Artificial Intelligence in Geosciences最新文献

{"title":"Unsupervised hierarchical sequence stratigraphy framework of carbonate successions","authors":"Márcio Vinicius Santana Dantas ,&nbsp;Kaio Henrique Masse Vieira ,&nbsp;Thomás Jung Spier ,&nbsp;José Arthur Oliveira Santos ,&nbsp;Alan Cabral Trindade Prado ,&nbsp;Danilo Vomlel ,&nbsp;Mariana Leite ,&nbsp;Felipe Alves Farias ,&nbsp;Daniel Galvão Carnier Fragoso ,&nbsp;Humberto Reis ,&nbsp;Gabriel Coutinho ,&nbsp;Douglas G. Macharet","doi":"10.1016/j.aiig.2025.100160","DOIUrl":"10.1016/j.aiig.2025.100160","url":null,"abstract":"<div><div>Performing the high-resolution stratigraphic analysis may be challenging and time-consuming if one has to work with large datasets. Moreover, sedimentary records have signals of different frequencies and intrinsic noise, resulting in a complex signature that is difficult to identify only through eyes-based analysis. This work proposes identifying transgressive-regressive (T-R) sequences from carbonate facies successions of three South American basins: (i) São Francisco Basin - Brazil, (ii) Santos Basin - Brazil, and (iii) Salta Basin - Argentina. We applied a hidden Markov model in an unsupervised approach followed by a Score-Based Recommender System that automatically finds medium or low-frequency sedimentary cycles from high-frequency ones. Our method is applied to facies identified using Fullbore Formation Microimager (FMI) logs, outcrop description, and composite logs from carbonate intervals. The automatic recommendation results showed better long-distance correlations between medium- to low-frequency sedimentary cycles, whereas the hidden Markov model method successfully identified high-resolution (high-frequency) transgressive and regressive systems tracts from the given facies successions. Our workflow offers advances in the automated analyses and construction of lower- to higher-rank stratigraphic framework and short to long-distance stratigraphic correlation, allowing for large-scale automated processing of the basin dataset. Our approach in this work fits the unsupervised learning framework, as we require no previous input of stratigraphical analysis in the basin. The results provide solutions for prospecting any sediment-hosted mineral resource, especially for the oil and gas industry, offering support for subsurface geological characterization, whether at the exploration scale or for reservoir zoning during production development.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"6 2","pages":"Article 100160"},"PeriodicalIF":4.2,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145264973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancements in Sinkhole Remediation: Field data-driven Sinkhole grout volume prediction model via machine learning-based regression Analysis","authors":"Bubryur Kim ,&nbsp;Yuvaraj Natarajan ,&nbsp;K.R. Sri Preethaa ,&nbsp;V. Danushkumar ,&nbsp;Ryan Shamet ,&nbsp;Jiannan Chen ,&nbsp;Rui Xie ,&nbsp;Timothy Copeland ,&nbsp;Boo Hyun Nam ,&nbsp;Jinwoo An","doi":"10.1016/j.aiig.2025.100159","DOIUrl":"10.1016/j.aiig.2025.100159","url":null,"abstract":"<div><div>Sinkhole formation poses a significant geohazard in karst regions, where unpredictable subsurface erosion often necessitates costly grouting for stabilization. Accurate estimation of grout volume remains a persistent challenge due to spatial variability, site-specific conditions, and the limitations of traditional empirical methods. This study introduces a novel machine learning-based regression model for grout volume prediction that integrates cone penetration test (CPT)-derived Sinkhole Resistance Ratio (SRR) values, spatial correlations between CPT and grouting points (GPs), and field-recorded grout volumes from six sinkhole sites in Florida. Three data transformation methods, the Proximal Allocation Method (PAM), the Equitable Distribution Method (EDM), and the Threshold-based Equitable Distribution Method (TEDM), were applied to distribute grout influence across CPTs, with TEDM demonstrating superior predictive performance. Synthetic data augmentation using spline methodology further improved model robustness. A high-degree polynomial regression model, optimized with ridge regularization, achieved high accuracy (R² = 0.95; PEV = 0.94) and significantly outperformed existing linear and logarithmic models. Results confirm that lower SRR values correlate with higher grout demand, and the proposed model reliably captures these nonlinear relationships. This research advances sinkhole remediation practice by providing a data-driven, accurate, and generalizable framework for grout volume estimation, enabling more efficient resource allocation and improved project outcomes.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"6 2","pages":"Article 100159"},"PeriodicalIF":4.2,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145320144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of a reliable rock slope stability model utilizing field and analytical data – An integration of FE-ML approaches","authors":"Virat Singh Chauhan ,&nbsp;Md. Rehan Sadique ,&nbsp;Mohd. Masroor Alam ,&nbsp;Mohd. Ahmadullah Farooqi","doi":"10.1016/j.aiig.2025.100158","DOIUrl":"10.1016/j.aiig.2025.100158","url":null,"abstract":"<div><div>Slope instability in hilly regions is a highly complex phenomenon, with triggering factors ranging from natural events to anthropogenic activities. Such failures hit disastrous losses both in terms of material as well as life. It is necessary to comprehend the mechanism of these failures to mitigate such events and also to predict their vulnerability for better preparedness. Significant advancements have already