Journal of Nondestructive Evaluation最新文献

{"title":"Comparative Evaluation of Hyperspectral, Thermal, and Near-Infrared Imaging Systems for Non-Destructive Prediction of Fruit and Vegetable Quality: A Review","authors":"Kok Jeng Lim,&nbsp;Phebe Ding,&nbsp;Nazmi Mat Nawi,&nbsp;Mashitah Jusoh,&nbsp;Zailani Khuzaimah","doi":"10.1007/s10921-026-01376-0","DOIUrl":"10.1007/s10921-026-01376-0","url":null,"abstract":"<div><p>The development of non-destructive sensing technologies has significantly improved quality evaluation in the agri-food sector by enabling rapid and contact-free assessment of both internal and external attributes of fruits and vegetables. This review compares three widely used modalities—hyperspectral imaging (HSI), thermal imaging (TI), and visible–near infrared spectroscopy (Vis–NIRS)—with attention to their working principles, system configurations, spectral properties, and predictive performance. Their effectiveness in estimating important physicochemical parameters, including soluble solids content, firmness, moisture, and pigment composition, is examined using commonly reported evaluation metrics such as the coefficient of determination (R²), root mean square error (RMSE), and residual predictive deviation (RPD). In addition, the contribution of chemometric and data-driven modeling approaches to improving prediction accuracy and robustness is discussed. Vis–NIRS is well suited for rapid compositional analysis, HSI provides detailed spatial and spectral information, and TI offers complementary insights into surface temperature variations associated with defects or physiological changes. The integration of these techniques through multimodal frameworks presents promising opportunities for practical grading and quality monitoring applications. Future developments are expected to focus on multimodal data fusion frameworks, deep learning–based feature extraction, transfer learning–enabled model generalization, and the deployment of portable, real-time sensing systems for in-field applications.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"45 2","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148008912","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":"Infrared Thermography for Rainfall-Induced Slope Instability: Mechanisms, Monitoring and Critical Insights","authors":"Kohima Dessai,&nbsp;Harikumar M,&nbsp;Lalat Indu Giri","doi":"10.1007/s10921-026-01377-z","DOIUrl":"10.1007/s10921-026-01377-z","url":null,"abstract":"<div><p>Rain-induced slope failures pose a major hazard in natural and engineered environments, emphasizing the need for non-destructive techniques capable of capturing subsurface moisture dynamics. Infrared thermography has gained increasing attention, of late, as a remote sensing tool for the detection of thermal anomalies associated with water seepage and moisture accumulation along the slope, which act as precursors for slope failure. This review critically evaluates the application of Infrared thermography (IRT) in Geotechnical Engineering, especially in assessing landslides, with primary focus on passive field-based monitoring that exploits natural thermal contrast, thereby exhibiting its efficiency as a non-contact, non-destructive landslide monitoring technique. This paper synthesizes recent studies demonstrating the utility of thermographic methods in identifying early signs of landslides by capturing thermally induced signatures of moisture redistribution that govern rainfall-induced slope instability. Key methodological aspects such as the detection limits, variation of surface emissivity and atmospheric attenuation under rainy conditions are discussed. Further, the thermal image processing methodologies and data analysis procedures, such as Principal Component Analysis (PCA) and Structural Similarity Index Method (SSIM), are comprehensively explained. Finally, by summarizing existing research and identifying gaps, this review article aims to focus on the advantages and limitations of infrared thermography in landslide prediction and outline future directions of research in this emerging field.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"45 2","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148008913","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":"Time–Frequency Domain Analysis of Subsurface Defects in Carbon Fiber Reinforced Polymer Composites using Chirp Modulated Thermal Wave Imaging","authors":"Mamta Janagal,&nbsp;Geetika Dua,&nbsp;Kulbir Singh","doi":"10.1007/s10921-026-01361-7","DOIUrl":"10.1007/s10921-026-01361-7","url":null,"abstract":"<div><p>The groundbreaking achievements in materials manufacturing technology have led to an increase in the usage of the Carbon Fiber Reinforced Polymer (CFRP) composite material in the aerospace, automotive, and civil engineering industries. The CFRP materials, because of their high strength-to-weight ratio, resistance to corrosion and reliability in terms of their mechanical properties, have become invaluable. However, the fact that these composites contain defects including but not limited to: gaps, delaminated and ingrained material, and inclusions can deteriorate their performance, which is why advanced quality control techniques are required. In this regard, active Thermal Wave Imaging techniques have considered of great significance. This study is an investigation of Chirp Modulated Thermal Wave Imaging (CMTWI), utilizing