Applied Computing and Geosciences最新文献

{"title":"Semantically triggered qualitative simulation of a geological process","authors":"Yuanwei Qu ,&nbsp;Eduard Kamburjan ,&nbsp;Anita Torabi ,&nbsp;Martin Giese","doi":"10.1016/j.acags.2023.100152","DOIUrl":"10.1016/j.acags.2023.100152","url":null,"abstract":"<div><p>The field of geology has been the subject of a range of research efforts aiming to formalize geological domain knowledge, notably through geological domain ontologies. The main focus of existing geological ontologies primarily lies in describing static geological objects and their properties, paying less attention to the knowledge concerning geological processes and events. Meanwhile, the geological process modeling and simulation predominantly rely on quantitative numerical approaches that necessitate comprehensive and abundant data as input. However, many geological processes took place on a million-year time scale with insufficient data and non-direct observations. Given the inherent incompleteness of geological data, geologists still rely on qualitative reasoning to validate their interpretations. There is currently a dearth of applicable methods to facilitate qualitative reasoning and simulate geological processes based on domain knowledge.</p><p>We propose to model the <em>effects</em> of a geological process through an object-oriented program, while keeping an ontological representation of the situation at each instant. To combine the two models, we propose using semantically defined ‘process triggers.’ These process triggers are defined as part of the ontology, in accordance with the Basic Formal Ontology. They enable geologists to describe the precise moment when a geological process is triggered and initiated. On the computational program side, we employ the ‘Semantic Micro Object Language’ to embody the knowledge and rules provided by geologists, facilitating the simulation of geological processes. Through an evaluation experiment, our proposed approach demonstrates promising results within a reasonable timeframe. As proof of concept, we have applied our method to a real-world scenario of petroleum thermal maturation in Ekofisk Field and got a promising result. Our approach provides a formalism that allows a powerful code to interact with domain ontologies, which paves the path for future knowledge reasoning.</p></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"21 ","pages":"Article 100152"},"PeriodicalIF":3.4,"publicationDate":"2024-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2590197423000411/pdfft?md5=b66838385e92e512aa61f6e7d3206e31&pid=1-s2.0-S2590197423000411-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139392217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Knowledge graphs for seismic data and metadata","authors":"William Davis ,&nbsp;Cassandra R. Hunt","doi":"10.1016/j.acags.2023.100151","DOIUrl":"10.1016/j.acags.2023.100151","url":null,"abstract":"<div><p>The increasing scale and diversity of seismic data, and the growing role of big data in seismology, has raised interest in methods to make data exploration more accessible. This paper presents the use of knowledge graphs (KGs) for representing seismic data and metadata to improve data exploration and analysis, focusing on usability, flexibility, and extensibility. Using constraints derived from domain knowledge in seismology, we define a semantic model of seismic station and event information used to construct the KGs. Our approach utilizes the capability of KGs to integrate data across many sources and diverse schema formats. We use schema-diverse, real-world seismic data to construct KGs with millions of nodes, and illustrate potential applications with three big-data examples. Our findings demonstrate the potential of KGs to enhance the efficiency and efficacy of seismological workflows in research and beyond, indicating a promising interdisciplinary future for this technology.</p></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"21 ","pages":"Article 100151"},"PeriodicalIF":3.4,"publicationDate":"2024-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S259019742300040X/pdfft?md5=8efe415c8294c5af013a3cb4ee2f664c&pid=1-s2.0-S259019742300040X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139392428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using a 3D heat map to explore the diverse correlations among elements and mineral species","authors":"Jiyin Zhang ,&nbsp;Xiang Que ,&nbsp;Bhuwan Madhikarmi ,&nbsp;Robert M. Hazen ,&nbsp;Jolyon Ralph ,&nbsp;Anirudh Prabhu ,&nbsp;Shaunna M. Morrison ,&nbsp;Xiaogang Ma","doi":"10.1016/j.acags.2024.100154","DOIUrl":"https://doi.org/10.1016/j.acags.2024.100154","url":null,"abstract":"<div><p>This paper presents an enhanced 3D heat map for exploratory data analysis (EDA) of open mineral data, addressing the challenges caused by rapidly evolving datasets and ensuring scientifically meaningful data exploration. The Mindat website, a crowd-sourced database of mineral species, provides a constantly updated open data source via its newly established application programming interface (API). To illustrate the potential usage of the API, we constructed an automatic workflow to retrieve and cleanse mineral data from it, thus feeding the 3D heat map with up-to-date records of mineral species. In the 3D heat map, we developed scientifically sound operations for data selection and visualization by incorporating knowledge from existing mineral classification systems and recent studies in mineralogy. The resulting 3D heat map has been shared as an online demo system, with the source code made open on GitHub. We hope this updated 3D heat map system will serve as a valuable resource for researchers, educators, and students in geosciences, demonstrating the potential for data-intensive research in mineralogy and broader geoscience disciplines.