Intricate Ionic Behaviors in High-Performance Self-Powered Hydrothermal Chemical Generator Using Water and Iron (III) Gate.

Utilizing the ionic flux to generate voltage output has been confirmed as an effective way to meet the requirements of clean energy sources. Different from ionic thermoelectric (i-TE) and hydrovoltaic devices, a new hydrothermal chemical generator is designed by amorphous FeCl3 particles dispersing in MWCNT and unique ferric chloride or water gate. In the presence of gate, the special ion behaviors enable the cell to present a constant voltage of 0.60 V lasting for over 96 h without temperature difference. Combining the differences of cation concentration, humidity and temperature between the right and left side of sample, the maximum short-circuit current and power output can be obtained to 168.46 µA and 28.11 µW, respectively. The generator also can utilize the low-grade heat to produce electricity wherein Seebeck coefficient is 6.79 mV K-1. The emerged hydrothermal chemical generator offers a novel approach to utilize the low-grade heat, water and salt solution resources, which provides a simple, sustainable and low-cost strategy to realize energy supply.

Structure and Charge Regulation Strategy Enabling Superior Cyclability for Ni-Rich Layered Cathode Materials.

Ni-rich layered oxides are significantly promising cathode materials for commercial high-energy-density lithium-ion batteries. However, their major bottlenecks limiting their widespread applications are capacity fading and safety concerns caused by their inherently unstable crystal structure and highly reactive surface. Herein, surface structure and bulk charge regulation are concurrently achieved by introducing high-valence Ta5+ ions in Ni-rich cathodes, which exhibit superior electrochemical properties and thermal stability, especially a remarkable cyclic stability with a capacity retention of 80% for up to 768 cycles at a 1C rate versus Li/Li+ . Due to the partial Ta enrichment on surface, the regulated surface enables high reversibility of Li+ insertion/extraction by preventing surface Ni reduction in deep charging. Moreover, bulk charge regulation that boosts charge density and its localization on oxygen remarkably suppresses microcracks and oxygen loss, which in turn prevents the fragmentation of the regulated surface and structural degradation associated with oxygen skeleton. This study highlights the significance of an integrated optimization strategy for Ni-rich cathodes and, as a case study, provides a novel and deep insights into the underlying mechanisms of high-valence ions substitution of Ni-rich layered cathodes.

Novel compound heterozygous mutations of PCNT gene in MOPD type II with central precocious puberty.

We report on a 6-year and 11-month old girl with short stature, microcephaly, proboscis nose, small teeth, left breast Tanner stage II, and nasopharynx adenoid hypertrophy. Her gestational age was 37 weeks and birth weight was 800 g. Her growth hormone peak was higher than 35.2 ng/ml, luteinizing hormone peak 8.97 IU/l, and blood glucose of 120 min 7.82 mmol/l in oral glucose tolerance test. Genetic testing revealed two novel heterozygous mutations in the PCNT gene, an insertion mutation at c.1828dupT (p.S610Ffs*32), and a splice site mutation at c.1207 + 1G>A, which were inherited from healthy carrier patients. This case shows that MOPDII can be associated with central precocious puberty and impaired glucose tolerance in addition to intrauterine growth restriction, postpartum growth defect, and microcephaly.

Deep learning networks for the recognition and quantitation of surface-enhanced Raman spectroscopy.

Surface-enhanced Raman spectroscopy (SERS) based on machine learning methods has been applied in material analysis, biological detection, food safety, and intelligent analysis. However, machine learning methods generally require extra preprocessing or feature engineering, and handling large-scale data using these methods is challenging. In this study, deep learning networks were used as fully connected networks, convolutional neural networks (CNN), fully convolutional networks (FCN), and principal component analysis networks (PCANet) to determine their abilities to recognise drugs in human urine and measure pirimiphos-methyl in wheat extract in the two input forms of a one-dimensional vector or a two-dimensional matrix. The best recognition result for drugs in urine with an accuracy of 98.05% in the prediction set was obtained using CNN with spectra as input in the matrix form. The optimal quantitation for pirimiphos-methyl was obtained using FCN with spectra in the matrix form, and the analysis was accomplished with a determination coefficient of 0.9997 and a root mean square error of 0.1574 in the prediction set. These networks performed better than the common machine learning methods. Overall, the deep learning networks provide feasible alternatives for the recognition and quantitation of SERS.

