriculture. Given the probabilistic nature of the ENSO phase forecast, we employ a Bayesian framework to estimate the value of ENSO information on various aspects of crop insurance. Our results indicate potential benefits of ENSO forecast to insurance rate setting and policy selection. At the same time, we caution against overoptimism in this assessment as economic benefits may diminish as the accuracy of ENSO forecast decreases. Simulations and numerical experiments demonstrate the practical usefulness of the proposed method for various stakeholders of the US crop insurance industry. Implications to various crop insurance policies are also discussed.

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Pipelines are one of the most efficient and economical methods of transporting fluids, such as oil, natural gas, and water. However, pipelines are often subject to leakage due to pipe corrosion, pipe aging, pipe weld defects, or damage by a third-party, resulting in huge economic losses and environmental degradation. Therefore, effective pipeline leak detection methods are important research issues to ensure pipeline integrity management and accident prevention. The conventional methods for pipeline leak detection generally need to extract the features of leak signal to establish a leak detection model. However, it is difficult to obtain actual leakage signal data samples in most applications. In addition, the operating modes of pipeline fluid transportation process often have frequent changes, such as regulating valves and pump operation. Aiming at these issues, this paper proposes a hybrid intelligent method that integrates kernel principal component analysis (KPCA) and cascade support vector data description (Cas-SVDD) for pipeline leak detection with multiple operating modes, using data samples that are leak-free during pipeline operation. Firstly, the local mean decomposition method is used to denoise and reconstruct the measured signal to obtain the feature variables. Then, the feature dimension is reduced and the nonlinear principal component is extracted by the KPCA algorithm. Secondly, the K-means clustering algorithm is used to identify multiple operating modes and then obtain multiple support vector data description models to obtain the decision boundaries of the corresponding hyperspheres. Finally, pipeline leak is detected based on the Cas-SVDD method. The experimental results show that the proposed method can effectively detect small leaks and improve leak detection accuracy.

Based on a synergistic digestion method of ultraviolet combined with ozone (UV/O3), this article investigates the reaction characteristics of nitrogen-containing compounds (N-compounds) in water and the influence of ions on digestion efficiency. In this respect, a novel and efficient AOPs-based dual-environmental digestion method for the determination of total dissolved nitrogen (TDN) in waters with complex components is proposed, in the hopes of improving the detection efficiency and accuracy of total nitrogen via online monitoring. The results show that inorganic and organic N-compounds have higher conversion rates in alkaline and acidic conditions, respectively. Meanwhile, the experimental results on the influence of Cl−, CO32−, and HCO3− on the digestion process indicate that Cl− can convert to radical reactive halogen species (RHS) in order to promote digestion efficiency, but CO32− and HCO3− cause a cyclic reaction consuming numerous •OH, weakening the digestion efficiency. Ultimately, to verify the effectiveness of this novel digestion method, total dissolved nitrogen samples containing ammonium chloride, urea, and glycine in different proportions were digested under the optimal conditions: flow rate, 0.6 L/min; reaction temperature, 40 °C; pH in acidic conditions, 2; digestion time in acidic condition, 10 min; pH in alkaline conditions, 11; digestion time in alkaline conditions, 10 min. The conversion rate (CR) of samples varied from 93.23% to 98.64%; the mean CR was greater than 95.30%. This novel and efficient digestion method represents a potential alternative for the digestion of N-compounds in the routine analysis or online monitoring of water quality.

Metal-phenolic networks (MPNs) have been exploited to be a versatile coating film to fabricate core-shell structure due to their general adherent properties. Herein, gold nanocuboid (GNCB) wrapped by MPNs (GNCB at MPNs) are prepared by a facile encapsulation method for surface-enhanced Raman scattering (SERS) analysis. The MPN coating not only reshapes the electric field distribution around the nanostructures but also allows the substrate to adsorb more analytes, both of which contribute to the superior SERS activity of GNCB at MPNs. The SERS signals induced by plasmonic nanostructures increase four- to sixfold after MPN coating, reaching a maximum Raman enhancement factor calculated to be 9.47 × 1 0 8 . Moreover, the core-shell SERS substrate also demonstrates improved biocompatibility (∼fivefold increase) that facilitates the reliable SERS analysis of cancer cells and further diverse biomedical applications.