Journal of Nondestructive Evaluation最新文献

{"title":"Surrogate Modelling Approach in Materials Dielectric Constant Prediction: Sim-to-Real and Generalization Capability Analysis","authors":"Ediz Erdem,&nbsp;Erkan Sami Kokten","doi":"10.1007/s10921-026-01430-x","DOIUrl":"10.1007/s10921-026-01430-x","url":null,"abstract":"<div><p>In this study, a machine learning–based surrogate modeling framework is proposed for the rapid and accurate estimation of the dielectric constant of materials using only the reflection coefficient amplitude, phase, frequency, and thickness of the materials. A comprehensive simulation dataset was generated using the finite element method (FEM) within the CST Studio Suite software environment for twelve materials exhibiting a wide dielectric spectrum. These data were used to train an XGBoost regression model designed to capture the nonlinear relationship between dielectric properties and microwave reflection behavior inside a rectangular waveguide. The model's prediction performance was evaluated using multiple error metrics, yielding high accuracy with R² = 0.9472, MAE = 0.1734, and MAPE = 2.52%. To assess the model's generalization capability, it was tested on an unseen simulation dataset, achieving an R² value of 0.9059 and confirming reliable interpolation capabilities within the evaluated parametric limits of the simulation space. Furthermore, a sim-to-real validation was conducted through experimental measurements using plexiglass, wood (Scots pine), and alumina ceramic surface plate (Al₂O₃) samples in the 1.72–6 GHz frequency band. The experimental results demonstrated strong agreement with ground-truth dielectric constants, with an average MAPE of 9.46%. SHAP-based explainability analysis revealed that the reflection amplitude is the dominant feature guiding the model’s predictions, consistent with electromagnetic theory. Overall, the findings confirm that the proposed surrogate model serves as a computationally efficient proof-of-concept framework. While its full generalization requires wider empirical validation, this initial implementation demonstrates an agile alternative for specific material classes by bypassing computationally expensive analytical inversion techniques.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"45 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148909540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Single-sensor Acoustic Emission Damage Location in Orthotropic Steel Decks Based on Continuous Wavelet Transform and Convolutional Neural Network","authors":"Pifu Cheng,&nbsp;Dan Li,&nbsp;Jiahao Nie,&nbsp;Lu Zhang,&nbsp;Zonglian Wang,&nbsp;Hongyu Li","doi":"10.1007/s10921-026-01423-w","DOIUrl":"10.1007/s10921-026-01423-w","url":null,"abstract":"<div><p>Orthotropic steel decks (OSDs) have found extensive application in long span bridges, owing to their excellent mechanical performance and constructional convenience. However, the presence of fatigue cracks remains a persistent threat to long-term structural safety. It is thus imperative to develop highly efficient and accurate methods for fatigue crack identification and location. In this context, a single-sensor acoustic emission (AE) damage location framework is developed based on continuous wavelet transform (CWT) and convolutional neural network (CNN), which leverages the high sensitivity of AE and the strong learning ability of artificial intelligence. CWT helps to represent the all-around propagation characteristics of AE signals in the time–frequency domain. CNN model then extracts those location sensitive features from CWT maps and predicts the location of AE damage sources. A field pencil lead break (PLB) experiment was conducted on an OSD segment in the operating status of the long-span bridge to validate the performance of the developed method, where a series of CNN models, including AlexNet, VGG19, and ResNet18, were established and compared. Results show that the framework based on CWT and ResNet18 could achieve satisfactory location accuracy with a single AE sensor, demonstrating significant potential for large-scale engineering structures.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"45 3","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148928091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DLE-UNet: Cross-Group Enhanced Channel Attention and Dual-Path Collaborative Encoding for Defect Detection in Aluminum Castings","authors":"Huijie Luo,&nbsp;Yi Liu,&nbsp;Pengcheng Zhang,&nbsp;Yongqi Yan,&nbsp;Yanan Wu,&nbsp;Zhiguo Gui","doi":"10.1007/s10921-026-01415-w","DOIUrl":"10.1007/s10921-026-01415-w","url":null,"abstract":"<div><p>Segmenting defects in X-ray digital radiography (DR) images of aluminum castings presents significant challenges due to the extremely low pixel coverage of minute defects, blurred boundaries, and weak contrast. The core issues lie in the fundamental trade-off between expanding the receptive field and preserving fine spatial details, as well as the background noise introduced by indiscriminate feature propagation during decoder fusion. To address these challenges, we propose a novel and improved U-Net architecture named DLE-UNet. First, we design a decoupled collaborative dual-path encoder. By structurally separating the contextual semantic stream from the spatial detail stream, it achieves synergistic optimization of large receptive fields and high-resolution details, thereby mitigating the loss of minute defect features in deep layers. Second, we introduce a cascaded boundary refinement decoder. In its initial stage, a Large-kernel Group Attention Gating module performs coarse feature screening to suppress background noise. Subsequently, a content-guided upsampling module conducts sub-pixel-level refinement of boundary features. Experiments on an aluminum casting defect dataset demonstrate that our method outperforms existing approaches on key metrics such as the F1-score (81.3%) and Intersection over Union (IoU, 68.4%). Notably, it maintains a superior recall rate while achieving high precision, validating its advanced capability and robustness in performing high-integrity, high-precision segmentation of minute defects in complex industrial scenarios.