been done in the area of slope stability analysis, and scores of valued tools and techniques have been developed, such as limit equilibrium methods, finite element and finite difference methods, stochastic methods, and several of their combinations. In this study, an attempt has been made to capitalize on machine learning tools to predict the factor of safety of rock slope stability in hilly regions. Three road-cut slopes have been considered and their stability is determined using both finite element (FE) and machine learning (ML) techniques. The idea to intertwine these approaches is to supplement each other and enhance the reliability of the results. The geotechnical data was acquired through field investigation trips to the adopted mountainous sites. Since the slopes at the site are rocky, in the FE model, the Generalized Hoek Brown (GHB) material model with shear strength reduction technique have been used. In the implementation of ML models, Random Forest (RF) and Gradient Boosting Machine (GBM) models have been used. For the training of the ML model, ample published data has been utilized, while for testing the ML model, the data from the current slope site is used. The analysis in ML model is carried out in three stages: a) without Hyperparameter tuning, b) with Hyperparameter tuning using GridSearchCV, and c) Pipeline incorporating Recursive Feature Elimination (RFE). Performance metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), and R² score, were evaluated to assess the accuracy of the model. A slight discrepancy within a range of 10 percent has been found, which is rather expected due to factors such as grid refinement and, data volume and variability. Overall, the proposed ML model demonstrates excellent compatibility with the FE model results. This study is an attempt to pick relevant ML techniques to develop a purpose-built framework that has the potential to validate the rock slope stability obtained using the traditional methods.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"6 2","pages":"Article 100158"},"PeriodicalIF":4.2,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145219428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification of major minerals in igneous rock microscopic images from thin sections through deep neural network analysis","authors":"Kouadio Krah ,&nbsp;Sié Ouattara ,&nbsp;Gbele Ouattara ,&nbsp;Marc Euphrem Allialy ,&nbsp;Alain Clement","doi":"10.1016/j.aiig.2025.100157","DOIUrl":"10.1016/j.aiig.2025.100157","url":null,"abstract":"<div><div>Several socio-environmental needs (medicine, industry, engineering, orogenesis, genesis, etc.) require minerals to be more precisly defined and characterised. The identification of minerals plays a crucial role for researchers and is becoming an essential aspect of geological analysis. However, traditional methods relied heavily on expert knowledge and specialised equipment, making them labour-intensive, costly and time-consuming. This dependence is often labour-intensive, not to mention costly and time-consuming. To address this issue, some researchers have opted for machine learning algorithms to quickly identify a single mineral in a microscopic image of rocks. However this approch does not correspond to patterns of mineral distribution, where minerals are typically found in associations. These associations make it difficult to accurately identify minerals using conventional machine learning algorithms. This paper introduces a deep neural learning model based on multi-label classification, utilizing the problem adaptation method to analyse microscopic images of rock thin sections. The model is based on the ResNet50 architecture, which is designed to analyse minerals and generates the probability of a mineral presence in an image. This method provides a solution to the dependence between associated minerals. Experiments on many test images showed a model confidence, achieving average precision, recall and F1_score 97.15 %, 96.25 % and 96.69 %, respectively. Visualisation of the class activation mapping using the Grad-CAM algorithm indicates that our model is likely to locate the identified minerals effectively. In this way, the importance of each pixel with the class of interest can be assessed using heat maps. The <strong>recorded</strong> results, in terms of both performance and pixel_level evaluation, demonstrate the promising potential of the model used. It can therefore be considered for multi-labels image classification, particulary for images representing rock minerals. This approach serves as a valuable support tool for geological studies.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"6 2","pages":"Article 100157"},"PeriodicalIF":4.2,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145219427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison of the performance of gradient boost, linear regression, decision tree, and voting algorithms to separate geochemical anomalies areas in the fractal environment","authors":"Mirmahdi Seyedrahimi-Niaraq ,&nbsp;Hossein Mahdiyanfar ,&nbsp;Mohammad hossein Olyaee","doi":"10.1016/j.aiig.2025.100156","DOIUrl":"10.1016/j.aiig.2025.100156","url":null,"abstract":"<div><div>In this investigation, the Gradient Boosting (GB), Linear Regression (LR), Decision Tree (DT), and Voting algorithms were applied to predict the distribution pattern of Au geochemical data. Trace and indicator elements, including Mo, Cu, Pb, Zn, Ag, Ni, Co, Mn, Fe, and As, were used with these machine learning algorithms (MLAs) to predict Au concentration values in the Doostbigloo porphyry Cu-Au-Mo mineralization area. The performance of the models was evaluated using the Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) metrics. The proposed ensemble Voting algorithm outperformed the other models, yielding more accurate predictions according to both metrics. The predicted data from the GB, LR, DT, and Voting MLAs were modeled using the Concentration-Area fractal method, and Au geochemical anomalies were mapped. To compare and validate the results, factors such as the location of the mineral deposits, their surface extent, and mineralization trend were considered. The results indicate that integrating hybrid MLAs with fractal modeling significantly improves geochemical prospectivity mapping. Among the four models, three (DT, GB, Voting) accurately identified both mineral deposits. The LR model, however, only identified Deposit I (central), and its mineralization trend diverged from the field data. The GB and Voting models produced similar results, with their final maps derived from fractal modeling showing the same anomalous areas. The anomaly boundaries identified by these two models are consistent with the two known reserves in the region. The results and plots related to prediction indicators and error rates for these two models also show high similarity, with lower error rates than the other models. Notably, the Voting model demonstrated superior performance in accurately delineating mineral deposit locations and identifying realistic mineralization trends while minimizing false anomalies.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"6 2","pages":"Article 100156"},"PeriodicalIF":4.2,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145219420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine-learning seismic damage assessment model for building structures","authors":"Fatma Zohra Belhadj ,&nbsp;Ahmed Fouad Belhadj ,&nbsp;Mohamed Chabaat","doi":"10.1016/j.aiig.2025.100155","DOIUrl":"10.1016/j.aiig.2025.100155","url":null,"abstract":"<div><div>Buildings in seismic-prone regions are highly vulnerable to structural damage, necessitating meticulous Seismic Damage Assessment (SDA) for accurate design and mitigation strategies. The intricate nature of Seismic Damage Assessment (SDA) poses challenges, particularly when employing Finite Element Analysis (FE) for individual structures, as simulation techniques are time-intensive due to the inherent complexity of the models. Computational methods combining Soil-Structure Interaction (SSI) for earthquake damage assessment further compound the challenge, requiring substantial computational efforts to construct a comprehensive database for area-based prediction models. This study introduces such challenges via a novel Artificial Neural Network (ANN) approaches-based model as an alternative for prompt building Seismic Damage Assessment evaluation. The proposed ANN model leverages three key inputs—seismic, building, and soil parameters—incorporating a multi-step analysis process to generate seismic responses with soil-structure interaction. The findings underscore the remarkable accuracy of the SDA-Net model, positioning it as an effective predictive tool and rapid decision support system for structures affected by SSI impacts. This innovative approach not only serves as a proactive pre-disaster management tool for assessing potential damage but also emerges as a practical asset for ensuring the safety and durability of structures in the face of natural disasters. The study's contribution lies in its potential application as a valuable tool in structural engineering, aligning with the objectives and scope of the Research Journal of The Institution of Structural Engineers.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"6 2","pages":"Article 100155"},"PeriodicalIF":4.2,"publicationDate":"2025-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145157171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application research of SSA-RF model in predicting the height of water-conducting fracture zone in deep and thick coal seams","authors":"Li Wang ,&nbsp;Jiming Zhu ,&nbsp;Zhongchang Wang","doi":"10.1016/j.aiig.2025.100154","DOIUrl":"10.1016/j.aiig.2025.100154","url":null,"abstract":"<div><div>The 91 measured values of the development height of the water-conducting fracture zone (WCFZ) in deep and thick coal seam mining faces under thick loose layer conditions were collected. Five key characteristic variables influencing the WCFZ height were identified. After removing outliers from the dataset, a Random Forest (RF) regression model optimized by the Sparrow Search Algorithm (SSA) was constructed. The hyperparameters of the RF model were iteratively optimized by minimizing the Out-of-Bag (OOB) error, resulting in the rapid determination of optimal parameters. Specifically, the SSA-RF model achieved an OOB error of 0.148, with 20 decision trees, a maximum depth of 8, a minimum split sample size of 2, and a minimum leaf node sample size of 1. Cross-validation experiments were performed using the trained optimal model and compared against other prediction methods. The results showed that the mining height had the most significant correlation with the development height of the WCFZ. The SSA-RF model outperformed all other models, with R² values exceeding 0.9 across the training, validation, and test datasets. Compared to other models, the SSA-RF model demonstrates a simpler structure, stronger fitting capacity, higher predictive accuracy, and superior stability and generalization ability. It also exhibits the smallest variation in relative error across datasets, indicating excellent adaptability to different data conditions.Furthermore, a numerical model was developed using the hydrogeological data from the 1305 working face at Wanfukou Coal Mine, Shandong Province, China, to simulate the dynamic development of the WCFZ during mining. The SSA-RF model predicted the WCFZ height to be 69.7 m, closely aligning with the PFC2D simulation result of 65 m, with an error of less than 5 %. Compared to traditional methods and numerical simulations, the SSA-RF model provides more accurate predictions, showing only a 7.23 % deviation from the PFC2D simulation, while traditional empirical formulas yield deviations as large as 19.97 %. These results demonstrate the SSA-RF model's superior predictive capability, reinforcing its reliability and engineering applicability for real-world mining operations. This model holds significant potential for enhancing mining safety and optimizing planning processes, offering a more accurate and efficient approach for WCFZ height prediction.