Diagnosis of the Severity of Fusarium Head Blight of Wheat Ears on the Basis of Image and Spectral Feature Fusion.

Fusarium head blight (FHB), one of the most prevalent and damaging infection diseases of wheat, affects quality and safety of associated food. In this study, to realize the early accurate monitoring of FHB, a diagnostic model of disease severity was proposed based on the fusion features of image and spectral features. First, the hyperspectral image of FHB infected in the range of the 400-1000 nm spectrum was collected, and the color parameters of wheat ear and spot region were segmented based on image features. Twelve sensitive bands were extracted using the successive projection algorithm, gray-scale co-occurrence matrix, and RGB color model. Four texture features were extracted from each feature band image as texture variables, and nine color feature variables were extracted from R, G, and B component images. Texture features with high correlation and color features were selected to participate in the final model building parameters via correlation analysis. Finally, the particle swarm optimization support vector machine (PSO-SVM) algorithm was used to build the model based on the diagnosis model of disease severity of FHB with different combinations of characteristic variables. The experimental results showed that the PSO-SVM model based on spectral and color feature fusion was optimal. Moreover, the accuracy of the training and prediction set was 95% and 92%, respectively. The method based on fusion features of image and spectral features can accurately and effectively diagnose the severity of FHB, thereby providing a technical basis for the timely and effective control of FHB and precise application of a pesticide.

Hyperspectral-based Estimation of Leaf Nitrogen Content in Corn Using Optimal Selection of Multiple Spectral Variables.

Accurate and dynamic monitoring of crop nitrogen status is the basis of scientific decisions regarding fertilization. In this study, we compared and analyzed three types of spectral variables: Sensitive spectral bands, the position of spectral features, and typical hyperspectral vegetation indices. First, the Savitzky-Golay technique was used to smooth the original spectrum, following which three types of spectral parameters describing crop spectral characteristics were extracted. Next, the successive projections algorithm (SPA) was adopted to screen out the sensitive variable set from each type of parameters. Finally, partial least squares (PLS) regression and random forest (RF) algorithms were used to comprehensively compare and analyze the performance of different types of spectral variables for estimating corn leaf nitrogen content (LNC). The results show that the integrated variable set composed of the optimal ones screened by SPA from three types of variables had the best performance for LNC estimation by the validation data set, with the values of R2, root means square error (RMSE), and normalized root mean square error (NRMSE) of 0.77, 0.31, and 17.1%, and 0.55, 0.43, and 23.9% from PLS and RF, respectively. It indicates that the PLS model with optimally multitype spectral variables can provide better fits and be a more effective tool for evaluating corn LNC.

Detection of Pirimiphos-Methyl in Wheat Using Surface-Enhanced Raman Spectroscopy and Chemometric Methods.

Pesticide residue detection is a hot issue in the quality and safety of agricultural grains. A novel method for accurate detection of pirimiphos-methyl residues in wheat was developed using surface-enhanced Raman spectroscopy (SERS) and chemometric methods. A simple pretreatment method was conducted to extract pirimiphos-methyl residue from wheat samples, and highly effective gold nanorods were prepared for SERS measurement. Raman peaks assignment was calculated using density functional theory. The Raman signal of pirimiphos-methyl can be detected when the concentrations of residue in wheat extraction solution and contaminated wheat is as low as 0.2 mg/L and 0.25 mg/L, respectively. Quantification of pirimiphos-methyl was performed by applying regression models developed by partial least squares regression, support vector machine regression and random forest with principal component analysis using different preprocessed methods. As for the contaminated wheat samples, the relative deviation between gas chromatography-mass spectrometry value and predicted value is in the range of 0.10%-6.63%, and predicted recovery is 94.12%-106.63%, ranging from 23.93 mg/L to 0.25 mg/L. Results demonstrated that the proposed SERS method is an effective and efficient analytical tool for detecting pirimiphos-methyl in wheat with high accuracy and excellent sensitivity.

The Dynamic Trends of Urinary LH and FSH Assayed by ICMA During Triptorelin Stimulation Tests in Girls - a Pilot Study.

