Applied Computing and Geosciences最新文献

{"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":"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}

{"title":"Classification of geological borehole descriptions using a domain adapted large language model","authors":"Hossein Ghorbanfekr,&nbsp;Pieter Jan Kerstens,&nbsp;Katrijn Dirix","doi":"10.1016/j.acags.2025.100229","DOIUrl":"10.1016/j.acags.2025.100229","url":null,"abstract":"<div><div>Geological borehole descriptions contain detailed textual information about the composition of the subsurface. However, their unstructured format presents significant challenges for extracting relevant features into a structured format. This paper introduces GEOBERTje: a domain adapted large language model trained on geological borehole descriptions from Flanders (Belgium) in the Dutch language. This model effectively extracts relevant information from the borehole descriptions and represents it into a numeric vector space. Showcasing just one potential application of GEOBERTje, we finetune a classifier model on a limited number of manually labeled observations. This classifier categorizes borehole descriptions into a main, second and third lithology class. We show that our classifier outperforms a rule-based approach (by 30% on average), non-contextual Word2Vec embeddings combined with a random forest classifier (by 38% on average), and a prompt engineering method with large language models (i.e., GPT-4 (by 11% on average) and Gemma 2 (by 28% on average)). This study exemplifies how domain adapted large language models enhance the efficiency and accuracy of extracting information from complex, unstructured geological descriptions. This offers new opportunities for geological analysis and modeling using vast amounts of data.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"25 ","pages":"Article 100229"},"PeriodicalIF":2.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143520867","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":"Rapid mapping of landslides using satellite SAR imagery: A progressive learning approach","authors":"Nikhil Prakash ,&nbsp;Andrea Manconi ,&nbsp;Alessandro Cesare Mondini","doi":"10.1016/j.acags.2025.100224","DOIUrl":"10.1016/j.acags.2025.100224","url":null,"abstract":"<div><div>Rapid detection of landslides after an exceptional event is critical for planning effective disaster management. Previous works have typically used machine learning-based methods, including the recently popular deep-learning approaches, to identify characteristics surface features from satellite remote sensing data, especially from optical images. However, data acquisition from optical images is not possible in cloudy conditions, leading to unpredictable delays in any mapping task from future events. These methods also rely on large manually labelled inventories for training, which is often not available before the event. In this work, we propose an active training strategy to generate a landslide map after an event using the first available synthetic-aperture radar (SAR) image and improve it once subsequent cloud-free optical images are acquired. The proposed active learning workflow can start with a small (<span><math><mrow><mo>∼</mo><mn>100</mn><msup><mrow><mi>m</mi></mrow><mrow><mn>2</mn></mrow></msup></mrow></math></span>) and incomplete inventory,- and can grow the extent and completeness in iterative steps with manual updates after each step. This significantly reduces the slow manual mapping typically required for generating a large training inventory. We designed our experiments to map the landslides triggered by the <span><math><msub><mrow><mi>M</mi></mrow><mrow><mi>w</mi></mrow></msub></math></span> 6.6 Hokkaido Eastern Iburi earthquake of 2018 in Japan using sequentially ALOS-2 (SAR) and PlanetScope (Optical) scenes in the order they are acquired. The choice of active learning prioritizes speed over accuracy. However, we note only a modest reduction in performance (<span><math><mrow><mo>∼</mo><mn>10</mn><mtext>%</mtext></mrow></math></span> drop in F1 and MCC scores), with our method allowing a preliminary landslide inventory to be completed within a single day. This is of major importance in disaster response, improving performance and reducing the potential subjectivity associated with manual mapping.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"25 ","pages":"Article 100224"},"PeriodicalIF":2.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143395700","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":"Chemical map classification in XMapTools","authors":"Pierre Lanari ,&nbsp;Mahyra Tedeschi","doi":"10.1016/j.acags.2025.100230","DOIUrl":"10.1016/j.acags.2025.100230","url":null,"abstract":"<div><div>Chemical mapping using electron beam or laser instruments is an important analytical technique that allows the study of the compositional variability of materials in two dimensions. While quantitative compositional mapping of minerals has received considerable attention over the last two decades, pixel misclassification in commonly used software solutions remains a fundamental limitation affecting several applications. Calibration of intensity maps to fully quantitative compositional maps requires accurate classification, for example when a calibration curve is applied to a group of pixels that are assumed