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"45 3","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148928092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dual-Parameter Inversion for Lift-Off-Resilient Eddy Current Testing of Broken-Wire Defects in Steel Wire Ropes","authors":"Junxia Li,&nbsp;Shuai Wang,&nbsp;Jianxing Song,&nbsp;Shining Qin,&nbsp;Ziming Kou,&nbsp;Haowen Zheng","doi":"10.1007/s10921-026-01422-x","DOIUrl":"10.1007/s10921-026-01422-x","url":null,"abstract":"<div><p>Steel wire ropes are critical load-bearing components in industrial conveying systems, and accurate quantification of broken-wire defects is essential for operational safety. However, in eddy current testing, lift-off disturbance significantly affects signals, causing nonlinear coupling between wire breakage severity and lift-off, which reduces evaluation accuracy and stability. To address this issue, a lift-off-resilient method for quantitative detection of broken-wire defects is proposed. First, a coaxial dual-receiver coil probe is designed to extract the minimum voltage values from the near and far receiver coils as dual features, thereby enhancing the system's capability to distinguish between lift-off variation and defect severity. Subsequently, a nonlinear relationship between voltage features and wire breakage severity is established based on finite element simulations under controlled lift-off conditions, and validated experimentally. Based on this, a forward model is constructed using natural neighbor interpolation, and a genetic algorithm is employed for global optimization, enabling the simultaneous inversion of wire breakage severity and lift-off value. The results demonstrate that the proposed method effectively mitigates the influence of lift-off variation on detection results, achieving a relative inversion error of less than 1%, while maintaining high accuracy and stability under varying defect severities and lift-off conditions. Compared with conventional single-coil methods, the proposed approach significantly improves inversion accuracy and stability, and effectively resolves the nonlinear coupling between lift-off effect and defect severity through a dual-feature and dual-parameter inversion framework. This study provides a reliable solution for high-precision quantitative detection of broken-wire defects in complex industrial environments.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"45 3","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148838285","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Dynamic Identification Method for Rotor Faults in Automotive Power Steering Pumps Based on Multi-sensor AE Signals and SVD Orthogonal Modal Centroid Frequency","authors":"Shishang Dong,&nbsp;Xiaokai Wang,&nbsp;Fangyu Chen,&nbsp;Ming Li","doi":"10.1007/s10921-026-01421-y","DOIUrl":"10.1007/s10921-026-01421-y","url":null,"abstract":"<div><p>Aiming at the dynamic identification of fault states for automotive power steering pump rotors, this paper proposes a dual-threshold identification method based on multi-sensor acoustic emission(AE) signals and singular value decomposition(SVD) orthogonal modal centroid frequencies(CF). Firstly, rotors under normal(<i>N</i>), cracked(<i>C</i>), and reversed-blade(<i>R</i>) conditions are separately assembled into the same pump. Under identical operating conditions, 4-channel AE signals are collected near the inlet and outlet with a sampling frequency of 1 MHz. Secondly, single-cycle sub-signals are extracted from the acquired raw signals. After wavelet denoising(WD) and whitening processing, a feature matrix is constructed and decomposed by SVD to obtain four orthogonal modes. Subsequently, each orthogonal mode is processed using 3-layer wavelet packet decomposition(WPD), and the CF values of the first four nodes are calculated. Through preliminary screening based on the relative increase and dual-threshold verification combining the silhouette coefficient and coefficient of variation, third-layer WPD Node (3,1) is determined as the optimal sensitive feature node. Finally, discrimination thresholds are established based on the Shapiro–Wilk normality test and the 99% two-sided confidence interval. The experimental results show that, after WPD of the orthogonal modes, the CF feature of Node (3,1) provides the best state discrimination capability. Using <i>T</i>_<i>N-C</i> = 115.0 kHz and <i>T</i>_<i>R-N</i> = 118.0 kHz as the core thresholds, the proposed method accurately identifies the three rotor states, with an identification accuracy close to 100%. This approach reduces the subjectivity and visual blind spots of traditional fluorescent magnetic particle inspection and provides a promising technical scheme for dynamic and real-time fault monitoring of automotive power steering pump rotors.