Natural Resources Research最新文献

{"title":"Experimental and Molecular Simulation Research on the Oxidation Behavior of Soaked Coal Spontaneous Combustion","authors":"Xin-Xiao Lu, Guo-Yu Shi, Shuo Wang, Guan Wang, Zi-Yao Chen","doi":"10.1007/s11053-025-10460-7","DOIUrl":"https://doi.org/10.1007/s11053-025-10460-7","url":null,"abstract":"<p>The goaf residual coal is more susceptible to oxidation after long-term soaking that raises the spontaneous combustion risk level. This paper investigates the oxidation thermodynamic mechanism and the active functional group proliferation trend in soaked coal. A coal macromolecular model C₂₁₇H₁₇₁O₄₄N₃S₂ is constructed to evaluate the oxygen adsorption effectiveness via molecular dynamics simulation. The results show that the soaking behavior reduces the coal intramolecular hydrogen bonds and expands the coal pore volume. The soaked coal creates 1.18 times average oxygen adsorption loading higher than the raw coal. The soaking decreases the crossing point temperature from 147.40 °C to 144.15 °C and enlarges the CO production rate by 1.087 times, increasing the potential hazard of coal oxidation. The reactive functional groups -CHO, –CH₂, –CH₃, and –OH exhibit an evident increase of 0.73, 0.01, 0.42, and 0.72 after water soaking. The –CH₂/–CH₃ drops from 1.95 to 1.13, indicating that aliphatic chain consists of shorter and more branched chains. The increase in oxygen adsorption and reactive functional group of soaked coal promotes the coal oxidation chain reaction, which boosts oxidation temperature rise and gas release.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"27 4 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143077722","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interpretability Analysis of Data Augmented Convolutional Neural Network in Mineral Prospectivity Mapping Using Black-Box Visualization Tools","authors":"Yue Liu, Tao Sun, Kaixing Wu, Wenyuan Xiang, Jingwei Zhang, Hongwei Zhang, Mei Feng","doi":"10.1007/s11053-025-10462-5","DOIUrl":"https://doi.org/10.1007/s11053-025-10462-5","url":null,"abstract":"<p>Machine learning is becoming a popular and appealing tool in mineral prospectivity mapping (MPM); however, it has always been challenged by some essential limitations, such as scarcity of training samples, overfitting, and uncertainties. Data augmentation has been proven to be effective in addressing these issues and improving the performance of artificial intelligence models, but its mechanism regarding how augmented data influences predictive modeling processes, improves model performance, and alleviates overfitting has yet to be elucidated due to the black-box nature of machine learning modeling. In this study, the synthetic minority oversampling technique (SMOTE), proven to perform best among five commonly used data augmentation methods, was selected and utilized to enhance the training data and improve model performance. The results indicate that the convolutional neural network (CNN) model trained by rational-feature ordering and SMOTE-augmented data achieved better performance, with higher test accuracy (0.9306), recall (0.9167), F1-score (0.9296), and alleviated overfitting (0.0215), compared with the model trained on original data. A set of black-box visualization tools, including filter weight visualization, individual conditional expectation (ICE) plots, derivative ICE (d-ICE) plots, partial dependence plots (PDPs), and Shapley additive explanations (SHAP), were employed to explore the beneficial mechanism of SMOTE when applied to enhance the predictive capabilities of CNN in MPM. The visualization of the weight filters reveals that the optimal model activates favorable excitations of W anomalies, Mn anomalies and proximity to Yanshanian intrusions, which are associated with tungsten mineralization, thus optimizing feature extraction, refining convolutional operation, and improving model performance. The ICE and d-ICE analyses reveal that the SMOTE-augmented model exhibites a more consistent decision trend in key ore-associated features and reduces variability in derivative estimates, particularly beyond decision thresholds, leading to stabler predictions. The PDP results show that SMOTE-augmented data increase the decision boundary difference between positive and negative samples, suggesting a broader decision width that favored more accurate classification. The SHAP analyses indicate that the SMOTE-augmented data boost the recognition ability of the CNN model by clearly separating feature values of key ore-associated factors with contrasting SHAP values and help the model make more convergent decision paths, especially for samples with top probabilities. Our findings provide a straightforward view for explaining how a superior algorithm can benefit model predictions through black-box modeling processes, and contribute to understanding the decision-making mechanism of machine learning in MPM.