to have the same matrix behavior under the electron beam or the laser. This paper compares seven automated supervised machine learning classification algorithms implemented in the open source XMapTools software along with various tools for manual classification, for selecting training data and assessing the quality of a classification result. This new implementation aims to provide the research and industry communities with a free software tool for fast and robust classification of chemical maps. A standardized color scheme with reference colors for minerals and mineral groups is proposed to improve the readability of the classified maps in petrological studies. The performance of each algorithm varies depending on the data set, especially when minerals exhibit strong compositional zoning or when different minerals have similar compositions for a given element. The random forest algorithm based on bootstrap aggregation provides satisfactory results in most situations and is recommended for general use in XMapTools.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"25 ","pages":"Article 100230"},"PeriodicalIF":2.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143509291","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":"lasertram: A Python library for time resolved analysis of laser ablation inductively coupled plasma mass spectrometry data","authors":"Jordan Lubbers ,&nbsp;Adam J.R. Kent ,&nbsp;Chris Russo","doi":"10.1016/j.acags.2025.100225","DOIUrl":"10.1016/j.acags.2025.100225","url":null,"abstract":"<div><div>Laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) data has a wide variety of uses in the geosciences for in-situ chemical analysis of complex natural materials. Improvements to instrument capabilities and operating software have drastically reduced the time required to generate large volumes of data relative to previous methodologies. Raw data from LA-ICP-MS, however, is in counts per unit time (typically counts per second), not elemental concentrations and converting these count ratesto concentrations requires additional processing. For complex materials where the ablated volume may contain a range of material compositions, a moderate amount of user input is also required if appropriate concentrations are to be accurately calculated. In geologic materials such as glasses and minerals that potentially have numerous heterogeneities (e.g., microlites or other inclusions) within them, this is typically determiningwhether the total ablation signal should be filtered to remove these heterogeneities. This necessitates that the LA-ICP-MS data processing pipeline is one that is not automated, but is also designed to enable rapid and efficient processing of large volumes of data.</div><div>Here we introduce <figure><img></figure> , a Python library for the time resolved analysis of LA-ICP-MS data. We outline its mathematical theory, code structure, and provide an example of how it can be used to provide the time resolved analysis necessitated by LA-ICP-MS data of complex geologic materials. Throughout the <figure><img></figure> pipeline we show how metadata and data are incrementally added to the objects created such that virtually any aspect of an experiment may be interrogated and its quality assessed. We also show, that when combined with other Python libraries for building graphical user interfaces, it can be utilized outside of a pure scripting environment. <figure><img></figure> can be found at <span><span>https://doi.org/10.5066/P1DZUR3Z</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"25 ","pages":"Article 100225"},"PeriodicalIF":2.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143549376","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":"Do more with less: Exploring semi-supervised learning for geological image classification","authors":"Hisham I. Mamode,&nbsp;Gary J. Hampson,&nbsp;Cédric M. John","doi":"10.1016/j.acags.2024.100216","DOIUrl":"10.1016/j.acags.2024.100216","url":null,"abstract":"<div><div>Labelled datasets within geoscience can often be small, with data acquisition both costly and challenging, and their interpretation and downstream use in machine learning difficult due to data scarcity. Deep learning algorithms require large datasets to learn a robust relationship between the data and its label and avoid overfitting. To overcome the paucity of data, transfer learning has been employed in classification tasks. But an alternative exists: there often is a large corpus of unlabeled data which may enhance the learning process. To evaluate this potential for subsurface data, we compare a high-performance semi-supervised learning (SSL) algorithm (SimCLRv2) with supervised transfer learning on a Convolutional Neural Network (CNN) in geological image classification.</div><div>We tested the two approaches on a classification task of sediment disturbance from cores of International Ocean Drilling Program (IODP) Expeditions 383 and 385. Our results show that semi-supervised transfer learning can be an effective strategy to adopt, with SimCLRv2 capable of producing representations comparable to those of supervised transfer learning. However attempts to enhance the performance of semi-supervised transfer learning with task-specific unlabeled images during self-supervision degraded representations. Significantly, we demonstrate that SimCLRv2 trained on a dataset of core disturbance images can out-perform supervised transfer learning of a CNN once a critical number of task-specific unlabeled images are available for self-supervision. The gain in performance compared to supervised transfer learning is 1% and 3% for binary and multi-class classification, respectively.