It is difficult to effectively distinguish the key information of non-stationary dynamic signals in many engineering applications, such as fault detection, geological exploration, and logistics transportation. To deal with this problem, a classification and recognition algorithm based on variational mode decomposition (VMD) and the Support Vector Machine (SVM) optimized by the Whale Optimization Algorithm (WOA) optimization model is first proposed in this study. The algorithm first applies VMD to decompose the non-stationary time-domain signals into multiple variational intrinsic mode functions (VIMFs). Then, it calculates the correlation coefficient between each mode and the original signals and conducts signal reconstruction by sorting the VIMFs. On the base of this, it performs modal filtering on the non-stationary signals according to the correlation coefficients between the reconstructed signal and the original signal. Subsequently, the WOA is used to optimize two key parameters of the SVM. Finally, the optimization model is exploited to classify and recognize the impact and vibration of non-stationary signals. A series of simulations and experiments for the algorithm is carried out and analyzed deeply. The comparative test results indicate that the classification and recognition method for non-stationary signals based on VMD and WOA-SVM (VMD-WOA-SVM) proposed in this paper converges faster and recognizes the key information of non-stationary dynamic signals more accurately with a recognition precision of 96.66%.

During the measurement of dynamic transient signals, a high sampling frequency brings great challenges to the analog-to-digital converter (ADC) and testing system. To address these issues, a high precision measurement method for dynamic transient signals is first proposed in this paper. The characteristics of dynamic transient signals are analyzed first. On the basis of this, a random sampling method combining compressed sensing (CS) with spline polynomial interpolation (SPI) is put forward. The fusion of the two algorithms can effectively reduce the quantity of sampling and observation points to reduce the requirement of the ADC and testing system for transient signal measurement and to improve the observation efficiency of the existing uniform sampling. Finally, a Machete hammer test platform for dynamic transient signals is established. A series of simulation and experimental results validate that the error of data reconstruction using the random sampling method combining CS with SPI is not greater than 5.1%.

Mutation signature of aristolochic acid (AA) found in urothelial or hepatocellular carcinoma causes public concern about the cancer risk of AA. In contrast, direct evidence based on the reliable bioanalytical method for the exposure of AA is still lacking and not universal. Here, we strictly complied with the qualitative and quantitative guidance for forensic toxicological analysis: In the sample preprocessing, DNA from formalin-fixed and paraffin-embedded (FFPE) tissues was digested to single nucleotide by a series of enzymes with 70% enzymatic digestion efficiency. After protein precipitation, the samples were submitted to an ABI6500+ mass spectrometer for LC-MS/MS analysis. Ion pairs 543.2/427.2 and 543.2/395.2 of dA-AAI were selected from 5 ion pairs due to their higher LC-MS/MS response. Both these ion pairs have excellent selectivity and specificity in rat liver DNA matrix, and a linear regression range from 5 pg/mL to 200 pg/mL with the best fit and determination coefficient (r) greater than 0.99. The intra and inter batch accuracy and precision of these two ion pairs are also acceptable with less than 15% variation. The total recovery for ion pair 543.2/427.2 and 543.2/395.2 of dA-AAI was 90.06% and 90.76%, respectively. Our method has a minor matrix effect and good stability under different temperature and time conditions. With signal to noise ratio ≥ 3, 2 ion pairs (< 50 % relative abundance variation), the lower limit of quantification (LLOQ) of our method is set to 5 pg/mL(~3.6 AAI-DNA adducts per 108 DNA bases). By using this validated bioanalytical method of dA-AAI, 107 human HCC FFPE tissues were analyzed, the total ratio of samples with peak543.2/427.2 is 15.9% (17/107), with peak543.2/395.2 is 9.34% (10/107) which yields the total ratio of samples combined peak543.2/427.2 and peak543.2/395.2 is 7.48% (8/107). Only one sample is higher than 5 pg/mL (6.89 pg/mL) under the qualitative requirements. In conclusion, we first reported a fully validated methods to analyze the DNA adducts level of aristolochic acid, which could be qualitatively and quantitatively applied to the investigation of AA exposure in the human and other species.