</p></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"21 ","pages":"Article 100154"},"PeriodicalIF":3.4,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2590197424000016/pdfft?md5=0b52703561a3bfd2d7bf0ed0e4d6590e&pid=1-s2.0-S2590197424000016-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139111608","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neural network approach for shape-based euhedral pyrite identification in X-ray CT data with adversarial unsupervised domain adaptation","authors":"Suraj Neelakantan ,&nbsp;Jesper Norell ,&nbsp;Alexander Hansson ,&nbsp;Martin Längkvist ,&nbsp;Amy Loutfi","doi":"10.1016/j.acags.2023.100153","DOIUrl":"10.1016/j.acags.2023.100153","url":null,"abstract":"<div><p>We explore an attenuation and shape-based identification of euhedral pyrites in high-resolution X-ray Computed Tomography (XCT) data using deep neural networks. To deal with the scarcity of annotated data we generate a complementary training set of synthetic images. To investigate and address the domain gap between the synthetic and XCT data, several deep learning models, with and without domain adaption, are trained and compared. We find that a model trained on a small set of human annotations, while displaying over-fitting, can rival the human annotators. The unsupervised domain adaptation approaches are successful in bridging the domain gap, which significantly improves their performance. A domain-adapted model, trained on a dataset that fuses synthetic and real data, is the overall best-performing model. This highlights the possibility of using synthetic datasets for the application of deep learning in mineralogy.</p></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"21 ","pages":"Article 100153"},"PeriodicalIF":3.4,"publicationDate":"2024-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2590197423000423/pdfft?md5=b48cfaa3e867a2a2e72a1453cf13f16e&pid=1-s2.0-S2590197423000423-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139393213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GeoCoDA: Recognizing and validating structural processes in geochemical data. A workflow on compositional data analysis in lithogeochemistry","authors":"Eric Grunsky ,&nbsp;Michael Greenacre ,&nbsp;Bruce Kjarsgaard","doi":"10.1016/j.acags.2023.100149","DOIUrl":"https://doi.org/10.1016/j.acags.2023.100149","url":null,"abstract":"<div><p>Geochemical data are compositional in nature and are subject to the problems typically associated with data that are restricted to the real non-negative number space with constant-sum constraint, that is, the simplex. Geochemistry can be considered a proxy for mineralogy, comprised of atomically ordered structures that define the placement and abundance of elements in the mineral lattice structure. Based on the innovative contributions of John Aitchison, who introduced the logratio transformation into compositional data analysis, this contribution provides a systematic workflow for assessing geochemical data in a simple and efficient way, such that significant geochemical (mineralogical) processes can be recognized and validated. This workflow, called GeoCoDA and presented here in the form of a tutorial, enables the recognition of processes from which models can be constructed based on the associations of elements that reflect mineralogy. Both the original compositional values and their transformation to logratios are considered. These models can reflect rock-forming processes, metamorphism, alteration and ore mineralization. Moreover, machine learning methods, both unsupervised and supervised, applied to an optimized set of subcompositions of the data, provide a systematic, accurate, efficient and defensible approach to geochemical data analysis. The workflow is illustrated on lithogeochemical data from exploration of the Star kimberlite, consisting of a series of eruptions with five recognized phases.</p></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"22 ","pages":"Article 100149"},"PeriodicalIF":3.4,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2590197423000381/pdfft?md5=73c63e3085ea08dc140737cfd1aa2255&pid=1-s2.0-S2590197423000381-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140113714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative performance analysis of simple U-Net, residual attention U-Net, and VGG16-U-Net for inventory inland water bodies","authors":"Ali Ghaznavi ,&nbsp;Mohammadmehdi Saberioon ,&nbsp;Jakub Brom ,&nbsp;Sibylle Itzerott","doi":"10.1016/j.acags.2023.100150","DOIUrl":"10.1016/j.acags.2023.100150","url":null,"abstract":"<div><p>Inland water bodies play a vital role at all scales in the terrestrial water balance and Earth’s climate variability. Thus, an inventory of inland waters is crucially important for hydrologic and ecological studies and management. Therefore, the main aim of this study was to develop a deep learning-based method for inventorying and mapping inland water bodies using the RGB band of high-resolution satellite imagery automatically and accurately.