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Estimation of in situ stresses based on back-analysis of measured stress changes and displacements has become an alternative to the direct stress measurement methods. In order to help users conduct own measurement and analysis, this paper presents in detail a field stress back-analysis approach directly from borehole strain changes measured during nearby underground excavation. Essential formulations in major steps and the procedure for the entire analysis process are provided to allow users to follow. The instrument for borehole strain change measurement can be the CSIR or CSIRO stress cells and other borehole strain cells that can measure strains on borehole walls. Strain changes corresponding to the stress changes at a borehole location are calculated in borehole environment. The stress changes due to nearby excavation can be calculated by an analytical model for a single circular opening and simulated by a numerical model for non-circular and multiple openings. These models are based on isotropic, homogeneous and linear elastic assumptions. The analysis of borehole strain changes is accomplished by multiple linear regression based on error minimization and an integrated process provides the best-fit solution directly to the in situ stresses. A statistical technique is adopted for screening outliers in the measurement data, checking measurement compatibility and evaluating the reliability of analysis results. An application example is included to demonstrate the practical application and the analysis procedure.

Aimed at the low accuracy of loudspeaker abnormal sound classification, an abnormal sound classification method based on the combination of adaptive variational mode decomposition (AVMD) and residual convolutional neural network (RCNN) is proposed. In AVMD, the grey wolf optimizer (GWO) algorithm was used to optimize the quadratic penalty factor (α) and modal decomposition number (K) of the variational mode decomposition (VMD), and then the VMD was used to extract the features. Finally, the random forest recursive feature elimination (RF-RFE) algorithm was used to extract the optimal feature data, and residual network (ResNet) was introduced by RCNN-3 to classify abnormal sound based on convolutional neural network (CNN). The experimental results show that the proposed method has better classification accuracy and stability, and its average classification accuracy can reach 99.3%.

Abstract Objectives The objectives of this study were to describe the coronavirus disease caused by SARS-CoV-2 (COVID-19) reinfection evaluation algorithm used in the early phase of the pandemic in Singapore and analyze the clinical and laboratory characteristics of the cases evaluated. Methods We performed a retrospective case-control analysis including all COVID-19 cases evaluated for possible reinfection under the local COVID-19 reinfection evaluation programme between 1 June 2020-30 June 2021. Whole genome sequencing (WGS) was used as confirmatory testing. We compared all reinfection (“RI”) cases against those who were evaluated but eventually assessed not to be reinfection (“non-RI”). Results There were 74 possible reinfection cases evaluated through the programme, of which 32 were subsequently classified as RI. There was strong statistical evidence that RI cases had a longer interval between 1st and 2nd episode (mean 297 days; 95%-confidence interval (CI) 267–327) compared to non-RI cases (mean 186 days; 95%-CI 144–228). The cycle threshold (Ct) value of initial polymerase chain rection (PCR) at 2nd episode was also found to be significantly lower in RI cases (mean 23; 95%-CI 20–26) compared to non-RI cases (mean 34; 95%-CI 32–36). There was no significant difference in the proportion of individuals who had fever, acute respiratory symptoms or asymptomatic in both groups. Delta and beta variants were most commonly identified from WGS and provide indication of re-infection as these were not ‘wild-type’ and were not circulating during the time period of the index infection. Conclusions Using a combination of serologic, microbiologic and genomic criteria to evaluate possible reinfection cases is useful and can provide a framework for evaluation that may be modified for future similar situations.