Applied Computing and Geosciences最新文献

{"title":"Seismic Intelligence Tool: an extensive multipurpose software for seismic signal analysis","authors":"Andrea Bono","doi":"10.1016/j.acags.2025.100241","DOIUrl":"10.1016/j.acags.2025.100241","url":null,"abstract":"<div><div>Over the past five years, the <em>Istituto Nazionale di Geofisica e Vulcanologia</em> (INGV) has started a technological transformation of its real-time seismic monitoring capabilities. This comprehensive restructuring initiative represents a pivotal moment in the Institute's commitment to advancing seismic research and enhancing public safety. At the heart of this transformation lies the development and deployment of the integrated system known as <em>Caravel</em>. Caravel stands as a testament to INGV's dedication to cutting-edge seismic monitoring technology and its mission to provide timely and accurate seismic information to researchers, emergency responders, and the general public. It represents a leap forward in real-time seismic monitoring, integrating state-of-the-art technologies and methodologies to detect, analyze, and disseminate seismic data with unprecedented efficiency and precision. This development reflects INGV's commitment to staying at the forefront of seismic research and hazard mitigation. This integrated system not only improves the accuracy of earthquake detection but also enhances our ability to rapidly assess the potential impact of seismic events, enabling more informed decision-making during emergency situations. <em>Seismic Intelligence Tool (SIT)</em> emerges as a software <em>fork</em> from one of Caravel's components previously known as <em>PickFX</em>. The reason behind this fork is to share with the scientific community a robust, multi-platform and freely accessible data analysis tool that adheres to current standards for representing seismic data while removing all INGV specific customizations from PickFX. The decision to fork the original software and release SIT underscores a commitment to democratizing access to advanced seismic analysis tools. By offering this resource at no cost, the scientific community gains access to a platform that is fully compatible with contemporary seismic data representation standards and that can become very powerful with time and cooperation. This endeavor not only promotes open access to critical seismic research tools but also facilitates collaboration and knowledge sharing among researchers, ultimately contributing to advancements in our understanding of seismic activity and its implications.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"26 ","pages":"Article 100241"},"PeriodicalIF":2.6,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143858828","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":"Examining the impacts of salt precipitation on soil hydraulic properties at the field lysimeter scale","authors":"Qian Liu ,&nbsp;Yanfeng Liu ,&nbsp;Menggui Jin ,&nbsp;Jinlong Zhou ,&nbsp;Paul A. Ferré","doi":"10.1016/j.acags.2025.100238","DOIUrl":"10.1016/j.acags.2025.100238","url":null,"abstract":"<div><div>Previous bench-scale investigations have demonstrated that salt precipitation reduces soil saturated hydraulic conductivity (<em>K</em>_s) due to the clogging effect. However, this conclusion may be confounded by the boundary effects inherent to the physical model. While existing field-scale studies have primarily focused on water-solute migration by directly assuming that salt precipitation reduces <em>K</em>_s, systematic investigations examining how salt crystallization alters soil hydraulic properties remain scarce. This study employed a time-windowed inverse method to analyze one set of data from four lysimeters supplied through the bottom with NaCl solution at concentrations of 3, 30, 100, and 250 g/L under field condition, aiming to examine: (1) whether the salt precipitation impacts the soil hydraulic properties; and (2) whether the degree of this impact depends on the water salinity. Results in each column showed that the inverse-derived <em>K</em>_s unexpectedly increased by more than 50 % at the intermediate time and then decreased to its early-time value. This trend in inverse-derived <em>K</em>_s showed a strong positive correlation with the ambient evaporation rate. Based on measurements of bottom fluxes and ambient evaporation, these opposing trends in inverse-derived <em>K</em>_s are primarily ascribed to the actual <em>K</em>_s change caused by the salt precipitation, rather than variations in salt crust-soil surface hydraulic connectivity (which also affect effective <em>K</em>_s). These findings highlight the need for future experiments to investigate salt precipitation-induced soil pore structure changes under varying evaporation intensities and across multiple scales.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"26 ","pages":"Article 100238"},"PeriodicalIF":2.6,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143821294","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":"Analyzing expert decision-making in geosteering: Statistical insights from a large-scale controlled experiment","authors":"Yasaman Cheraghi ,&nbsp;Sergey Alyaev ,&nbsp;Reidar B. Bratvold ,&nbsp;Aojie Hong ,&nbsp;Igor Kuvaev ,&nbsp;Stephen Clark ,&nbsp;Andrei Zhuravlev","doi":"10.1016/j.acags.2025.100237","DOIUrl":"10.1016/j.acags.2025.100237","url":null,"abstract":"<div><div>Geosteering is a sequential decision-making process used in the oil and gas industry which adjusts and controls the drilling trajectory of a well in real time, aimed at maximizing values derived from hydrocarbon production operations. For layered geological formations, Stratigraphy-Based Steering (SBS) has emerged as a popular approach to generate decision-supporting information to guide steering horizontal wells. This method involves the interpretation of log data measure while drilling, the development of a geomodel around the wellbore based on the log interpretation, and the use of the geomodel to guide well placement decisions. However, the main challenge in geosteering is that it is often not approached as a structured decision-making process. Consequently, essential decision quality elements—such as defining clear objectives and their trade-offs, alternatives, and properly quantifying uncertainties—are often missing. This issue causes a lack of unique and standard guidelines for geosteering practices.