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In order to investigate the analysis and processing methods for nonstationary signals generated in bridge health monitoring systems, this study combines the advantages of complete ensemble empirical mode decomposition (CEEMD) and wavelet threshold denoising algorithms to construct the CEEMD–wavelet threshold denoising algorithm. The algorithm follows the following steps: first, add noise to the monitoring data and obtain all the mode components through empirical mode decomposition (EMD), denoise the mode components with noise using the wavelet threshold function to remove the noise components, select the optimal stratification for denoising the monitoring data of the Guozigou Bridge in Xinjiang in January 2023, determine the wavelet type and threshold selection criteria, and reconstruct the denoised intrinsic mode function (IMF) components to achieve accurate extraction of the effective signal. By referencing the deflection, temperature, and strain data of the Guozigou Bridge in Xinjiang in January 2023 and comparing the data cleaned by different mode decomposition and wavelet threshold denoising methods, the results show that compared with empirical mode decomposition (EMD)–wavelet threshold denoising and variational mode decomposition (VMD)–wavelet threshold denoising, the signal-to-noise ratios and root-mean-square errors of the four types of monitoring data obtained by the algorithm proposed in this study are the most ideal. Under the premise of minimizing reconstruction errors when processing a large amount of data, it has better convergence, verifying the practicality and reliability of the algorithm in the field of bridge health monitoring data cleaning and providing a certain reference value for further research in the field of signal processing. The computational method constructed in this study will provide theoretical support for data cleaning and analysis of nonstationary and nonlinear random signals, which is conducive to further promoting the improvement of bridge health monitoring systems. [ABSTRACT FROM AUTHOR]

Vehicle force identification is one of the core technical problems to be solved urgently in the management of transportation infrastructure, and it has also been a research hotspot in recent years. To promote the application of vehicle force identification technology on bridges and explore its development direction, the development status of indirect vehicle force identification methods based on bridge response is reviewed during this study. The basic theories of two major methods, including bridge weigh-in-motion (BWIM) and moving force identification (MFI), are described in detail in this study, and then, the key technical principles of bridge force identification are revealed. Secondly, the development status of BWIM in recent years is reviewed from three aspects, including test accuracy, applicability and test efficiency. Combined with a variety of theories, the current status of MFI is analyzed from the establishment of the solution to the equation. Finally, the development direction of an artificial neural network and machine vision technology are prospected in this study. The BP neural network has good self-learning ability and self-adaptive ability, but the algorithm needs to be improved. The identification method based on machine vision represents the current development direction in vehicle force identification, with great potential. [ABSTRACT FROM AUTHOR]

Organic thioethers play an important role in the discovery of drugs and natural products. However, the green synthesis of organic sulfide compounds remains a challenging task. The convenient and efficient synthesis of 5-alkoxy-3-halo-4-methylthio-2(5H)-furanones from DMSO is performed via the mediation of 1,3-dibromo-5,5-dimethylhydantoin (DBDMH), affording a facile route for the sulfur-functionalization of 3,4-dihalo-2(5H)-furanones under transition metal-free conditions. This new approach has demonstrated the functionalization of non-aromatic Csp2-X-type halides with unique structures containing C-X, C-O, C=O and C=C bonds. Compared with traditional synthesis methods using transition metal catalysts with ligands, this reaction has many advantages, such as the lower temperature, the shorter reaction time, the wide substrate range and good functional group tolerance. Notably, DMSO plays multiple roles, and is simultaneously used as an odorless methylthiolating reagent and safe solvent. [ABSTRACT FROM AUTHOR]