BACKGROUND: Gonadotropin-releasing hormone stimulation test is a gold standard for evaluating the function of the hypothalamic-pituitary-gonadal axis (HPGA) in children. These tests are usually uncomfortable because of multi-venipunctures. A urine specimen is a good alternative because it is noninvasive and convenient. More studies have shown the correlation between sera and urine LH and FSH levels under different physiological and pathological conditions. METHODS: The study investigated the dynamic trends of urine LH (uLH) and FSH (uFSH) assayed by immunochemiluminometric assays (ICMA) during triptorelin stimulation tests in girls. The triptorelin stimulation tests were performed in 52 girls with disorders of puberty. The time 0 hour was regarded as the start time of the test (8:30 am). The day before the tests, urine samples were collected at 12 hours diurnal (-24 hours ~ -12 hours) and nocturnal (-12 hours ~ 0 hour) time points. On the day of the testing, the first 12 hours (0 hour ~ 12 hours), the second 12 hours (12 hours ~ 24 hours), the third 12 hours (24 hours ~ 36 hours), the fourth 12 hours (36 hours ~ 48 hours), the third and fourth overnight urine samples were also collected. The LH and FSH levels were assayed by ICMA, and uLH and uFSH were corrected for creatinine (Cr). RESULTS: The HPGA in 41 girls was activated but it was nonactivated in 11 girls. In girls with HPGA activated, uLH/Cr or uFSH/Cr was significantly elevated within 24 hours, and gradually dropped to baseline after 48 hours. When HPGA was nonactivated in girls, there were the same dynamic trends but much lower amplitude of uLH/Cr or uFSH/Cr, which dropped to baseline after 24 hours. CONCLUSIONS: The stimulated uLH and uFSH assayed by ICMA are valuable for evaluating the function of HPGA in girls, and the valuable time window is within 24 hours.

Fusion of Unmanned Aerial Vehicle Panchromatic and Hyperspectral Images Combining Joint Skewness-Kurtosis Figures and a Non-Subsampled Contourlet Transform.

To obtain fine and potential features, a highly informative fused image created by merging multiple images is usually required. In our study, a novel fusion algorithm called JSKF-NSCT is proposed for fusing panchromatic (PAN) and hyperspectral (HS) images by combining the joint skewness-kurtosis figure (JSKF) and the non-subsampled contourlet transform (NSCT). The JSKF model is used first to derive the three most sensitive bands from the original HS image according to the product of the skewness and the kurtosis coefficients of each band. Afterwards, an intensity-hue-saturation (IHS) transform is used to obtain the luminance component I of the produced false-colour image consisting of the above three bands. Then the NSCT method is used to decompose component I of the false-colour image and the PAN image. The weight-selection rule based on the regional energy is adopted to acquire the low-frequency coefficients and the correlation between the central pixel and its surrounding pixels is used to select the high-frequency coefficients. Finally, the fused image is obtained by applying an IHS inverse transform and an inverse NSCT transform. The unmanned aerial vehicle (UAV) HS and PAN images under low- and high-vegetation coverage of wheat at the flag leaf stage (Stage I) and the grain filling stage (Stage II) are used as the sample data sources. The fusion results are comparatively validated using spatial (entropy, standard deviation, average gradient and mean) and spectral (normalised difference vegetation, NDVI, and leaf area index, LAI) assessments. Additional comparative studies using anomaly detection and pixel clustering also demonstrate that the proposed method outperforms other methods. They show that the algorithm reported herein can better preserve the original spatial and spectral characteristics of the two types of images to be fused and is more stable than IHS, principal components analysis (PCA), non-negative matrix factorization (NMF) and Gram-Schmidt (GS).

Study on the Effect of Microwaved Brewer's Spent Grains on the Quality and Flavor Characteristics of Bread.

To enable a wider utilization of co-products from beer processing and minimize the negative effect of added grain on bread quality, flavor, and other attributes, brewer's spent grains (BSG) are processed through microwave pretreatment, and then the microwave-treated BSG (MW-BSG) is added to bread. So far, there has been no investigation on the effect of microwave-pretreated BSG on bread quality and flavor. In this study, we examined the effects of diverse microwave treatment variables on the physicochemical structure of BSG and explored the consequences of MW-BSG on the quality and flavor of bread. The results showed that soluble dietary fiber and water-soluble protein levels in MW-BSG increased significantly (144.88% and 23.35%) at a 540 W microwave power, 3 min processing time, and 1:5 material-liquid ratio of BSG to water. The proper addition of MW-BSG positively affected the bread texture properties and color, but excessive amounts led to an irregular size and distribution of the bread crumbs. The result of electronic nose and HS-SPME-GC-MS analyses showed that the addition of MW-BSG modified the odor profile of the bread. A sensory evaluation showed mean scores ranging from 6.81 to 4.41 for bread containing 0-10% MW-BSG. Consumers found a maximum level of 6% MW-BSG acceptable. This study endeavors to decrease environmental contamination caused by brewing waste by broadening the methods by which beer co-products can be utilized through an innovative approach.