</div><div>This paper presents an analysis of data collected from 349 participants of a controlled geosteering experiment – the Rogii Geosteering World Cup (GWC) 2021. The data consists of log interpretations and geosteering decisions made by the participants, acting as geosteerers, for two wells representing conventional and unconventional drilling operations. More than 10,000 snapshots were recorded, consisting of interpretations of log data for each participant's well and corresponding decisions, every 2 min. These snapshots form a comprehensive database that is useful and valuable to provide insights into the decision-making process of the geosteerers and learning for improving geosteering decision-making. The dataset utilized in this study is openly accessible and published alongside the paper.</div><div>The novelties and key contributions of this paper are (1) a statistical analysis of recorded data to investigate causation and correlation between geosteering decisions and the quality of well placements, (2) revealing the factors that contribute to good geosteering decisions and well placements and (3) evaluating the extent to which good well placements are the result of interpretation and decision-making skills versus luck. By conducting a comprehensive statistical analysis of the recorded data, this study provides insights into the geosteering decision-making process and identifies key factors that are likely to contribute to favorable outcomes.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"26 ","pages":"Article 100237"},"PeriodicalIF":2.6,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143825510","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":"Advanced AI techniques for landslide susceptibility mapping and spatial prediction: A case study in Medellín, Colombia","authors":"I.N. Gómez-Miranda ,&nbsp;C. Restrepo-Estrada ,&nbsp;A. Builes-Jaramillo ,&nbsp;João Porto de Albuquerque","doi":"10.1016/j.acags.2025.100226","DOIUrl":"10.1016/j.acags.2025.100226","url":null,"abstract":"<div><div>Landslides, a global phenomenon, significantly impact economies and societies, especially in densely populated areas. Effective mitigation requires awareness of landslide risks, yet temporal links between occurrences are often neglected, challenging model performance due to non-stationary triggering and predisposing factors. This study presents a novel landslide susceptibility model that incorporates spatial and temporal dependencies, including landslide recurrence. We applied AI models — Naive Bayes, Linear Discriminant Analysis, Quadratic Discriminant Analysis, Decision Trees, Random Forest, and Support Vector Machine (SVM) — to a case study in Medellín, a mountainous city in northwest Colombia. Using heuristic methods, we evaluated geological and geomorphological characteristics to identify high-risk areas. Integrating temporal data from four consecutive periods allowed us to enhance estimation robustness by incorporating random effects. Our findings identify slope, stream distance, geology, geomorphology, and mean annual precipitation as key factors influencing landslide susceptibility in Medellín. The SVM model demonstrated superior performance with an accuracy of 85%, closely aligning with previous studies. This research underscores the importance of temporal dynamics in landslide susceptibility assessments, improving prediction accuracy and supporting more effective risk management.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"25 ","pages":"Article 100226"},"PeriodicalIF":2.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143387194","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":"Land use and land cover classification for change detection studies using convolutional neural network","authors":"V. Pushpalatha ,&nbsp;P.B. Mallikarjuna ,&nbsp;H.N. Mahendra ,&nbsp;S. Rama Subramoniam ,&nbsp;S. Mallikarjunaswamy","doi":"10.1016/j.acags.2025.100227","DOIUrl":"10.1016/j.acags.2025.100227","url":null,"abstract":"<div><div>Efficient land use land cover (LULC) classification is crucial for environmental monitoring, urban planning, and resource management. This study investigates LULC changes in Nanjangud taluk, Mysuru district, Karnataka, India, using remote sensing (RS) and geographic information systems (GIS). This paper mainly focuses on the classification and change detection analysis of LULC in 2010 and 2020 using linear imaging self-scanning sensor-III (LISS-III) remote sensing images. Traditional methods for LULC classification involve manual interpretation of satellite images, which provides lower accuracy. Therefore, this paper proposed the Convolutional Neural Network (CNN)-based deep learning method for LULC classification. The main objective of the research work is to perform an efficient LULC classification for the change detection study of the Nanjagud taluk using the classified maps of the years 2010 and 2020. The experimental results indicate that the proposed classification method is outperformed, with an overall accuracy of 94.08% for the 2010 data and 95.30% for the 2020 data. Further, change detection analysis has been carried out using classified maps and the results show that built-up areas increased by 8.34 sq. km (0.83%), agricultural land expanded by 2.21 sq. km (0.23%), and water bodies grew by 3.31 sq. km (0.35%). Conversely, forest cover declined by 1.49 sq. km (0.15%), and other land uses reduced by 11.93 sq. km (1.22%) over the decade.