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"6 2","pages":"Article 100154"},"PeriodicalIF":4.2,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145048833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Opportunities, epistemological assessment and potential risks of machine learning applications in volcano science","authors":"Mónica Ágreda-López,&nbsp;Maurizio Petrelli","doi":"10.1016/j.aiig.2025.100153","DOIUrl":"10.1016/j.aiig.2025.100153","url":null,"abstract":"<div><div>This manuscript explores the opportunities and epistemological risks of using machine learning in the Earth sciences with a focus on igneous petrology and volcanology. It begins by highlighting the benefits of machine learning, particularly in automating tasks, enhancing modelling strategies, and accelerating knowledge discovery. However, the integration of machine learning into scientific research also introduces significant challenges. Key concerns include understanding what machine learning models learn, ensuring transparency, reproducibility, and improving model interpretability. These issues become especially critical in high-risk contexts such as volcanic hazard assessment, risk mitigation, and crisis management, where the reliance on machine learning outcomes can have profound consequences for human lives. The manuscript also introduces additional ethical considerations, such as the risk of over-reliance on machine learning models and the broader implications of geopolitical development plans, laws and regulations in the EU, China, and the US.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"6 2","pages":"Article 100153"},"PeriodicalIF":4.2,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145009932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning assisted enhancement of petrophysical property dataset of fractured Variscan granites of the Cornubian Batholith, SW UK","authors":"A. Turan ,&nbsp;E. Artun ,&nbsp;I. Sass","doi":"10.1016/j.aiig.2025.100151","DOIUrl":"10.1016/j.aiig.2025.100151","url":null,"abstract":"<div><div>Outcrop analogue studies play an important role in advancing our comprehension of reservoir architectures, offering insights into hidden reservoir rocks prior to drilling, in a cost-effective manner. These studies contribute to the delineation of the three-dimensional geometry of geological structures, the characterization of petro- and thermo-physical properties, and the structural geological aspects of reservoir rocks. Nevertheless, several challenges, including inaccessible sampling sites, limited resources, and the dimensional constraints of different laboratories hinder the acquisition of comprehensive datasets. In this study, we employ machine learning techniques to estimate missing data in a petrophysical dataset of fractured Variscan granites from the Cornubian Batholith in Southwest UK. The utilization of mean, k-nearest neighbors, and random forest imputation methods addresses the challenge of missing data, thereby revealing the effectiveness of random forest imputation in providing realistic estimations. Subsequently, supervised classification models are trained to classify samples according to their pluton origins, with promising accuracy achieved by models trained with imputed values. Variable importance ranking of the models showed that the choice of imputation method influences the inferred importance of specific petrophysical properties. While porosity (POR) and grain density (GD) were among important variables, variables with high missingness ratio were not among the top variables. This study demonstrates the value of machine learning in enhancing petrophysical datasets, while emphasizing the importance of careful method selection and model validation for reliable results. The findings contribute to a more informed decision-making process in geothermal exploration and reservoir characterization efforts, thereby demonstrating the potential of machine learning in advancing subsurface characterization techniques.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"6 2","pages":"Article 100151"},"PeriodicalIF":4.2,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145009931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Understanding hydrological responses through LULC analysis and predictive modelling (MLPNN-MC Model): A study of Bandu Sub-watershed (India) over three decades","authors":"Sudipto Halder ,&nbsp;Somnath Mandal ,&nbsp;Zarkheen Mukhtar ,&nbsp;Debdas Ray ,&nbsp;Gupinath Bhandari ,&nbsp;Suman Paul","doi":"10.1016/j.aiig.2025.100152","DOIUrl":"10.1016/j.aiig.2025.100152","url":null,"abstract":"","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"6 2","pages":"Article 100152"},"PeriodicalIF":4.2,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144887019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}