Identification of surface-enhanced Raman spectroscopy using hybrid transformer network.

Surface-enhanced Raman Spectroscopy (SERS) is extensively implemented in drug detection due to its sensitivity and non-destructive nature. Deep learning methods, which are represented by convolutional neural network (CNN), have been widely applied in identifying the spectra from SERS for powerful learning ability. However, the local receptive field of CNN limits the feature extraction of sequential spectra for suppressing the analysis results. In this study, a hybrid Transformer network, TMNet, was developed to identify SERS spectra by integrating the Transformer encoder and the multi-layer perceptron. The Transformer encoder can obtain precise feature representations of sequential spectra with the aid of self-attention, and the multi-layer perceptron efficiently transforms the representations to the final identification results. TMNet performed excellently, with identification accuracies of 99.07% for the spectra of hair containing drugs and 97.12% for those of urine containing drugs. For the spectra with additive white Gaussian, baseline background, and mixed noises, TMNet still exhibited the best performance among all the methods. Overall, the proposed method can accurately identify SERS spectra with outstanding noise resistance and excellent generalization and holds great potential for the analysis of other spectroscopy data.

Encapsulation of roast beef flavor by soy protein isolate/chitosan complex Pickering emulsions to improve its releasing properties during the processing of plant-based meat analogues.

During the production of plant-based meat analogues (PBMA), a significant loss of flavor characteristic compounds in meat-flavor essences could be observed. Pickering emulsion-based encapsulation is an effective method to improve their stability. Therefore, a soy protein isolate (SPI)/chitosan (CS) complex Pickering emulsion was fabricated to encapsulate roast beef flavor (RBF) and further applied in the processing of PBMA. Our results indicated that the network structure of emulsions was dominated by elasticity, while hydrogen and covalent bonding interactions played important roles in the encapsulation process. The release rate of flavor compounds gradually increased with the increase of pH value, glutamine transaminase, NaCl content, heating temperature or heating time, while encapsulation significantly reduced the loss of characteristic aroma compounds. In addition, the releasing characteristics of aroma compounds and textural properties of PBMA were greatly improved by treating with RBF-loaded emulsions. Consequently, the emulsions were promising to improve the flavor quality of PBMA.

A novel strategy for estimating biomass of submerged aquatic vegetation in lake integrating UAV and Sentinel data.

Submerged aquatic vegetation (SAV) plays a fundamental ecological role in mediating carbon cycling within lakes, and its biomass is essential to assess the carbon sequestration potential of lake ecosystems. Remote sensing (RS) offers a powerful tool for large-scale SAV biomass retrieval. Given the underwater location of SAV, the spectral signal in RS data often exhibits weakness, capturing primarily horizontal structure rather than volumetric information crucial for biomass assessment. Fortunately, easily-measured SAV coverage can serve as an intermediary variable for difficultly-quantified SAV biomass inversion. Nevertheless, obtaining enough SAV coverage samples matching satellite image pixels for robust model development remains problematic. To overcome this challenge, we employed a UAV to acquire high-precision data, thereby replacing manual SAV coverage sample collection. In this study, we proposed an innovative strategy integrating unmanned aerial vehicle (UAV) and satellite data to invert large-scale SAV coverage, and subsequently estimate the biomass of the dominant SAV population (Potamogeton pectinatus) in Ulansuhai Lake. Firstly, a coverage-biomass model (R2 = 0.93, RMSE = 0.8 kg/m2) depicting the relationship between SAV coverage and biomass was developed. Secondly, in a designed experimental area, a high-precision multispectral image was captured by a UAV. Based on the Normalized Difference Water Index (NDWI), the UAV-based image was classified into non-vegetated and vegetated areas, thereby generating an SAV distribution map. Leveraging spatial correspondence between satellite pixels and the UAV-based SAV distribution map, the proportion of SAV within each satellite pixel, referred to as SAV coverage, was computed, and a coverage sample set matched with satellite pixels was obtained. Subsequently, based on the sample set, a satellite-scale SAV coverage estimation model (R2 = 0.78, RMSE = 14.05 %) was constructed with features from Sentinel-1 and Sentinel-2 data by XGBoost algorithm. Finally, integrating the coverage-biomass model with the obtained coverage inversion results, fresh biomass of SAV in Ulansuhai Lake was successfully estimated to be approximately 574,600 tons.