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"25 ","pages":"Article 100227"},"PeriodicalIF":2.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143166134","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":"Pymaginverse: A python package for global geomagnetic field modeling","authors":"Frenk Out ,&nbsp;Maximilian Schanner ,&nbsp;Liz van Grinsven ,&nbsp;Monika Korte ,&nbsp;Lennart V. de Groot","doi":"10.1016/j.acags.2025.100222","DOIUrl":"10.1016/j.acags.2025.100222","url":null,"abstract":"<div><div>Data-based geomagnetic models are key for mapping the global field, predicting the movement of magnetic poles, understanding the complex processes happening in the outer core, and describing the global expression of magnetic field reversals. There exists a wide range of models, which differ in a priori assumptions and methods for spatio-temporal interpolation. A frequently used modeling procedure is based on regularized least squares (RLS) spherical harmonic analysis, which has been used since the 1980s. The first version of this algorithm has been written in Fortran and inspired many different research groups to produce versions of the algorithm in other programming languages, either published open-access or only accessible within the institute. To open up the research field and allow for reproducibility of results between existing versions, we provide a user-friendly open-source Python version of this popular algorithm. We complement this method with an overview on background literature – concerning Maxwells equations, spherical harmonics, cubic B-Splines, and regularization – that forms the basis for RLS geomagnetic models. We included six spatial and two temporal damping methods from literature to further smooth the magnetic field in space and time. Computational resources are kept to a minimum by employing the banded structure of the normal equations involved and incorporating C-code (with Cython) for matrix formation, enabling a massive speed-up. This ensures that the algorithm can be executed on a simple laptop, and is as fast as its Fortran predecessor. Four tutorials with ample examples show how to employ the new lightweight and quick algorithm. With this properly documented open-source Python algorithm, we have the intention to encourage current and new users to employ and further develop the method.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"25 ","pages":"Article 100222"},"PeriodicalIF":2.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143165476","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":"Automatic variogram inference using pre-trained Convolutional Neural Networks","authors":"Mokdad Karim ,&nbsp;Koushavand Behrang ,&nbsp;Boisvert Jeff","doi":"10.1016/j.acags.2025.100219","DOIUrl":"10.1016/j.acags.2025.100219","url":null,"abstract":"<div><div>A novel approach is presented for inferring covariance functions from sparse data using Convolutional Neural Networks (CNNs). Two workflows are proposed: (1) direct prediction of variogram model parameters, and (2) prediction of experimental variogram values at specified lag distances, which are smooth and easily autofit. Workflow 1 achieves an r-squared of 0.80, while Workflow 2 attains a higher r-squared of 0.96. Data augmentation through rotation improves robustness, and can be used to examine variogram uncertainty; the distribution for each predicted parameter can be obtained and used in uncertainty modeling. The CNNs are pre-trained, ensuring minimal computational time and fully automated processing. The workflows are applicable to sparse or dense data but are currently limited to 2D normal score variograms.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"25 ","pages":"Article 100219"},"PeriodicalIF":2.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143165477","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":"Developing ground motion prediction models for West Java: A machine learning approach to support Indonesia's earthquake early warning system","authors":"Andy Rachmadan,&nbsp;Ardiansyah Koeshidayatullah,&nbsp;SanLinn I. Kaka","doi":"10.1016/j.acags.2024.100212","DOIUrl":"10.1016/j.acags.2024.100212","url":null,"abstract":"<div><div>Indonesia, one of the most earthquake-prone countries in the world, is currently developing an Earthquake Early Warning (EEW) system. A key component of this system, the Regional EEW, relies on Ground Motion Prediction models (GMPMs) to issue end-user alerts. However, in West Java, one of the pilot regions for this project, there is a lack of region-specific GMPMs essential for accurate early warnings. Traditionally, GMPMs are developed using linear regression based on complex, predefined mathematical equations and coefficients. However, Machine learning offers the advantages of bypassing the need for predefined equations and effectively capturing the nonlinear behavior present in ground motion data. To address this gap, we evaluated three machine learning algorithms (i.e. Artificial Neural Network [ANN], Gradient Boosting [GB], and Random Forest [RF]) to develop GMPMs for three tectonic categories: shallow-crustal, interface, and intraslab. These models were used to predict Peak Ground Acceleration (PGA) in West Java, utilizing 3116 strong ground motion records from 365 earthquakes with moment magnitude ranging from 2.4 to 7 and epicentral distance between 5.5 