Quantitative estimating size of deep defects in multi-layered structures from eddy current NDT signals using improved ant colony algorithm

Detection and quantitative estimation of deep defects in multi-layered structures is an essential task in a range of technological applications, such as maintaining the integrity of structures, enhancing the safety of aging aircraft, and assuring the quality of products. A novel approach to accurately quantify the two-dimensional axisymmetric deep defect size from eddy current nondestructive testing (NDT) signals is presented here. The method uses a finite element forward model to simulate the underlying physical process and an improved ant colony algorithm (IACA) to solve the inverse problem. Experiments are carried out. The performance comparison between the IACA method and the least square method is shown. The comparison results demonstrate the feasibility and validity of the IACA method. Between them, the IACA method gives a better estimation performance than the least square method at present

A Bayesian Network Method for Quantitative Evaluation of Defects in Multilayered Structures from Eddy Current NDT Signals

Accurate evaluation and characterization of defects in multilayered structures from eddy current nondestructive testing (NDT) signals are a difficult inverse problem. There is scope for improving the current methods used for solving the inverse problem by incorporating information of uncertainty in the inspection process. Here, we propose to evaluate defects quantitatively from eddy current NDT signals using Bayesian networks (BNs). BNs are a useful method in handling uncertainty in the inspection process, eventually leading to the more accurate results. The domain knowledge and the experimental data are used to generate the BN models. The models are applied to predict the signals corresponding to different defect characteristic parameters or to estimate defect characteristic parameters from eddy current signals in real time. Finally, the estimation results are analyzed. Compared to the least squares regression method, BNs are more robust with higher accuracy and have the advantage of being a bidirectional inferential mechanism. This approach allows results to be obtained in the form of full marginal conditional probability distributions, providing more information on the defect. The feasibility of BNs presented and discussed in this paper has been validated

Application of various genetic analysis techniques for detecting two rare cases of 9p duplication mosaicism during prenatal diagnosis

Abstract Background The identification of genetic mosaicism and the genetic counseling needed following its discovery have been challenging problems in the field of prenatal diagnosis. Herein, we describe the clinical phenotypes and various prenatal diagnostic processes used for two rare cases of 9p duplication mosaicism and review the prior literature in the field to evaluate the merits of different methods for diagnosing mosaic 9p duplication. Methods We recorded ultrasound examinations, reported the screening and diagnosis pathways, and analyzed the mosaic levels of the two cases of 9p duplication using karyotype analysis, chromosomal microarray analysis (CMA), and fluorescence in situ hybridization analysis (FISH). Results Case 1 had a normal clinical phenotype for tetrasomy 9p mosaicism, and Case 2 showed multiple malformations caused by both trisomy 9 and trisomy 9p mosaicism. Both cases were initially suspected after non‐invasive prenatal screening (NIPT) based on cell‐free DNA. The mosaic ratio of 9p duplication found via karyotyping was lower than what was discovered by CMA and FISH, in both cases. Contrary to previous findings, the mosaic level of trisomy 9 found by karyotype analysis was greater than what was found by CMA, in terms of complex mosaicism involving trisomy 9 and trisomy 9p, in Case 2. Conclusion NIPT can indicate 9p duplication mosaicism during prenatal screening. Different strengths and limitations existed in terms of diagnosing mosaic 9p duplication by karyotype analysis, CMA, and FISH. The combined use of various methods may be capable of more accurately determining break‐points and mosaic levels of 9p duplication during prenatal diagnosis

Generalized discrete‐time equivalent model for interfacing the grid‐connected photovoltaic system

Abstract The power system dynamics have consistently challenged the rapid and persistent proliferation of photovoltaic (PV) systems in power systems. In this paper, the authors propose the generalized discrete‐time equivalent model (GDEM) of PV power generation system using a fourth‐order dynamic equivalent model for representing the physical characteristics of PV power stations in power system dynamic studies. In addition, the authors propose a grid‐connected system model which includes modelling interfaces (MIs) for the GDEM of power grid and the GDEM of PV power generation system in dynamic studies. The proposed equivalent models of the power grid and PV power generation system will improve the simulation accuracy and speed in dynamic power system studies. The authors have used the IEEE 14‐bus system and simulated various types of short‐circuit faults and PV penetration levels in their study to demonstrate the merits of the proposed GDEM of grid‐connected system in power systems. The pertinent simulation results are analyzed and conclusions are presented

Eddy Current Inversion Models for Estimating Dimensions of Defects in Multilayered Structures

In eddy current nondestructive evaluation, one of the principal challenges is to determine the dimensions of defects in multilayered structures from the measured signals. It is a typical inverse problem which is generally considered to be nonlinear and ill-posed. In the paper, two effective approaches have been proposed to estimate the defect dimensions. The first one is a partial least squares (PLS) regression method. The second one is a kernel partial least squares (KPLS) regression method. The experimental research is carried out. In experiments, the eddy current signals responding to magnetic field changes are detected by a giant magnetoresistive (GMR) sensor and preprocessed for noise elimination using a wavelet packet analysis (WPA) method. Then, the proposed two approaches are used to construct the inversion models of defect dimension estimation. Finally, the estimation results are analyzed. The performance comparison between the proposed two approaches and the artificial neural network (ANN) method is presented. The comparison results demonstrate the feasibility and validity of the proposed two methods. Between them, the KPLS regression method gives a better prediction performance than the PLS regression method at present

Thermal treatment enhances the resisting exercise fatigue effect of Phyllanthus emblica L.: novel evidence from tannin conversion in vitro, metabolomics, and gut microbiota community analysis

Abstract Polyphenols are the main component of Phyllanthus emblica (PE). However, polyphenols are so easy to transform that it is unknown that how drying methods driven by heating affect the anti-fatigue effect of PE. This manuscript investigated the effects of five drying methods on the chemical composition transformation and anti-fatigue of PE, and discussed the action mechanism. The results suggested that the anti-fatigue effect of PE with hot-air-dried at 100 °C was the best, which was as 1.63 times as that with freeze-drying. Ellagic acid (EA) may be a key component of PE in anti-fatigue, and its mechanism of action may be related to regulating intestinal microbiota, protecting mitochondria, and regulating energy metabolism. This study first revealed the thermal transformation of polyphenols in PE, found the most effective strategy for enhancing the anti-fatigue function, and explores its action mechanism