Applied Computing and Geosciences最新文献

{"title":"Evaluating the performances of SVR and XGBoost for short-range forecasting of heatwaves across different temperature zones of India","authors":"Srikanth Bhoopathi,&nbsp;Nitish Kumar,&nbsp;Somesh,&nbsp;Manali Pal","doi":"10.1016/j.acags.2024.100204","DOIUrl":"10.1016/j.acags.2024.100204","url":null,"abstract":"<div><div>This research aims to forecast maximum temperatures and the frequency of heatwave days across four different temperature zones (Zone 1, 2, 3 and 4) in India. These four zones are categorized based on the 30-year average maximum temperatures (T_30AMT) during the summer months of April, May, and June (AMJ). Two Machine Learning (ML) algorithms eXtreme Gradient Boosting (XGBoost) and Support Vector Regression (SVR) are employed to achieve this goal. The study utilizes nine key atmospheric variables namely air temperature, geopotential height, relative humidity, U-wind, V-wind, soil moisture, solar radiation, sea surface temperature, and mean sea level pressure at a daily scale spanning from 1991 to 2020 for the months of March, April, May, and June as predictors. The India Meteorological Department daily maximum temperature data spanning from 1991 to 2020 for the months of AMJ serves as the predictands. ML models are developed using spatially averaged atmospheric variables and daily maximum temperature across the grids falling within each temperature zone. Results indicate that for a 7-day lead time, SVR outperforms XGBoost in Zone-1 (T_30AMT &gt; 38 °C) and Zone-2 (T_30AMT: 35.01 °C–38 °C) by more accurately capturing peak temperatures during training and testing. Conversely, for a 15-day lead time in Zone-1, XGBoost better predicts temperature peaks in both phases. In Zone-3 (T_30AMT: 30 °C–35 °C) and Zone-4 (T_30AMT &lt; 30 °C) for both lead times, the performance of both models decline, indicating models and input variables are more effective in predicting higher temperatures typical of Zone-1 and 2 but less so in Zone-3 and 4. In a nutshell, the study attempts to highlight the capability of advanced ML techniques combined with spatial climate data to enhance the prediction of extreme heatwave events. These insights can aid in heatwave preparedness, climate management, and adaptation strategies for different Indian temperature zones.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"24 ","pages":"Article 100204"},"PeriodicalIF":2.6,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142534341","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":"Current progress in subseasonal-to-decadal prediction based on machine learning","authors":"Zixiong Shen ,&nbsp;Qiming Sun ,&nbsp;Xinyu Lu ,&nbsp;Fenghua Ling ,&nbsp;Yue Li ,&nbsp;Jiye Wu ,&nbsp;Jing-Jia Luo ,&nbsp;Chaoxia Yuan","doi":"10.1016/j.acags.2024.100201","DOIUrl":"10.1016/j.acags.2024.100201","url":null,"abstract":"<div><div>The application of machine learning (ML) techniques to climate science has received significant attention, particularly in the field of climate predictions, ranging from sub-seasonal to decadal time scales. This paper reviews recent progress of ML techniques employed in climate phenomena prediction and the enhancement of dynamic forecast models, which provide valuable insights into the great potentials of ML techniques to improve climate prediction capabilities with reduced computational time and resource consumption. This paper also discusses several major challenges in the application of ML to climate prediction, including the scarcity of datasets, physical inconsistency, and lack of model transparency and interpretability. Additionally, this paper sheds light on how climate change impacts ML model training and prediction, and explores three key areas with potential breakthroughs: large-scale climate models, knowledge discovery driven by ML, and hybrid dynamical-statistical models, underscoring the important role of the integration of “ML and dynamical models” in building a bridge between the artificial intelligence and climate science.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"24 ","pages":"Article 100201"},"PeriodicalIF":2.6,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142534340","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":"Novel application of unsupervised machine learning for characterization of subsurface seismicity, tectonic dynamics and stress distribution","authors":"Mohammad Salam,&nbsp;Muhammad Tahir Iqbal,&nbsp;Raja Adnan Habib,&nbsp;Amna Tahir,&nbsp;Aamir Sultan,&nbsp;Talat Iqbal","doi":"10.1016/j.acags.2024.100200","DOIUrl":"10.1016/j.acags.2024.100200","url":null,"abstract":"<div><div>Our study pioneers an innovative use of unsupervised machine learning, a powerful tool for navigating unclassified data, to unravel the complexities of subsurface seismic activities and extract meaningful patterns. Our central objective is to comprehensively characterize seismicity within an active region by identifying distinct seismic clusters in spatial distribution, thereby gaining a deeper understanding of subsurface stress distribution and tectonic dynamics. Employing a diverse range of clustering algorithms, with particular emphasis on Fuzzy C-Means (FCM), our research meticulously dissects the intricate physical processes that govern a complex tectonic zone. This technique effectively delineates distinct tectonic zones, aligning seamlessly with established seismological knowledge and underscoring the transformative potential of Artificial Intelligence (AI) in analyzing regional subsurface phenomena, even under conditions of data scarcity. Moreover, associating earthquakes with specific seismogenic structures significantly enhances seismic hazard analyses, potentially paving the way for autonomous insights that inform engineering hazard assessments.