A group decision making method with preference analysis to re-build the Global Entrepreneurship Index.

This study proposes a group decision making (GDM) method with preference analysis to re-build the Global Entrepreneurship Index (GEI). Specifically, a single decision maker is firstly identified using a specified individual judgement about the importance order of three sub-indices of the GEI. A preliminary group decision matrix is constructed in terms of taking all possible individual judgments into account. Then the analysis of the preferential differences and preferential priorities with respect to the preliminary group decision matrix is conducted to obtain a revised group decision matrix, in which preferential differences calculate the weighted differences as the degrees of differences among different alternatives for each decision maker, preferential priorities describe the favorite ranking of alternatives for each decision maker. Finally, we employ the Stochastic Multicriteria Acceptability Analysis for group decision-making (SMAA-2) to create the holistic acceptability indices for measuring the entrepreneurship performance. In addition, a satisfaction index is developed to indicate the merits of proposed GDM method. A case study using the GEI-2019 of 19 G20 countries is carried out to validate our GDM method.

Redox-dual-sensitive multiblock copolymer vesicles with disulfide-enabled sequential drug delivery.

Based on disulfide-enriched multiblock copolymer vesicles, we present a straightforward sequential drug delivery system with dual-redox response that releases hydrophilic doxorubicin hydrochloride (DOX·HCl) and hydrophobic paclitaxel (PTX) under oxidative and reductive conditions, respectively. When compared to concurrent therapeutic delivery, the spatiotemporal control of drug release allows for an improved combination antitumor effect. The simple and smart nanocarrier has promising applications in the field of cancer therapy.

Drought stress identification of tomato plant using multi-features of hyperspectral imaging and subsample fusion.

Drought stress (DS) is one of the most frequently occurring stresses in tomato plants. Detecting tomato plant DS is vital for optimizing irrigation and improving fruit quality. In this study, a DS identification method using the multi-features of hyperspectral imaging (HSI) and subsample fusion was proposed. First, the HSI images were measured under imaging condition with supplemental blue lights, and the reflectance spectra were extracted from the HSI images of young and mature leaves at different DS levels (well-watered, reduced-watered, and deficient-watered treatment). The effective wavelengths (EWs) were screened by the genetic algorithm. Second, the reference image was determined by ReliefF, and the first four reflectance images of EWs that are weakly correlated with the reference image and mutually irrelevant were obtained using Pearson's correlation analysis. The reflectance image set (RIS) was determined by evaluating the superposition effect of reflectance images on identification. The spectra of EWs and the image features extracted from the RIS by LeNet-5 were adopted to construct DS identification models based on support vector machine (SVM), random forest, and dense convolutional network. Third, the subsample fusion integrating the spectra and image features of young and mature leaves was used to improve the identification further. The results showed that supplemental blue lights can effectively remove the high-frequency noise and obtain high-quality HSI images. The positive effect of the combination of spectra of EWs and image features for DS identification proved that RIS contains feature information pointing to DS. Global optimal classification performance was achieved by SVM and subsample fusion, with a classification accuracy of 95.90% and 95.78% for calibration and prediction sets, respectively. Overall, the proposed method can provide an accurate and reliable analysis for tomato plant DS and is hoped to be applied to other crop stresses.

Determination of adulteration in wheat flour using multi-grained cascade forest-related models coupled with the fusion information of hyperspectral imaging.