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"50 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143071609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrated Hydrogeophysical Study for the Delineation of Mio–Plio–Quaternary Aquifers in the Central Part of the Sousse Governorate (Tunisian Sahel)","authors":"S. Ben Skander, R. Guellala, W. Abouda","doi":"10.1007/s11053-025-10457-2","DOIUrl":"https://doi.org/10.1007/s11053-025-10457-2","url":null,"abstract":"<p>The Sousse governorate (Tunisian Sahel) is an important socio-economic pole with a strong need for water. Intense drought during the last years has harmed the governorate’s activities. Therefore, groundwater exploitation is increasingly becoming necessary for ensuring sustainable development. It takes in-depth knowledge of aquifers to create an appropriate exploitation plan. In this context, the present study aimed for precise delineation of Mio–Plio–Quaternary aquifers in the central part of the Sousse governorate by thoroughly reconstructing their geometry and understanding their functioning. To reach this goal, a rich database, including 142 water boreholes and 123 vertical electrical soundings (VES), was used. Water borehole data containing lithological columns and their corresponding well logs as well as hydrodynamic and hydrochemical measurements were exploited. Second, VES data were interpreted and geoelectrical cross sections are accordingly established. Two aquifer formations showing variable arrangement were differentiated from well log analysis and correlation: AI and AII, which are Quaternary and Mio–Pliocene in age, respectively. Aquifer AI is absent at the Kalaa Kebira anticline, while on either side of this structure, both formations are present with deepening of aquifer AII. The established piezometric map exhibits groundwater flow toward the north and south of the Kalaa Kebira anticline. In the same directions, water salinity values increase gradually from 1 to 5 g/l. The geoelectrical cross sections highlighted that tectonic deformations control the water reservoirs arrangement and the groundwater circulation. Cross-comparison of the deduced information regarding the aquifers geometry, hydrodynamics, and water quality brings new elements to the hydrogeological scheme in the central part of the Sousse governorate. The Mio–Plio–Quaternary deposits encompass two multilayered aquifers, which are the Balaoum–Sidi Bou Ali aquifer to the north and the Oued Laya aquifer to the south. These aquifers are juxtaposed with a groundwater divide at the Kalaa Kebira anticline. The present study will guide groundwater exploitation in the Sousse governorate and thereby support sustainable development in the Tunisian Sahel. More broadly, it constitutes a model of hydrogeophysical application for better groundwater management in other arid regions.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"59 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143055118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Learning-Based Surrogate-Assisted Intelligent Optimization Framework for Reservoir Production Schemes","authors":"Lian Wang, Hehua Wang, Liehui Zhang, Liang Zhang, Rui Deng, Bing Xu, Xing Zhao, Chunxiang Zhou, Li Fan, Xindong Lv, Junda Wu","doi":"10.1007/s11053-025-10458-1","DOIUrl":"https://doi.org/10.1007/s11053-025-10458-1","url":null,"abstract":"<p>Determination of reservoir production schemes has always been a difficult problem during the close-loop management of waterflooding reservoir. Different well control results in significant influence on production, water breakthrough time and recovery rate of producing wells, especially in heterogeneous reservoirs. To optimize well controls, a new method using transpose convolution neural network (TCNN) surrogate model and adaptive differential evolution with optional external archive (JADE) algorithm was introduced. In this method, the TCNN surrogate model, which uses image processing, took well controls (i.e., bottom hole pressure and injection rate) and production time as parameters to predict oil saturation and pressure distribution fields at different time periods. It could well replace a numerical simulator, accurately predict the regional production dynamics at different production time steps, and significantly reduce the simulation time during the optimization process. Meanwhile, the JADE algorithm, as an improved differential evolution algorithm, greatly improved the convergence rate while ensuring the search breadth and it was suitable for solving multi-parameter well control optimization problems. Using a comprehensive reservoir optimization problem as an example, the selection and setting of some parameters during the TCNN training and JADE optimization are discussed. Finally, the method was applied to a real 3D reservoir. The computational speed of the TCNN model was about 3600 times and 2300 times faster than that of a numerical simulation model for the synthetic reservoir and L43 block, respectively.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"34 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143031013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced Lithology Classification Using an Interpretable SHAP Model Integrating Semi-Supervised Contrastive Learning and Transformer with Well Logging Data","authors":"Youzhuang Sun, Shanchen Pang, Hengxiao Li, Sibo Qiao, Yongan Zhang","doi":"10.1007/s11053-024-10452-z","DOIUrl":"https://doi.org/10.1007/s11053-024-10452-z","url":null,"abstract":"<p>In petroleum and natural gas exploration, lithology identification—analyzing rock types beneath the Earth’s surface—is crucial for assessing hydrocarbon reservoirs and optimizing drilling strategies. Traditionally, this process relies on logging data such as gamma rays and resistivity, which often require manual interpretation, making it labor-intensive and prone to errors. To address these challenges, we propose a novel machine learning framework—contrastive learning-transformer—leveraging self-attention mechanisms to enhance the accuracy of lithology identification. Our method first extracts unlabeled samples from logging data while obtaining labeled core sample data. Through self-supervised contrastive learning and a transformer backbone network, we optimize performance using techniques like batch normalization. After