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"45 3","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148782955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hyperspectral Technology-based Prediction of Infrared Rejection for Automotive Window Film","authors":"Jianjun Xu,&nbsp;Yifan Jiang,&nbsp;Yong He,&nbsp;Lulu Jiang","doi":"10.1007/s10921-026-01413-y","DOIUrl":"10.1007/s10921-026-01413-y","url":null,"abstract":"<div><p>Automotive window film is a critical component for enhancing driving comfort and safety, as its infrared rejection rate directly affects the vehicle’s thermal insulation performance and energy consumption. Given the wide variety of brands and the considerable variation in product quality in the current market, there is an urgent need for a rapid, non-destructive, and brand-independent universal detection technology to regulate the market, protect consumer rights, and strengthen quality control during production. This study aims to develop a quantitative prediction model for the infrared rejection rate of automotive window films based on hyperspectral imaging technology. A total of 765 window film samples from five major brands were collected, and their visible-near-infrared hyperspectral data were acquired. To optimize spectral quality, six preprocessing methods-none, Savitzky-Golay smoothing, standard normal variate (SNV), baseline correction, detrending, and Normalization-were applied. Feature wavelength selection was performed using three algorithms: successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), and uninformative variable elimination (UVE). The optimal number of principal components was determined using ten-fold cross-validation, the one-standard-error (1-SE) rule, and a threshold of CV R² ≥ 0.80 to avoid overfitting. Subsequently, a partial least squares regression (PLSR) model was established for quantitative prediction of the infrared rejection rate. The results indicated that SNV and Normalization preprocessing yielded the best performance, significantly improving prediction accuracy and model stability. The CARS algorithm achieved the best balance between dimensionality reduction and prediction accuracy. The final optimal detection scheme was determined as Normalization preprocessing combined with CARS feature extraction and PLSR modeling. A total of 140 feature wavelengths were selected, reducing the dimensionality by 65.1% compared to the full spectrum. The model achieved a test set R² of 0.9344, RMSEP of 5.03, and RPD of 3.92, with a strong fit between predicted and measured values, a uniform residual distribution, and no significant overfitting. This model enables accurate and stable quantitative predictions of the infrared rejection rate of automotive window films from different brands, providing an efficient and reliable technical means for quality inspection and rapid screening of automotive films.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"45 3","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148782956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Nondestructive Testing Method for Local Concealment Features in Aviation Glass Based on Multi-Dimensional Feature Fusion","authors":"Dahai Liao,&nbsp;Yilang Tu,&nbsp;Leqi Jiang,&nbsp;Baoxi Zhu,&nbsp;Wei Wang,&nbsp;Hao Chen","doi":"10.1007/s10921-026-01414-x","DOIUrl":"10.1007/s10921-026-01414-x","url":null,"abstract":"<div><p>A feature extraction method combines anisotropic guided filtering with multimodal large-component adaptive segmentation. It addresses the local concealment features of aviation glass and enables accurate detection of small-target defects. The local concealment features of aviation glass are analyzed. Canny edge detection provides the edge foundation for the guided filtering. Local window size, regularization parameter, and anisotropic factor are dynamically adjusted to remove noise while preserving details. The adaptive threshold is dynamically determined according to the local gray-level characteristics of the image for binarization. An area threshold is set to filter and fuse potential target features, achieving accurate segmentation of local concealment feature images. The filtered image achieves a peak signal-to-noise ratio of 37.61, a structural similarity index of 0.91, and a feature extraction accuracy of 0.9962. The method effectively overcomes the interference of local concealment features in the precise segmentation and extraction of aviation glass feature images.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"45 3","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148751294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficient Coded Excited Thermal Wave Imaging for Non-Destructive Testing and Evaluation of Material Loss in Pipelines","authors":"Sameeha Sharma,&nbsp;Vanita Arora,&nbsp;Ravibabu Mulaveesala","doi":"10.1007/s10921-026-01418-7","DOIUrl":"10.1007/s10921-026-01418-7","url":null,"abstract":"<div><p>Detection of subsurface material loss in metallic pipelines is essential for ensuring structural integrity and operational safety yet remains challenging due to limited depth resolution and reduced sensitivity to deeper defects in conventional active infrared thermography techniques. In this work, a novel optimized 15-bit coded excitation sequence is proposed for the detection of material loss in mild steel pipelines using active infrared thermography. The excitation sequence