</div><div>Supervised transfer learning can be deployed with comparative ease, whereas the current SSL algorithms such as SimCLRv2 require more effort. We recommend that SSL be explored in cases when large amounts of unlabeled task-specific images exist and improvement of a few percent in metrics matter. When examining small, highly specialized datasets, without large amounts of unlabeled images, supervised transfer learning might be the best strategy to adopt. Overall, SSL is a promising approach and future work should explore this approach utilizing different dataset types, quantity, and quality.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"25 ","pages":"Article 100216"},"PeriodicalIF":2.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143166137","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 new inversion algorithm (PyMDS) based on the Pyro library to use chlorine 36 data as a paleoseismological tool on normal fault scarps","authors":"Maureen Llinares,&nbsp;Ghislain Gassier,&nbsp;Sophie Viseur,&nbsp;Lucilla Benedetti","doi":"10.1016/j.acags.2025.100234","DOIUrl":"10.1016/j.acags.2025.100234","url":null,"abstract":"<div><div>Paleoseismology (study of earthquakes that occurred before records were kept and before instruments can record them) provides useful information such as recurrence periods and slip rate to assess seismic hazard and better understand fault mechanisms. Chlorine 36 is one of the paleoseismological tools that can be used to date scarp exhumation associated with earthquakes events.</div><div>We propose an algorithm, PyMDS, that uses chlorine 36 data sampled on a fault scarp to retrieve seismic sequences (age and slip associated to each earthquake) and long term slip rate on a normal fault.</div><div>We show that the algorithm, based on Hamiltonian kernels, can successfully retrieve earthquakes and long term slip rate on a synthetic dataset. The precision on the ages can vary between few thousand years for old earthquakes (&gt;5000 yr BP) and down to few hundreds of years for the most recent ones (&lt;2000 yr BP). The resolution on the slip is ∼30–50 cm and on the slip rate is ∼ 1 mm/yr. Diagnostic tools (R_hat and divergences on chains) are used to check the convergence of the results.</div><div>Our new code is applied to a site in Central Italy, the results yielded are in agreement with the ones obtained previously with another inversion procedure. We found 4 events 7800±400 yr, 4700±400 yr, 3000±200 and 400 ±20 yr BP on the MA3 site. The associated slips were of 130±10 cm, 140±20 cm, 580 ± 20 cm and 205±20 cm. The results are comparable with a previous study made by (Schlagenhauf et al., 2010). The yielded slip rate of 2.7 mm/yr ± 0.4 mm/yr is also coherent with the one determined by Tesson et al. (2020).</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"25 ","pages":"Article 100234"},"PeriodicalIF":2.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143631906","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":"SeisAug: A data augmentation python toolkit","authors":"D. Pragnath ,&nbsp;G. Srijayanthi ,&nbsp;Santosh Kumar ,&nbsp;Sumer Chopra","doi":"10.1016/j.acags.2025.100232","DOIUrl":"10.1016/j.acags.2025.100232","url":null,"abstract":"<div><div>A common limitation in applying any deep learning and machine learning techniques is the limited labelled dataset which can be addressed through Data augmentation (DA). SeisAug is a DA python toolkit to address this challenge in seismological studies. DA. DA helps to balance the imbalanced classes of a dataset by creating more examples of under-represented classes. It significantly mitigates overfitting by increasing the volume of training data and introducing variability, thereby improving the model's performance on unseen data. Given the rapid advancements in deep learning for seismology, ‘SeisAug’ assists in extensibility by generating a substantial amount of data (2–6 times more data) which can aid in developing an indigenous robust model. Further, this study demonstrates the role of DA in developing a robust model. For this we utilized a basic two class identification models between earthquake/signal and noise/(non-earthquake). The model is trained with original, 1 and 5 times augmented datasets and their performance metrics are evaluated. The model trained with 5X times augmented dataset significantly outperforms with accuracy of 0.991, AUC 0.999 and AUC-PR 0.999 compared to the model trained with original dataset with accuracy of 0.50, AUC 0.75 and AUC-PR 0.80. Furthermore, by making all codes available on GitHub, the toolkit facilitates the easy application of DA techniques, empowering end-users to enhance their seismological waveform datasets effectively and overcome the initial drawbacks posed by the scarcity of labelled data.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"25 ","pages":"Article 100232"},"PeriodicalIF":2.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143591747","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}