pretraining, the model is fine-tuned with a limited number of labeled samples to improve accuracy and significantly reduce reliance on large labeled datasets, thereby lowering the costs associated with drilling core annotations. Additionally, our research incorporates shapley additive explanations (SHAP) technology to enhance the transparency of the model’s decision-making process, facilitating the analysis of the contribution of each feature to lithology predictions. The model also learns time-reversal invariance by reversing sequential data, ensuring reliable identification even with variations in data sequences. Experimental results demonstrate that our transformer model, combined with semi-supervised contrastive learning, significantly outperforms traditional methods, achieving more precise lithology identification, especially in complex geological environments.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"60 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142987613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Novel Framework for Optimizing the Prediction of Areas Favorable to Porphyry-Cu Mineralization: Combination of Ant Colony and Grid Search Optimization Algorithms with Support Vector Machines","authors":"Sarina Akbari, Hamidreza Ramazi, Reza Ghezelbash","doi":"10.1007/s11053-024-10431-4","DOIUrl":"https://doi.org/10.1007/s11053-024-10431-4","url":null,"abstract":"<p>In the realm of mineral prospectivity mapping, a novel hybrid approach for optimizing hyperparameters of the support vector machine (SVM) algorithm is proposed here. The concept of ant colony optimization (ACO) algorithm, inspired by collective intelligence of ant colonies, and grid search (GS) that systematically evaluate all hyperparameter combinations to find the optimal model configuration are leveraged to fine-tune SVM parameters, enhancing its predictive capabilities. A dataset comprising geophysical, geochemical, geological, tectonic, and remote sensing evidence layers from the Sardouyeh region in Kerman province, Iran, is utilized for model development aimed the prediction of areas favorable for porphyry-Cu mineralization. After generating the regular and tuned predictive models, a comparison was carried out using quantitative performance metrics such as confusion matrix and success rate curves. The results demonstrated that the optimized versions of SVM using ACO (ACO–SVM) and GS (GS–SVM) models exhibit superior performance, achieving better accuracy and predictive capability in identifying locations favorable for porphyry-Cu mineralization. The study highlights the potential of incorporating optimization algorithms, especially ACO, into SVM, leading to the development of more effective predictive models for mineral prospectivity mapping.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"9 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142961771","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Small-Sample InSAR Time-Series Data Prediction Method Based on Generative Models","authors":"Yuchen Han, Xuexiang Yu, Jiajia Yuan, Mingfei Zhu, Shicheng Xie","doi":"10.1007/s11053-024-10434-1","DOIUrl":"https://doi.org/10.1007/s11053-024-10434-1","url":null,"abstract":"<p>In surface deformation monitoring for mining areas, interferometric synthetic aperture radar (InSAR) technology has become a popular research topic due to its efficiency and high accuracy. However, transforming temporal monitoring data into surface deformation predictions remains challenging. In practical applications, InSAR data often face limitations like low acquisition frequency and insufficient data volume, leading to prediction models being prone to overfitting and having poor accuracy. Therefore, this paper proposes an improved temporal convolutional network (TCN) time-series generative adversarial network (GAN) with an attention mechanism, called the Attention–TCN–TimeGAN, to enhance InSAR surface deformation data for better prediction results. By combining the embedding, recovery, generator, and discriminator networks, we used the TCN to expand the receptive field and capture long-term temporal features. Additionally, we integrated the self-attention mechanism into the generator and discriminator to adapt to random vectors, achieving better data generation results. The loss function uses the Wasserstein distance to measure the original data distribution and adds a gradient penalty term with adaptive weights to achieve effective feature extraction from time-series data. Experimental results show that the data generated by our model more comprehensively cover the original data distribution. The prediction results at four test points showed the lowest mean absolute error and mean-squared error and the highest coefficient of determination (R²). These results demonstrate the effectiveness of our generative model in predicting small-sample InSAR time-series data, providing a new method for surface deformation monitoring.