Applied Computing and Geosciences最新文献

{"title":"Optimizing bathymetric position index (BPI) calculation: An analysis of parameters and recommendations for the selection of their optimal values","authors":"A. Mena,&nbsp;L.M. Fernández-Salas","doi":"10.1016/j.acags.2024.100168","DOIUrl":"https://doi.org/10.1016/j.acags.2024.100168","url":null,"abstract":"<div><p>The present research paper addresses a critical gap in existing literature concerning the absence of a standardized methodology for parameter selection in the computation of the Bathymetric Position Index (BPI) values. The BPI is a measure of where a georeferenced location, with a defined depth, is relative to the neighbouring seascape, and it plays a significant role in characterizing benthic terrain for modelling and classification. Arguably, the two most important parameters when calculating the BPI are the size and the shape of the neighbourhood of analysis. With regards to the radius parameter, which defines the size of the neighbourhood, the optimal radius value for calculating the BPI must be carefully chosen, considering both the size of the target morphology and the scale factor, which is equal to the radius in map units multiplied by the cell size. It is recommended that the optimal radius value should closely match the size of the target morphology. Tests were performed using an annular neighbourhood shape and they have revealed that the outer radius is the most influential factor in the BPI calculation. Further experimentations and comparisons between circular and annular shapes have indicated that the use of different shapes has no significant impact on the results. The study has found no substantial correlation between the BPI values and other examined terrain variables, such as depth, slope, and curvature. This lack of correlation may be attributed to the BPI values accounting for the specific neighbourhood size, while for the studied variables the default window size was used, which is a considerably smaller scale than the ones used in most BPI calculations. In conclusion, this research highlights the importance of parameter selection in BPI calculations and provides valuable insights into the optimal radius choice and the negligible impact of neighbourhood shape. The findings also shed light on the unique nature of BPI values and their relationship with other geospatial variables.</p></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"23 ","pages":"Article 100168"},"PeriodicalIF":3.4,"publicationDate":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2590197424000156/pdfft?md5=0286e8b9c27e2b3079a179596aba29da&pid=1-s2.0-S2590197424000156-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141286448","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":"BioReactPy: An open-source software for simulation of microbial-mediated reactive processes in porous media","authors":"M. Starnoni,&nbsp;M.A. Dawi,&nbsp;X. Sanchez-Vila","doi":"10.1016/j.acags.2024.100166","DOIUrl":"https://doi.org/10.1016/j.acags.2024.100166","url":null,"abstract":"<div><p>This paper provides a new open-source software, named BioReactPy, for simulation of microbial-mediated coupled processes of flow and reactive transport in porous media. The software is based on the micro-continuum approach, and geochemistry is handled in a fully coupled manner with biomass-nutrient growth treated with Monod equation in a single integrated framework, without dependencies on third party packages. The distinguishing features of the software, its design principles, and formulation of multiphysics problems and discretizations are discussed. Validation of the <em>Python</em> implementation using several established benchmarks for flow, reactive transport, and biomass growth is presented. The flexibility of the framework is then illustrated by simulations of highly non-linearly coupled flow and microbial reactive transport at conditions relevant to carbon mineralization for CO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span> storage. All results can be reproduced by openly available simulation scripts.</p></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"22 ","pages":"Article 100166"},"PeriodicalIF":3.4,"publicationDate":"2024-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2590197424000132/pdfft?md5=0356eeaf365220fb8e4e4b0812a35643&pid=1-s2.0-S2590197424000132-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140644289","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":"Single image multi-scale enhancement for rock Micro-CT super-resolution using residual U-Net","authors":"Liqun Shan ,&nbsp;Chengqian Liu ,&nbsp;Yanchang Liu ,&nbsp;Yazhou Tu ,&nbsp;Sai Venkatesh Chilukoti ,&nbsp;Xiali Hei","doi":"10.1016/j.acags.2024.100165","DOIUrl":"https://doi.org/10.1016/j.acags.2024.100165","url":null,"abstract":"<div><p>Micro-CT, also known as X-ray micro-computed tomography, has emerged as the primary instrument for pore-scale properties study in geological materials. Several studies have used deep learning to achieve super-resolution reconstruction in order to balance the trade-off between resolution of CT images and field of view. Nevertheless, most existing methods only work with single-scale CT scans, ignoring the possibility of using multi-scale image features for image reconstruction. In this study, we proposed a super-resolution approach via multi-scale fusion using residual U-Net for rock micro-CT image reconstruction (MS-ResUnet). The residual U-Net provides an encoder-decoder structure. In each encoder layer, several residual sequential blocks and improved residual blocks are used. The decoder is composed of convolutional ReLU residual blocks and residual chained pooling blocks. During the encoding-decoding method, information transfers between neighboring multi-resolution images are fused, resulting in richer rock characteristic information. Qualitative and quantitative comparisons of sandstone, carbonate, and coal CT images demonstrate that our proposed algorithm surpasses existing approaches. Our model accurately reconstructed the intricate details of pores in carbonate and sandstone, as well as clearly visible coal cracks.