and 867 km, recorded since 2010. Our results show that The Gradient Boosting model outperformed the others across all three tectonic categories, with the lowest Mean Squared Error values (0.94, 0.60, 0.65), and Standard Deviation of Residuals (0.97, 0.77, 0.80), as well as the highest Pearson correlation coefficient-value (0.83, 0.88, 0.90) for shallow-crustal, interface, and intraslab events, respectively, demonstrating strong accuracy in predicting PGA. The model was further validated with recent earthquake data and from 2024 showing good agreement and confirming its robustness. Epicentral Distance and Moment Magnitude were the most influential in predicting PGA among the six explanatory variables used in this study. These findings highlight the potential of machine learning models to improve the accuracy of ground-shaking predictions, contributing to the success of Indonesia's Earthquake Early Warning System (EEWS).</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"25 ","pages":"Article 100212"},"PeriodicalIF":2.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143166120","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":"Irrigated rice-field mapping in Brazil using phenological stage information and optical and microwave remote sensing","authors":"Andre Dalla Bernardina Garcia ,&nbsp;MD Samiul Islam ,&nbsp;Victor Hugo Rohden Prudente ,&nbsp;Ieda Del’Arco Sanches ,&nbsp;Irene Cheng","doi":"10.1016/j.acags.2025.100223","DOIUrl":"10.1016/j.acags.2025.100223","url":null,"abstract":"<div><div>Irrigated rice-field mapping methodologies have been rapidly evolving as a result of advanced remote sensing (RS) technology. However, current methods rely on extensive time-series data and a wide range of multi-spectral bands. These methods often struggle with classification accuracy with contaminated satellite data due to environmental factors or acquisition device constraints, e.g., cloud cover, shadows, noise, and the temporal and spectral resolution trade-off. Our goal is map irrigated rice-field by using a suitable satellite image band composition instead of time-series data. We divide the growth cycle into different rice phenological stages: beginning, middle and end of season, as well as the season transition periods. Near-infrared (NIR), short-wave infrared (SWIR) and red bands of MultiSpectral Instrument - MSI/Sentinel-2 (optical RS), along with polarizations of VV (vertical–vertical) and VH (vertical–horizontal) of Sentinel-1 C-band Synthetic Aperture Radar (SAR) (microwave RS), were used to create ten different false-color image composites. Ground truth maps from two consecutive growth seasons (2017/2018 and 2018/2019) served as references. We applied a modified version of the Fusion Adaptive Patch Network (FAPNET), named as Patch Layer Adaptive Network (PLANET) convolutional neural network (CNN) to obtain binary rice mapping, which was evaluated using the traditional Mean Intersection over Union (MIoU) and Dice coefficient. Analytic results show that the end of season is the most suitable for obtaining a reliable classification based on optical and SAR sensors. Although complex rice-field pose challenges, our predictions consistently scored a MIoU above 0.9. We conclude that choosing the right phenological stage for rice mapping combined with deep learning model can greatly improve the classification results. These results indicate that the choice of composition significantly impacts classification accuracy, especially in more complex environments.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"25 ","pages":"Article 100223"},"PeriodicalIF":2.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143165475","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":"Deformation analysis by an improved similarity transformation","authors":"Vahid Mahboub","doi":"10.1016/j.acags.2025.100221","DOIUrl":"10.1016/j.acags.2025.100221","url":null,"abstract":"<div><div>In this contribution, deformation analysis is rigorously performed by a non-linear 3-D similarity transformation. In contrast to traditional methods based on linear least-squares (LLS), here we solve a non-linear problem without any linearization. To achieve this goal, a new weighted total least-squares (WTLS) approach with general dispersion matrix is implemented to deformation analysis problem. Although some researchers have been trying to solve deformation analysis using TLS approaches, these attempts require modification since they used to apply unstructured TLS techniques such as Generalized TLS (GTLS) to similarity transformation which requires structured TLS (STLS) techniques while the WTLS approach preserves the structure of the functional model when based on the perfect description of the variance-covariance matrix. As a secondary scope, here it is analytically proved that LLS is not identical to nonlinear estimations such as the WTLS methods and rigorous nonlinear least-square (RNLS) as opposed to what in some contributions has been claimed. The third attainment of this contribution is proposing another algorithm for rigorous similarity transformation with arbitrary rotational angles. It is based on the RNLS method which can obtain the correct update of misclosure. Moreover, compared to transformation methods that deal with arbitrary rotational angles, we do not need to impose any orthogonality constraints here. Two case studies numerically confirm that the WTLS and RNLS methods provide the most accurate results among the LLS, GTLS, RNLS and WTLS approaches in two landslide areas.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"25 ","pages":"Article 100221"},"PeriodicalIF":2.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143165022","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}