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"99 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142936089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring the Dynamic Evolution of Shallow and Deep Coal Nanopore Structures Under Acidic Fracturing Fluids Using Synchrotron Radiation Small-Angle X-Ray Scattering","authors":"Yingfeng Sun, Shuaipeng Zhu, Hui Wang, Yixin Zhao, Fei Xie, Ping Chen, Changjiang Ji, Zhaoying Chen, Qifei Wang","doi":"10.1007/s11053-024-10449-8","DOIUrl":"https://doi.org/10.1007/s11053-024-10449-8","url":null,"abstract":"<p>Acid fracturing technology is one of the most effective methods for resolving mineral plugging and for improving the pore structure of coal reservoirs. To investigate the characteristics of shallow and deep coal nanopore structures under the influence of acidic fracturing fluids, experiments using synchrotron radiation small-angle X-ray scattering were conducted on shallow and deep coal samples soaked in acidic fracturing fluids of different concentrations for varying durations. This quantitatively characterized the different nanoscale pore scattering intensity ratios (<i>A</i>_<i>I</i>), fractal dimensions, and nanopore parameters. The research indicates that, under the influence of acidic fracturing fluids, the shallow coal nanopore structure tends to become more complex while that of deep coal becomes simpler. The impact of 20% acidic fracturing fluid is greatest on shallow coal nanopore structure, while deep coal nanopore structure is more susceptible to 12% acidic fracturing fluid, with these effects primarily concentrated in the 2–10 nm pores. Acidic fracturing fluids primarily affect the shallow and deep coal nanopore structures by dissolving, among others, carbonate minerals, pyrite, and clay minerals, resulting in the dynamic evolution of the shallow and deep coal nanopore structures during the soaking process.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"98 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142934905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Novel Approach for Enhancing Geologically Aligned Fusion of Multiple Geophysical Inverse Models in the Porphyry-Cu Deposit of Zaftak, Kerman, Iran","authors":"Morteza Erfanian-Norouzzadeh, Nader Fathianpour","doi":"10.1007/s11053-024-10447-w","DOIUrl":"https://doi.org/10.1007/s11053-024-10447-w","url":null,"abstract":"<p>The simultaneous interpretation of multiple geophysical data through their inverted models of various physical properties of subsurface geological structures and formations related to mineral deposits is a challenging task in mineral exploration. In this paper, a three-dimensional fusion algorithm based on the use of a two-dimensional contourlet transform for concurrent interpretation of multiple geophysical models is proposed. To achieve this, initially, a synthetic model based on a general structure simulating the spatial distribution of physical and geological properties of typical porphyry-Cu deposits using a mineral exploration database is generated, and the results of applying the proposed algorithm to this model are presented. Subsequently, the proposed algorithm is implemented on the Zaftak porphyry-Cu deposit in the southern part of Kerman Province in southern Iran. For this purpose, two fusion models with different contourlet decomposition levels are compared through their consistency with the geological settings of the study area to select the best fusion model using two well-known consistency analyses known as Jensen–Shannon divergence index and BLOB Analysis score. Moreover, the fusion models with 2 and 3 contourlet decomposition levels are compared based on available exploratory data. Finally, based on the validation and conformity of the fused model with the available exploratory borehole data and the geology of the study area, a suitable match for the three-dimensional fused model using two-dimensional contourlet transform with the Jensen–Shannon divergence index of 95.13% and a BLOB Analysis score of 4.68 was found.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"13 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142917159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Novel Multifractal Method for Geochemical Element Distribution Analysis","authors":"Mengyu Zhao, Yi Jin, Jiabin Dong, Junling Zheng, Qinglin Xia","doi":"10.1007/s11053-024-10450-1","DOIUrl":"https://doi.org/10.1007/s11053-024-10450-1","url":null,"abstract":"<p>Accurately analysis of the multifractal characteristics of geochemical element distribution is crucial for identifying geochemical anomalies and meaningful element associations. However, the most commonly used multifractal method, i.e., the method of moments, may generate different multifractal spectra for a single element distribution due to variations in the range of moment orders. This is because multifractals and their control mechanisms are not well defined. Fractal topography provides a basis for defining multifractals and clarifies the physical meaning of the singularity index. Therefore, a multifractal analysis method based on fractal topography is proposed to generate a unified multifractal spectrum and give new insight into the singularity analysis of element distribution. The similarities and distinctions between the two methods were evaluated using the de Wijs model. The distributions of two multifractal spectra are shown to be fundamentally consistent. The novel method, nevertheless, utilizes fewer statistics and presents a simplified criterion for element enrichment or depletion. To demonstrate its application, Cu geochemical distribution in the Zhongdian area, China, was used as a case study. Based on the comparison results of the two approaches, the proposed novel approach proves beneficial for accurately characterizing the heterogeneity of geochemical element distribution while maintaining a consistent range of the singularity index. The singularity index distribution map at a fine scale provides a comprehensively detailed zonation of geochemical anomalies and, at different scales, it can effectively reveal and interpret the variation of element distribution.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"4 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142917160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}