</p></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"22 ","pages":"Article 100165"},"PeriodicalIF":3.4,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2590197424000120/pdfft?md5=a5d1fae25e7acce0a16ad1a4c88f7058&pid=1-s2.0-S2590197424000120-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140644288","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":"Machine learning technique in the north zagros earthquake prediction","authors":"Salma Ommi ,&nbsp;Mohammad Hashemi","doi":"10.1016/j.acags.2024.100163","DOIUrl":"https://doi.org/10.1016/j.acags.2024.100163","url":null,"abstract":"<div><p>Studying the changes in seismicity, and the potential of the occurrences of large earthquakes in a seismic zone is not only extremely important from the aspect of seismological research, but it is additionally significant in the decisions of crisis management. Since, nowadays Machine learning techniques have proven the high ability for analyzing information, and discovering the relations among the parameters, in this research were tested some of these techniques for the earthquake prediction. For analysis, the north Zagros seismic catalogue was selected. A region that is an active seismic zone, and large cities are located there. Moreover, nine seismic parameters were used to study the possibility of large earthquake prediction for 1 month using three different Machine Learning (ML) techniques (Artificial Neural Network (ANN), Random Forest, and Support Vector Machine (SVM)). The accuracy of prediction models was evaluated using four different statistical measures (recall, accuracy, precision, and F1-score). The results showed that the (ANN) method is more accurate than other methods. Based on three investigated methodologies, greater accuracy results have been produced to forecast the earthquakes with bigger scale earthquakes about the completeness of the seismic catalogue in large magnitude. These achievements promise the possibility of successful prediction in a short period, which is hopeful for better crisis management performance.</p></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"22 ","pages":"Article 100163"},"PeriodicalIF":3.4,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2590197424000107/pdfft?md5=bd218566b9d38745ae009d44255d10cd&pid=1-s2.0-S2590197424000107-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140619101","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 hybrid knowledge graph for efficient exploration of lithostratigraphic information in open text data","authors":"Wenjia Li ,&nbsp;Xiaogang Ma ,&nbsp;Xinqing Wang ,&nbsp;Liang Wu ,&nbsp;Sanaz Salati ,&nbsp;Zhong Xie","doi":"10.1016/j.acags.2024.100164","DOIUrl":"https://doi.org/10.1016/j.acags.2024.100164","url":null,"abstract":"<div><p>Rocks formed during different geologic time record the diverse evolution of the geosphere and biosphere. In the past decades, substantial geoscience data have been made open access, providing invaluable resources for studying the stratigraphy in different regions and at different scales. However, many open datasets have information recorded in natural language with heterogeneous terminologies, short of efficient approaches to analyze them. In this research, we constructed a hybrid Stratigraphic Knowledge Graph (StraKG) to help address this challenge. StraKG has two layers, a simple schema layer and a rich instance layer. For the schemas, we used a short but functional list of classes and relationships, and then incorporated community-recognized terminologies from geological dictionaries. For the instances, we used natural language processing techniques to analyze open text data and obtained massive records, such as rocks and spatial locations. The nodes in the two layers were associated to establish a consistent structure of stratigraphic knowledge. To verify the functionality of StraKG, we applied it to the Baidu encyclopedia, the largest online Chinese encyclopedia. Three experiments were implemented on the topics of stratigraphic correlation, spatial distribution of ophiolite in China, and spatio-temporal distribution of open lithostratigraphic data. The results show that StraKG can provide strong knowledge reference for stratigraphic studies. Used together with data exploration and data mining methods, StraKG illustrates a new approach to analyze the open and big text data in geoscience.</p></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"22 ","pages":"Article 100164"},"PeriodicalIF":3.4,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2590197424000119/pdfft?md5=f9a7de24734aba4b725f80aef417972d&pid=1-s2.0-S2590197424000119-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140558902","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":"Geosteering based on resistivity data and evolutionary optimization algorithm","authors":"Maksimilian Pavlov ,&nbsp;Georgy Peshkov ,&nbsp;Klemens Katterbauer ,&nbsp;Abdallah Alshehri","doi":"10.1016/j.acags.2024.100162","DOIUrl":"https://doi.org/10.1016/j.acags.2024.100162","url":null,"abstract":"<div><p>Currently, the oil and gas industry faces numerous challenges in addressing geosteering issues in horizontal drilling. To optimize the extraction of hydrocarbon resources and to avoid penetration in aquifers, industry