</p><p>The Sentinel-2 Harmonized dataset, together with ZABAGED-validated ground truth, was used as the main dataset for the model training step. Three different deep learning algorithms based on U-Net architecture were employed to segment inland waters, including a simple U-Net, Residual Attention U-Net, and VGG16-U-Net. All three algorithms were trained using a combination of Sentinel-2 visible bands (Red [B04; 665nm], Green [B03; 560nm], and Blue [B02; 490 nm]) at a 10-meter spatial resolution.</p><p>The Residual Attention U-Net achieved the highest computational cost due to the increased number of trainable parameters. The VGG16-U-Net had the shortest run time and the lowest number of trainable parameters, attributed to its architecture compared to the simple and Residual Attention U-Net architectures, respectively. As a result, the VGG16-U-Net provided the best segmentation results with a mean-IoU score of 0.9850, a slight improvement compared to other proposed U-Net-based architectures.</p><p>Although the accuracy of the model based on VGG16-U-Net does not make a difference from Residual Attention U-Net, the computation costs for training VGG16-U-Net were dramatically lower than Residual Attention U-Net.</p></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"21 ","pages":"Article 100150"},"PeriodicalIF":3.4,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2590197423000393/pdfft?md5=e26e50e9fd7c6d7b45541d9f356c212b&pid=1-s2.0-S2590197423000393-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139015408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrigendum to ‘Parallel investigations of remote sensing and ground-truth lake Chad's level data using statistical and machine learning methods’ [Appl. Comput. Geosci. 20 (2023) 100135]","authors":"Kim-Ndor Djimadoumngar","doi":"10.1016/j.acags.2023.100141","DOIUrl":"10.1016/j.acags.2023.100141","url":null,"abstract":"","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"20 ","pages":"Article 100141"},"PeriodicalIF":3.4,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2590197423000307/pdfft?md5=e6efd8c63afb83e52ab8e0a17a1bf13b&pid=1-s2.0-S2590197423000307-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136127382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating Imputation Methods for rainfall data under high variability in Johor River Basin, Malaysia","authors":"Zulfaqar Sa’adi ,&nbsp;Zulkifli Yusop ,&nbsp;Nor Eliza Alias ,&nbsp;Ming Fai Chow ,&nbsp;Mohd Khairul Idlan Muhammad ,&nbsp;Muhammad Wafiy Adli Ramli ,&nbsp;Zafar Iqbal ,&nbsp;Mohammed Sanusi Shiru ,&nbsp;Faizal Immaddudin Wira Rohmat ,&nbsp;Nur Athirah Mohamad ,&nbsp;Mohamad Faizal Ahmad","doi":"10.1016/j.acags.2023.100145","DOIUrl":"https://doi.org/10.1016/j.acags.2023.100145","url":null,"abstract":"<div><p>Missing values in rainfall records might result in erroneous predictions and inefficient management practices with significant economic, environmental, and social consequences. This is particularly important for rainfall datasets in Peninsular Malaysia (PM) due to the high level of missingness that can affect the inherent pattern in the highly variable time series. In this work, 21 target rainfall stations in the Johor River Basin (JRB) with daily data between 1970 and 2015 were used to examine 19 different multiple imputation methods that were carried out using the Multivariate Imputation by Chained Equations (MICE) package in R. For each station, artificial missing data were added at rates of up to 5%, 10%, 20%, and 30% for different types of missingness, namely, Missing Completely At Random (MCAR), Missing At Random (MAR), and Missing Not At Random (MNAR), leaving the original missing data intact. The imputation quality was evaluated based on several statistical performance metrics, namely mean absolute error (MAE), root mean square error (RMSE), normalized root mean square error (NRMSE), Nash-Sutcliffe efficiency (NSE), modified degree of agreement (MD), coefficient of determination (R2), Kling-Gupta efficiency (KGE), and volumetric efficiency (VE), which were later ranked and aggregated by using the compromise programming index (CPI) to select the best method. The results showed that linear regression predicted values (<em>norm.predict</em>) consistently ranked the highest under all types and levels of missingness. For example, under MAR, MNAR, and MCAR, this method showed the lowest MAE values, ranging between 0.78 and 2.25, 0.93–2.57, and 0.87–2.43, respectively. It also consistently shows higher NSE and R2 values of 0.71–0.92, 0.6–0.92, and 0.66–0.91, and 0.77–0.92, 0.71–0.93, and 0.75–0.92 under MAR, MCAR, and MNAR, respectively. The methods of <em>mean</em>, <em>rf</em>, and <em>cart</em> also appear to be efficient. The incorporation of the compromise programming index (CPI) as a decision-support tool has enabled an objective assessment of the output from the multiple performance metrics for the ranking and selection of the top-performing method. During validation, the Probability Density Function (PDF) demonstrated that even with up to 30% missingness, the shape of the distribution was retained after imputation compared to the actual data. The methodology proposed in this study can help in choosing suitable imputation methods for other tropical rainfall datasets, leading to improved accuracy in rainfall estimation and prediction.