is designed using a brute-force optimization framework to achieve favourable pulse-compression characteristics and an enhanced matched-filter response, enabling effective concentration of thermal energy within a narrow main lobe while suppressing sidelobe energy distribution. Experimental investigations are carried out on a mild steel pipeline specimen containing multiple artificial defects representing localized material loss. The acquired thermographic data are processed using frequency-domain phase, time-domain phase, and cross-correlation coefficient (CCC) based post-processing approaches to extract defect-related features and mitigate the effects of non-uniform heating. The performance of the proposed excitation scheme is quantitatively evaluated and compared with Linear Frequency Modulated Thermal Wave Imaging (LFMTWI) using spatial and temporal signal-to-noise ratio (SNR) as the primary figures of merit. The results demonstrate that the optimized 15-bit coded excitation, particularly when combined with CCC processing, provides significantly higher SNR, improved defect contrast, and superior detectability of deeper defects compared to LFMTWI and phase-based processing methods. These findings confirm that enhanced main-lobe energy concentration and effective sidelobe suppression are the key contributors to improved sensitivity, reliability, and depth-resolved inspection capability, establishing the proposed approach as a robust and practical solution for thermographic evaluation of material loss in mild steel pipeline structures.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"45 3","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148751297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Real-Time Diagnosis of Crack Initiation in Aluminum 2024 Sharp Notch Samples Using Acoustic Emission Processing","authors":"Jesse Yochens,&nbsp;Cheosung O’Brien,&nbsp;Brian Wisner","doi":"10.1007/s10921-026-01416-9","DOIUrl":"10.1007/s10921-026-01416-9","url":null,"abstract":"<div><p>This study presents a real-time framework for diagnosing crack initiation in Aluminum 2024-T3 sharp notch samples by integrating acoustic emission (AE) processing with supervised machine learning. To overcome the technical limitations of traditional ex-situ analysis, a multi-threaded, concurrent processing architecture was developed in the C# programming language, enabling simultaneous data collection, feature extraction, and classification. A novel \"initiation ratio\" metric was implemented to identify the onset of cracking by quantifying the proportion of fracture-specific waveforms relative to other damage-related signals. Comparative analysis between contact-based piezoelectric (PZT) sensors and a non-contact laser vibrometer demonstrated that PZT sensors achieved 100% accuracy in detecting crack initiation under both monotonic and fatigue loading. While the laser vibrometer showed a 96.8% accuracy during monotonic tests, its performance in fatigue was limited by sensitivity to global movement and background noise. The concurrent framework reduced total processing time by up to 78%, providing a robust and efficient methodology for early-stage structural health monitoring in real-world applications.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"45 3","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10921-026-01416-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148751295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"In-situ Monitoring Of Time-varying Geometry During FDM via Ultrasonic Leaky Lamb Waves","authors":"Qi Zhu,&nbsp;Yiming Liu,&nbsp;Feng He,&nbsp;Haiyan Zhang,&nbsp;Qingqing Zhang,&nbsp;Shiwei Ma","doi":"10.1007/s10921-026-01417-8","DOIUrl":"10.1007/s10921-026-01417-8","url":null,"abstract":"<div><p>With the widespread of additive manufacturing technologies, real-time monitoring of deficiencies during the printing process has become essential for improving product quality and manufacturing efficiency. An ultrasonic method based on Lamb wave leakage from the printing bed to the deposition layers is proposed for the in-situ monitoring of the time-varying geometry during fused deposition modeling (FDM). An array sensor network is arranged on the printing bed to collect energy attenuation coefficients in a pitch-catch configuration. The leakage characteristics of various Lamb wave modes under different deposition conditions have been explored in detail through both simulation and experimental. The energy attenuation coefficient of each mode exhibits a stable linear relationship with deposition length in a given layer. Oscillation can be observed initially with deposition thickness due to resonance. A high sensitivity can be found for the first layer, which is lowered for the subsequent layers. Nonlinear effects exist for the deposition location and distribution across modes. The fastest mode can be selected to prevent secondary source interference. An imaging scheme based on the simultaneous iterative reconstruction technique (SIRT) is established to reconstruct the PLA deposition contour on an aluminum printing bed. The imaging error in the central region is less than 10%, with each image completed within 380 ms. The proposed methodology is powerful for the first layer quality inspection throughout the whole FDM process.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"45 3","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148614520","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}