simulations and experiments, as a non-destructive evaluation method for CFRP materials. The simulation part included a CFRP specimen with 25 defects placed systematically in terms of diameter and depths. The synthetic data is overlaid with Gaussian noise in order to simulate the realistic environmental disruption. In a complementary manner, an empirical study is carried out on a CFRP test sample with nine defects, and the variation in both diameter and depth spans three discrete levels. The results of simulated and experimental studies are analyzed using modern time and frequency domain methods. Baseline drift is attenuated first by using the method of second-order polynomial fitting. Later post-processing involved the actual Discrete Fourier Transform (using Fast Fourier Transform), which helped extract frequency-domain magnitude thermal profiles, as well as pulse compression through circular convolution in the time domain. The obtained pulse-compressed thermal profiles are measured in the time domain, included statistical metrics, such as the peak to peak amplitude, mean value, crest factor, and standard deviation. Similarly, the features such as maximum magnitude, kurtosis, frequency skewness, and Harmonics to Noise Ratio (HNR) are obtained from the frequency domain data. The results indicate that high level of correlation between simulation and empirical evidence, hence, these findings support the effectiveness of CMTWI when combined with the use of dual-domain (time–frequency) analysis in order to ensure the accurate identification of defects in CFRP composites and their correction.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"45 2","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148008980","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":"Magnetic-Resonance Eddy-Current Test Enhanced by a Bidirectional GRU Network for Simultaneous Prediction of Rebar Diameter and Embedment Depth in Concrete","authors":"Jie Zhu,&nbsp;Leng Liao,&nbsp;Jinming Zhang","doi":"10.1007/s10921-026-01372-4","DOIUrl":"10.1007/s10921-026-01372-4","url":null,"abstract":"<div><p>This paper introduces a non-destructive inspection paradigm for reinforced concrete that integrates Magnetic Resonance Eddy-Current Testing (MRECT) with a bidirectional Gated Recurrent Unit (GRU) dual-target regressor. The MRECT probe excites resonant magnetic fields whose near-field coupling with embedded steel yields an order-of-magnitude increase in sensitivity to rebar presence and geometry. By spatially scanning resonant-voltage signatures, the system synthesizes a high-resolution map of the hidden reinforcement grid without surface preparation. Concurrently, the GRU-based post-processor lifts the resonant-voltage signatures acquired directly above each rebar into a pre-trained, learned feature space whose coordinates seamlessly encode continuous variations in bar diameter and embedment depth, enabling single-shot, high-fidelity prediction of both parameters. Extensive validation on 120 concrete slabs (cover 2–5 cm, diameters 6–25 mm) delivers Mean Absolute Percentage Errors (MAPE) of 2.41% for diameter and 2.44% for depth—outperforming ISO 1920–7 benchmarks for cover meters and ground-penetrating radar. The resulting rapid, single-sided, contactless protocol provides a laboratory-validated technical prototype, offering promising potential for future application in construction quality assurance and long-term structural health monitoring of concrete infrastructure.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"45 2","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147938029","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":"Prediction of Mechanical Properties of Straw-Stabilized Earth Blocks Using Non-Destructive Ultrasonic Testing: Ultrasonic Pulse Velocity","authors":"Abderrahmane Jouhar,&nbsp;Mokhfi Takarli,&nbsp;Fatima Allou,&nbsp;Mohammed Cherraj,&nbsp;Driss El Hachmi","doi":"10.1007/s10921-026-01375-1","DOIUrl":"10.1007/s10921-026-01375-1","url":null,"abstract":"<div><p>Quality control of compressed earth construction materials remains a technical challenge, mainly due to the variability of raw materials and the absence of standardized non-destructive evaluation methods. This study proposes an original experimental approach to predict the mechanical performance of compressed earth blocks (CEBs), stabilized with wheat straw, using ultrasonic pulse velocity (UPV) measurements. A total of 120 blocks were manufactured according to an experimental design combining four levels of compaction energy (25, 50, 75, and 100 blows per layer) and five straw contents (0% to 8%). Each specimen was subjected to destructive tests (dry and saturated compressive strength) and non-destructive tests using UPV. Statistical analysis enabled the development of accurate regression models linking UPV measurements to the physical and mechanical properties of the blocks. The results show a very strong linear correlation between UPV and dry compressive strength (R² ≈ 0.97), a good prediction of saturated compressive strength (R² ≈ 0.96), and a moderate but consistent relationship with air-dry density. This novel approach shows that UPV can be a reliable and effective non-destructive indicator for monitoring the quality of fiber-reinforced CEBs within the investigated material system. The methodology developed in this work thus contributes to the valorization of bio-based materials and the optimization of quality control in sustainable construction sectors, while providing a reproducible and accessible protocol for resource-limited environments.