A Modified Principal Component Analysis Method for Honeycomb Sandwich Panel Debonding Recognition Based on Distributed Optical Fiber Sensing Signals

The safety and integrity requirements of aerospace composite structures necessitate real-time health monitoring throughout their service life.To this end,distributed optical fiber sensors utilizing back Rayleigh scattering have been extensively deployed in structural health monitoring due to their advantages,such as lightweight and ease of embedding.However,identifying the precise location of damage from the optical fiber signals remains a critical challenge.In this paper,a novel approach which namely Modified Sliding Window Principal Component Analysis(MSWPCA)was proposed to facilitate automatic damage identification and localization via distributed optical fiber sensors.The proposed method is able to extract signal characteristics interfered by measurement noise to improve the accuracy of damage detection.Specifically,we applied the MSWPCA method to monitor and analyze the debonding propagation process in honeycomb sandwich panel structures.Our findings demonstrate that the training model exhibits high precision in detecting the location and size of honeycomb debonding,thereby facilitating reliable and efficient online assessment of the structural health state.

GreenPhos,a universal method for in-depth measurement of plant phosphoproteomes with high quantitative reproducibility

Protein phosphorylation regulates a variety of important cellular and physiological processes in plants.In-depth profiling of plant phosphoproteomes has been more technically challenging than that of animal phosphoproteomes.This is largely due to the need to improve protein extraction efficiency from plant cells,which have a dense cell wall,and to minimize sample loss resulting from the stringent sample clean-up steps required for the removal of a large amount of biomolecules interfering with phosphopeptide purification and mass spectrometry analysis.To this end,we developed a method with a streamlined workflow for highly efficient purification of phosphopeptides from tissues of various green organisms including Arabidopsis,rice,tomato,and Chlamydomonas reinhardtii,enabling in-depth identification with high quantitative reproducibility of about 11000 phosphosites,the greatest depth achieved so far with single liquid chromatography-mass spectrometry(LC-MS)runs operated in a data-dependent acquisition(DDA)mode.The mainstay features of the method are the minimal sample loss achieved through elimination of sample clean-up before protease digestion and of desalting before phosphopeptide enrichment and hence the dramatic increases of time-and cost-effectiveness.The method,named GreenPhos,combined with single-shot LC-MS,enabled in-depth quantitative identification of Arabidopsis phosphoproteins,including differentially phosphorylated spliceosomal proteins,at multiple time points during salt stress and a number of kinase substrate motifs.GreenPhos is expected to serve as a universal method for purification of plant phosphopeptides,which,if samples are further fractionated and analyzed by multiple LC-MS runs,could enable measurement of plant phosphoproteomes with an unprecedented depth using a given mass spectrometry technology.