Wheat flour (WF) is a common ingredient in staple foods. However, the presence of intentional or unintentional adulterants makes it difficult to guarantee WF quality. Multi-grained cascade forest (gcForest) model, a non-neural network deep learning structure, fused with image-spectra features from hyperspectral imaging (HSI) was employed for detecting adulterant type (peanut, walnut, or benzoyl peroxide) and the corresponding concentration (0.03%, 0.05%, 0.1%, 0.5%, 1%, and 2%). Based on the spectra of full wavelength and effective wavelength (EW) from hyperspectral images of WF samples, the gcForest-related models exhibited high performance (lowest ACCP = 92.45%) and stability (lowest area under the curve = 0.9986). Furthermore, the fusion of the EW and the image features extracted by the symmetric all convolutional neural network (SACNN) was used to establish the gcForest-related models. The maximum accuracy improvement of the fusion feature model relative to the single spectral model and the image model was 2.45% and 44.37%, respectively. The results indicate that the gcForest-related model, combined with the image-spectra fusion feature of HSI, provides an effective tool for detection in food and agriculture.

Fusion of hyperspectral imaging (HSI) and RGB for identification of soybean kernel damages using ShuffleNet with convolutional optimization and cross stage partial architecture.

Identification of soybean kernel damages is significant to prevent further disoperation. Hyperspectral imaging (HSI) has shown great potential in cereal kernel identification, but its low spatial resolution leads to external feature infidelity and limits the analysis accuracy. In this study, the fusion of HSI and RGB images and improved ShuffleNet were combined to develop an identification method for soybean kernel damages. First, the HSI-RGB fusion network (HRFN) was designed based on super-resolution and spectral modification modules to process the registered HSI and RGB image pairs and generate super-resolution HSI (SR-HSI) images. ShuffleNet improved with convolution optimization and cross-stage partial architecture (ShuffleNet_COCSP) was used to build classification models with the optimal image set of effective wavelengths (OISEW) of SR-HSI images obtained by support vector machine and ShuffleNet. High-quality fusion of HSI and RGB with the obvious spatial promotion and satisfactory spectral conservation was gained by HRFN. ShuffleNet_COCSP and OISEW obtained the optimal recognition performance of ACCp=98.36%, Params=0.805 M, and FLOPs=0.097 G, outperforming other classification methods and other types of images. Overall, the proposed method provides an accurate and reliable identification of soybean kernel damages and would be extended to analysis of other quality indicators of various crop kernels.

Detection of wheat Fusarium head blight using UAV-based spectral and image feature fusion.

Infection caused by Fusarium head blight (FHB) has severely damaged the quality and yield of wheat in China and threatened the health of humans and livestock. Inaccurate disease detection increases the use cost of pesticide and pollutes farmland, highlighting the need for FHB detection in wheat fields. The combination of spectral and spatial information provided by image analysis facilitates the detection of infection-related damage in crops. In this study, an effective detection method for wheat FHB based on unmanned aerial vehicle (UAV) hyperspectral images was explored by fusing spectral features and image features. Spectral features mainly refer to band features, and image features mainly include texture and color features. Our aim was to explain all aspects of wheat infection through multi-class feature fusion and to find the best FHB detection method for field wheat combining current advanced algorithms. We first evaluated the quality of the two acquired UAV images and eliminated the excessively noisy bands in the images. Then, the spectral features, texture features, and color features in the images were extracted. The random forest (RF) algorithm was used to optimize features, and the importance value of the features determined whether the features were retained. Feature combinations included spectral features, spectral and texture features fusion, and the fusion of spectral, texture, and color features to combine support vector machine, RF, and back propagation neural network in constructing wheat FHB detection models. The results showed that the model based on the fusion of spectral, texture, and color features using the RF algorithm achieved the best performance, with a prediction accuracy of 85%. The method proposed in this study may provide an effective way of FHB detection in field wheat.

Laser-Generated Guided Waves for Damage Detection in Metal-Lined Composite-Overwrapped Pressure Vessels.

This paper characterizes laser-generated guided waves in a metal-lined composite-overwrapped pressure vessel (COPV) to assess typical damage, including interfacial debonding and low-velocity impact damage. First, an eigenfrequency approach that avoids additional coding is utilized to theoretically analyze the dispersion characteristics of a COPV. The theoretical results show that interfacial debonding significantly alters dispersion curves, and the wavenumber of the L(0, 1) mode is sensitive to impact damage. Experimental verifications were conducted based on the full wavefield acquired using a scanning laser-ultrasonic system with a repetition rate of 1 kHz. By comparing the experimental dispersion curves with the theoretical ones, it was found that the metal-composite interface was not bonded. In addition, a local wavenumber estimation method was established to detect the impact damage by obtaining the spatial distribution of the wavenumber of the L(0, 1) mode.