experts frequently modify the drilling trajectory using real-time measurements. This approach involves quantifying subsurface uncertainties in real-time, enhancing operational decision-making with more informed insights but also adding to its complexity. This paper demonstrates an approach to decision making for trajectory correction based on real-time formation evaluation data and the differential evolution algorithm. The approach uses volumetric resistivity log data and data from reservoir models, such as porosity. The provided methodology suggests corrections for planned well trajectories by maximization of the objective function. The objective function operates with a calculated hydrocarbon saturation environment as the decision-making system in a virtual sequential drilling process. To demonstrate the accuracy and reliability of our approach, we compared the simulations of the corrected trajectory with the preliminary trajectory drilled in the same area. In addition, we conducted several experiments to tune the hyper-parameters of the differential evolution algorithm to select the optimal parameter set for our case study and compared proposed differential evolution algorithm with particle swarm optimization and pattern search algorithms. The results of our experiments showed that the real-time formation evaluation data combined with the differential evolution algorithm outperformed a trajectory provided by the drilling engineers. Differential evolution algorithm demonstrated strong performance compared to others optimization algorithms. We have implemented a complete pipeline from generating resistivity and porosity cubes, using the Archie equation to estimate oil saturation, and consequently generating a corrected trajectory in this cube based on near-well data, angle constraints and predefined hyper-parameters set prior to well trajectory planning. The methods developed were validated on synthetic and real datasets. Our decision-making system shows better cumulative oil saturation values than the preliminary provided horizontal well.</p></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"22 ","pages":"Article 100162"},"PeriodicalIF":3.4,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2590197424000090/pdfft?md5=121ad0b2564ad9df2ff5474153c7c429&pid=1-s2.0-S2590197424000090-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140320952","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":"Fracture density reconstruction using direct sampling multiple-point statistics and extreme value theory","authors":"Ana Paula Burgoa Tanaka ,&nbsp;Philippe Renard ,&nbsp;Julien Straubhaar","doi":"10.1016/j.acags.2024.100161","DOIUrl":"https://doi.org/10.1016/j.acags.2024.100161","url":null,"abstract":"<div><p>The aim of this work is to present a methodology for the reconstruction of missing fracture density within highly fractured intervals, which can represent preferential fluid flow pathways. The lack of record can be very common due to the intense presence of fractures, dissolution processes, or data acquisition issues. The superposition of numerous fractures makes the definition of fracture surfaces impossible, as a consequence, modeling such zones is challenging. In order to address this issue, the usage of direct sampling multiple-point statistics to perform gap filling in well logs is demonstrated as an alternative to other techniques. It reproduces data patterns and provides several models representing uncertainty. The method was tested in intervals from a highly fractured well, by removing previously known fracture density data, and simulating different scenarios with direct sampling. Simulation results are compared to the observed data using cross-validation and continuous rank probability score. The reference scenario training data set consists in one well and two variables: fracture density and fracture occurrence. A sensitivity analysis is carried out considering additional variables, additional wells, different intervals, resampling with extremes, and other gap filling techniques. The auxiliary variable plays an important role in pattern matching, but adding wells and logs increases the complexity of the method without improving pattern retrieval. Best results are obtained applying extreme values theory for stochastic process with the enrichment of the fracture density data at the tail region, followed by resampling of the new values. The enriched data is used for the gap filling resulting in lower continuous rank probability score, and the achievement of extreme fracture density values.</p></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"22 ","pages":"Article 100161"},"PeriodicalIF":3.4,"publicationDate":"2024-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2590197424000089/pdfft?md5=c27203f5daa8671df46f77001c99d0ae&pid=1-s2.0-S2590197424000089-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140320951","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":"DIFFUSUP: A graphical user interface (GUI) software for diffusion modeling","authors":"Junxing Chen ,&nbsp;Yi Zou ,&nbsp;Xu Chu","doi":"10.1016/j.acags.2024.100157","DOIUrl":"10.1016/j.acags.2024.100157","url":null,"abstract":"<div><p>Advancements in high-resolution in-situ analyses have led to the extensive use of mineral diffusion zonings in determining petrologic and orogenic rates. The diffusion simulation, especially in multi-element systems, is numerically complex in practice. To streamline the application, we developed DIFFUSUP, a software featuring a graphic user interface (GUI) that facilitates the numerical simulation of diffusion with intricate initial conditions and thermal histories. DIFFUSUP alleviates the need for the knowledge