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"45 2","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147938154","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":"Damage Classification of Carbon Fiber Reinforced Polymer Cables under Progressive Reciprocating Tensile Loading using a Hybrid Deep Learning Approach","authors":"Peng-Fei Zhang,&nbsp;Lian-Hua Ma,&nbsp;Ran Liu,&nbsp;Wei Zhou","doi":"10.1007/s10921-026-01371-5","DOIUrl":"10.1007/s10921-026-01371-5","url":null,"abstract":"<div><p>Carbon fiber reinforced polymer (CFRP) cables are increasingly used in bridges, cableways, and other engineering structures due to their lightweight, high strength, and excellent corrosion resistance. However, accurately identifying and monitoring their damage modes under cyclic loading remains a critical challenge for structural health monitoring. In this study, progressive reciprocating tensile tests were conducted on CFRP cables and their constituent materials, while acoustic emission (AE) technique was employed to capture damage‑related signals. To achieve precise classification of damage modes, a Kepler Optimization Algorithm (KOA) enhanced k‑medoids clustering was first used to preliminarily identify the characteristic frequency ranges of matrix cracking and fiber breakage. Subsequently, a hybrid deep learning model integrating convolutional neural networks (CNN), bidirectional long short‑term memory networks (BiLSTM), and an attention mechanism, with hyperparameters optimized by KOA, was developed for accurate damage classification. The results demonstrate that the proposed KOA‑CNN‑BiLSTM‑Attention model achieves a classification accuracy exceeding 99.71% (validated by tenfold cross‑validation) and effectively distinguishes three damage modes: matrix cracking, debonding, and fiber fracture. The model’s predicted damage evolution shows excellent agreement with experimental observations of final failure behaviors, such as matrix explosion and fiber fracture. This work provides a reliable deep‑learning‑based tool for in‑situ damage monitoring of CFRP cables under service‑like cyclic loading.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"45 2","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147797101","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":"EHE-YOLO: An Enhanced Detection Approach for CFRP Hole-Making Burr Defects","authors":"Rongrong Wang,&nbsp;Jigang Wu,&nbsp;Youzhi Jiang,&nbsp;Tai An,&nbsp;Li Sun,&nbsp;Jiaming Chen","doi":"10.1007/s10921-026-01365-3","DOIUrl":"10.1007/s10921-026-01365-3","url":null,"abstract":"<div><p>Carbon Fiber Reinforced Plastic (CFRP) is widely used in aerospace and other fields due to its high specific strength and high specific modulus. Hole-making is a critical process in the assembly of CFRP components. However, the anisotropy of CFRP often results in burr defects during this process, which compromise assembly precision and reduce service life. Therefore, accurate detection of burrs in CFRP hole-making is crucial for ensuring the safety of components. In response to the challenges of detecting burr defects in CFRP, such as severe background texture interference, blurred defect edges, a wide range of target scales, and difficulties in identifying small target defects, this paper proposes the Enhanced High-Extraction YOLO (EHE-YOLO) defect detection method. It introduces the Efficient Aggregation Enhanced Module (EAEM) to boost feature extraction against background and edge blurring, designs the High Resolution Boundary Enhanced Neck (HRBE-Neck) for cross-scale co-optimization, and constructs the Efficient Detail Capture Detection Head (EDCDH) for micro-burr recognition. Validated on the self-constructed CFRP-Burr dataset through ablation studies, comparative analyses, and visualization experiments, and validated through generalization experiments on the Severstal Steel Defect and VisDrone2019 datasets. The results demonstrate that EHE-YOLO achieves a detection accuracy of 91.51% for CFRP burrs, enabling high-precision detection of multi-scale and small targets. Finally, this paper discusses the advantages and limitations of the EHE-YOLO model, proposes targeted directions for further improvement, and summarizes the model’s value in industrial applications as well as its future development prospects.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"45 2","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147797100","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":"Ultrasonic Inspection of Tow-Steered Composite Laminates During Autoclave Cure","authors":"Tyler B. Hudson,&nbsp;Cyrus J. R. Kosztowny,&nbsp;Nicholas A. Georgiou,&nbsp;Ryan S. Enos,&nbsp;Finnian P. Day","doi":"10.1007/s10921-026-01366-2","DOIUrl":"10.1007/s10921-026-01366-2","url":null,"abstract":"<div><p>Due to the anisotropic properties of unidirectional carbon fiber reinforced polymer (CFRP) composites, novel layup techniques, such as tow-steering, can be used to tailor mechanical properties of the composite structure. In this work, two tow-steered composite panels were designed, fabricated using the Integrated Structural Assembly of Advanced Composites (ISAAC) automated fiber placement (AFP) machine located at the NASA Langley Research Center (LaRC), and