</p></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"20 ","pages":"Article 100145"},"PeriodicalIF":3.4,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2590197423000344/pdfft?md5=807ccb11378bbc7aafaff142104149e9&pid=1-s2.0-S2590197423000344-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138558749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AnnRG - An artificial neural network solute geothermometer","authors":"Lars H. Ystroem,&nbsp;Mark Vollmer,&nbsp;Thomas Kohl,&nbsp;Fabian Nitschke","doi":"10.1016/j.acags.2023.100144","DOIUrl":"https://doi.org/10.1016/j.acags.2023.100144","url":null,"abstract":"<div><p>Solute artificial neural network geothermometers offer the possibility to overcome the complexity given by the solute-mineral composition. Herein, we present a new concept, trained from high-quality hydrochemical data and verified by <em>in-situ</em> temperature measurements with a total of 208 data pairs of geochemical input parameters (Na⁺, K⁺, Ca²⁺, Mg²⁺, Cl⁻, SiO₂, and pH) and reservoir temperature measurements being compiled. The data comprises nine geothermal sites with a broad variety of geochemical characteristics and enthalpies. Five sites with 163 samples (Upper Rhine Graben, Pannonian Basin, German Molasse Basin, Paris Basin, and Iceland) are used to develop the ANN geothermometer, while further four sites with 45 samples (Azores, El Tatio, Miavalles, and Rotorua) are used to encounter the established artificial neural network in practice to unknown data. The setup of the application, as well as the optimisation of the network architecture and its hyperparameters, are stepwise introduced. As a result, the solute ANN geothermometer, AnnRG (Artificial neural network Regression Geothermometer), provides precise reservoir temperature predictions (RMSE of 10.442 K) with a high prediction accuracy of R² = 0.978. In conclusion, the implementation and verification of the first adequate ANN geothermometer is an advancement in solute geothermometry. Our approach is also a basis for further broadening and refining applications in geochemistry.</p></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"20 ","pages":"Article 100144"},"PeriodicalIF":3.4,"publicationDate":"2023-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2590197423000332/pdfft?md5=44b6e2e297c5c6c3291a38dab912498a&pid=1-s2.0-S2590197423000332-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136696934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comparative analysis of super-resolution techniques for enhancing micro-CT images of carbonate rocks","authors":"Ramin Soltanmohammadi,&nbsp;Salah A. Faroughi","doi":"10.1016/j.acags.2023.100143","DOIUrl":"10.1016/j.acags.2023.100143","url":null,"abstract":"<div><p>High-resolution digital rock micro-CT images captured from a wide field of view are essential for various geosystem engineering and geoscience applications. However, the resolution of these images is often constrained by the capabilities of scanners. To overcome this limitation and achieve superior image quality, advanced deep learning techniques have been used. This study compares four different super-resolution techniques, including super-resolution convolutional neural network (SRCNN), efficient sub-pixel convolutional neural networks (ESPCN), enhanced deep residual neural networks (EDRN), and super-resolution generative adversarial networks (SRGAN) to enhance the resolution of micro-CT images obtained from heterogeneous porous media. Our investigation employs a dataset consisting of 5000 micro-CT images acquired from a highly heterogeneous carbonate rock. The performance of each algorithm is evaluated based on its accuracy to reconstruct the pore geometry and connectivity, grain-pore edge sharpness, and preservation of petrophysical properties, such as porosity. Our findings indicate that EDRN outperforms other techniques in terms of the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) index, increased by nearly 4 dB and 17%, respectively, compared to bicubic interpolation. Furthermore, SRGAN exhibits superior performance compared to other techniques in terms of the learned perceptual image patch similarity (LPIPS) index and porosity preservation error. SRGAN shows a nearly 30% reduction in LPIPS compared to bicubic interpolation. Our results provide deeper insights into the practical applications of these techniques in the domain of porous media characterizations, facilitating the selection of optimal super-resolution CNN-based methodologies.</p></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"20 ","pages":"Article 100143"},"PeriodicalIF":3.4,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2590197423000320/pdfft?md5=ccbbe7617370fecf380cd2b36778bb1c&pid=1-s2.0-S2590197423000320-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135763903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}