of diffusion formulae, numerical solutions, and programming while still necessitating a fundamental understanding of problem setting, including the initial profiles and <em>P</em>-<em>T</em>-<em>t</em> evolution. DIFFUSUP's intuitive interface significantly simplifies the simulation setup process, making it particularly beneficial for reconnaissance research. It provides users with a balance between simplicity and flexibility, catering to a wide range of applications. These include support for multi-component systems, linear or isotropic spherical settings, variations in <em>P-T</em>-<em>f</em>_O₂ conditions, initial profiles, and boundary conditions. The software is stand-alone, compatible with Windows and macOS, and can be adapted to diverse problem settings. The software, user's guide, and a few examples can be downloaded from <span>www.diffusup.org</span><svg><path></path></svg>.</p></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"22 ","pages":"Article 100157"},"PeriodicalIF":3.4,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2590197424000041/pdfft?md5=1e18ac5ee5afe6e671dfd3a98c75af76&pid=1-s2.0-S2590197424000041-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139965915","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":"GeaVR: An open-source tools package for geological-structural exploration and data collection using immersive virtual reality","authors":"Fabio Luca Bonali ,&nbsp;Fabio Vitello ,&nbsp;Martin Kearl ,&nbsp;Alessandro Tibaldi ,&nbsp;Malcolm Whitworth ,&nbsp;Varvara Antoniou ,&nbsp;Elena Russo ,&nbsp;Emmanuel Delage ,&nbsp;Paraskevi Nomikou ,&nbsp;Ugo Becciani ,&nbsp;Benjamin van Wyk de Vries ,&nbsp;Mel Krokos","doi":"10.1016/j.acags.2024.100156","DOIUrl":"https://doi.org/10.1016/j.acags.2024.100156","url":null,"abstract":"<div><p>We introduce GeaVR, an open-source package containing tools for geological-structural exploration and mapping in Immersive Virtual Reality (VR). GeaVR also makes it possible to carry out quantitative data collection on 3D realistic, referenced and scaled Virtual Reality scenarios. Making use of Immersive Virtual Reality technology through the Unity game engine, GeaVR works with commercially available VR equipment. This allows VR to be accessible to a broad audience, resulting in a revolutionary tool package for Earth Sciences. Users can explore various 3D datasets, spanning from freely available Digital Surface Models and Bathymetric data to ad-hoc 3D high-resolution models from photogrammetry processing. The user can navigate the 3D model in first person, walking or flying above the surrounding environment, mapping the main geological features such as points, lines and polygons, and collecting quantitative data using the provided field survey tools. Such data, including geographic coordinates, can be exported for further spatial analyses. Here we describe three different case studies aimed at showing the potential of our tools. GeaVR is revolutionary as it can be used worldwide, with no spatial limitations, both for geo-education and Earth Science popularization, as well as for research purposes. Secondly, it makes it possible to safely access dangerous areas, such as vertical cliffs or volcanic terrains, virtually from a computer screen or Virtual Reality headset. Furthermore, it can help to reduce carbon emissions by avoiding the use of flights and vehicles to conduct field trips.</p></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"21 ","pages":"Article 100156"},"PeriodicalIF":3.4,"publicationDate":"2024-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S259019742400003X/pdfft?md5=5f17c7a813557d113d7b05af41b9e72b&pid=1-s2.0-S259019742400003X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139487794","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 novel few-shot learning framework for rock images dually driven by data and knowledge","authors":"Zhongliang Chen ,&nbsp;Feng Yuan ,&nbsp;Xiaohui Li ,&nbsp;Mingming Zhang ,&nbsp;Chaojie Zheng","doi":"10.1016/j.acags.2024.100155","DOIUrl":"10.1016/j.acags.2024.100155","url":null,"abstract":"<div><p>In the field of geosciences, the integration of artificial intelligence is transitioning from perceptual intelligence to cognitive intelligence. The simultaneous utilization of knowledge and data in the geoscience domain is a universally addressed concern. In this paper, based on the interpretability of deep learning models for rock images, rock features such as structure, texture, mineral and macroscopic identification characteristics were selected to extract a rock identification subgraph from the petrographic knowledge graph and carry out rock type similarity reasoning. Comparative experiments were conducted on few-shot learning of rock images under the supervision of rock type similarity knowledge. The results of the few-shot learning comparisons demonstrate that the supervision of rock type similarity knowledge significantly enhances performance. Additionally, rock type similarity knowledge exhibits a marginal effect on improving few-shot learning performance. Given the absence of Chinese word embedding and large-scale Chinese pre-trained language models in the geological domain, graph embedding based on domain-specific knowledge graphs in geosciences can offer computable geoscience knowledge for research dually propelled by data and knowledge.</p></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"21 ","pages":"Article 100155"},"PeriodicalIF":3.4,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2590197424000028/pdfft?md5=93393ae565797d66d072313d4d50afa4&pid=1-s2.0-S2590197424000028-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139458379","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}