cured while simultaneously being inspected using an ultrasonic inspection system operating inside the autoclave. A pristine panel was fabricated along with a panel with intentionally placed defects. The defect composite panel contained overlaps that are intrinsic to the tow-steered design and had intentionally introduced layup defects including folds, wrinkles, splices, tow twists, foreign object debris (FOD), gaps, and additional overlaps. The inspections during the cure cycle focused on the area within the “defect” laminate containing a fold, a twist, and intrinsic overlaps. The ultrasonic inspections performed during the cure cycle were analyzed and compared to post-cure ultrasonic inspections of both laminates. An ultrasound method and a structured light method were used to quantify warpage of each panel post cure.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"45 2","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147797120","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":"Hybrid Deep Learning-Based Automatic Identification, Localization and Quantitative Evaluation of Internal Defects in Welds","authors":"Qi Zheng,&nbsp;Hao Wang,&nbsp;Xiaohui Zhao,&nbsp;Xiujun Wang,&nbsp;Mengran Li,&nbsp;Chao Chen","doi":"10.1007/s10921-026-01368-0","DOIUrl":"10.1007/s10921-026-01368-0","url":null,"abstract":"<div><p>Currently, there is a lack of integrated application solutions for the rapid identification, localization, and quantitative evaluation of internal defects in weld seams within industrial scenarios. Based on this, this study proposes a hybrid detection scheme that integrates You Only Look Once version 8 (YOLOv8) with the Cascade Feedforward Neural Network (CFNN), establishing an intelligent technical system that encompasses the entire workflow of defect quality inspection. In this scheme, YOLOv8 utilizes a lightweight feature pyramid network to enable accurate identification of defects; CFNN constructs a nonlinear mapping model between the pixel coordinates of defect features and their corresponding physical coordinates to achieve accurate localization; and a novel geometric integration approach is proposed to calculate the cross-sectional area of defect contours, thereby facilitating quantitative defect evaluation. The verification experimental results indicate that the defect recognition accuracy achieves 100%. The average errors for the lateral and vertical coordinates of the defects (<span>({text{W}}_{1}^{*})</span>,<span>({text{W}}_{2}^{*})</span>) are 0.000332 mm (at the sixth decimal place) and 0.000576 mm (at the sixth decimal place), respectively, while the average error for the depth coordinate (<span>({text{h}}^{*})</span>) is 0.001577 mm. In the process of quantitative evaluation of defects, the average errors in the height (<span>({text{H}}^{*})</span>) and cross-sectional area (<span>({text{S}}^{*})</span>) of weld defects are 0.00282 mm and 1.46 mm², respectively. This study presents a comprehensive and integrated technical solution for the online detection of weld defects in industrial environments, which significantly advances the practical implementation of ultrasonic testing technology in actual industrial applications.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"45 2","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147797139","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":"Development of Inspection Method for Steel structure Using Active Temperature Gap Method with Backside Heating","authors":"Daisuke Imanishi,&nbsp;Daiki Shiozawa,&nbsp;Takahide Sakagami","doi":"10.1007/s10921-026-01369-z","DOIUrl":"10.1007/s10921-026-01369-z","url":null,"abstract":"<div><p>This study proposes a novel non-contact inspection technique for detecting fatigue cracks in steel structures, specifically targeting the travelling girders of grab-type unloader cranes. These girders are often difficult to inspect due to their height and limited accessibility. The proposed method, called the Active Temperature Gap Method with backside heating, utilizes localized heating on the internal surface of the girder to induce a temperature gap at crack locations, which is then visualized using infrared thermography. Laboratory experiments and finite element (FE) analysis were conducted to determine optimal heating conditions, including heating time and distance. A heat gun was used as a practical heating source, and the effectiveness of both fixed and travelling heating methods was evaluated. The results showed that a heating distance of 30 mm and a heating time of 20 s provided the best balance between thermal diffusion and crack insulation. Additionally, the use of a travelling heat source significantly improved the uniformity of heating and enhanced crack edge detection. Image processing using the Sobel filter further improved the clarity and accuracy of crack visualization. The method successfully detected both surface and through thickness cracks, even in areas inaccessible from the outside. This approach offers a promising solution for efficient, safe, and cost-effective maintenance of aging industrial infrastructure. Future work will focus on field validation and automation of the heating and image analysis processes to support real time inspection.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"45 2","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147797122","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}