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"24 ","pages":"Article 100200"},"PeriodicalIF":2.6,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142534339","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 study on machine learning approaches for rock mass classification using drilling data","authors":"Tom F. Hansen ,&nbsp;Georg H. Erharter ,&nbsp;Zhongqiang Liu ,&nbsp;Jim Torresen","doi":"10.1016/j.acags.2024.100199","DOIUrl":"10.1016/j.acags.2024.100199","url":null,"abstract":"<div><div>Current rock engineering design in drill and blast tunnelling primarily relies on engineers' observational assessments. Measure While Drilling (MWD) data, a high-resolution sensor dataset collected during tunnel excavation, is underutilised, mainly serving for geological visualisation. This study aims to automate the translation of MWD data into actionable metrics for rock engineering. It seeks to link data to specific engineering actions, thus providing critical decision support for geological challenges ahead of the tunnel face. Leveraging a large and geologically diverse dataset of ∼500,000 drillholes from 15 tunnels, the research introduces models for accurate rock mass quality classification in a real-world tunnelling context. Both conventional machine learning and image-based deep learning are explored to classify MWD data into Q-classes and Q-values—examples of metrics describing the stability of the rock mass—using both tabular- and image data. The results indicate that the K-nearest neighbours algorithm in an ensemble with tree-based models using tabular data effectively classifies rock mass quality. It achieves a cross-validated balanced accuracy of 0.86 in classifying rock mass into the Q-classes A, B, C, D, E1, E2, and 0.95 for a binary classification with E versus the rest. Classification using a CNN with MWD-images for each blasting round resulted in a balanced accuracy of 0.82 for binary classification. Regressing the Q-value from tabular MWD-data achieved cross-validated R² and MSE scores of 0.80 and 0.18 for a similar ensemble model as in classification. High performance in regression and classification boosts confidence in automated rock mass assessment. Applying advanced modelling on a unique dataset demonstrates MWD data's value in improving rock mass classification accuracy and advancing data-driven rock engineering design, reducing manual intervention.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"24 ","pages":"Article 100199"},"PeriodicalIF":2.6,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142534338","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 generative deep neural network as an alternative to co-kriging","authors":"Herbert Rakotonirina ,&nbsp;Paul Honeine ,&nbsp;Olivier Atteia ,&nbsp;Antonin Van Exem","doi":"10.1016/j.acags.2024.100198","DOIUrl":"10.1016/j.acags.2024.100198","url":null,"abstract":"<div><div>In geosciences, kriging is leading spatial interpolation, and co-kriging is the most commonly used method for accomplishing spatial interpolation of a target variable by incorporating information from a secondary variable. Co-kriging relies on the assumption of spatial stationarity, which may not hold true in all geospatial contexts, leading to potential inaccuracies in interpolation. The effectiveness of co-kriging can be compromised in areas with sparse data, impacting the reliability of interpolated results. Moreover, it can be resource-intensive when used for interpolation with a substantial volume of data, especially in the case of 3D interpolation. In this paper, we introduce a new method for spatial interpolation that considers two variables using a generative deep neural network. This approach utilizes a convolutional neural network with an encoder–decoder architecture, featuring a single encoder and two decoders to handle the two variables. Additionally, we introduce a loss function that facilitates the control over the relationships between the two variables. Traditional Deep Learning methods require prior training and labeled data, whereas the proposed approach eliminates this requirement and simplifies the interpolation process. In order to assess the performance of our method, we use two real-world datasets. The first one is a 2D dataset of total soil organic carbon combined with the Normalized Difference Vegetation Index. The second one is a 3D dataset that combines concentrations of Hydrocarbon and Fluoride obtained from hyperspectral analysis of soil cores with very limited number of boreholes. The experimental results demonstrate that the proposed method outperforms ordinary kriging and co-kriging, showing a significant improvement when both variables are used. We also demonstrate how the inclusion of the auxiliary variable serves as a means to mitigate the overfitting of the model.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"24 ","pages":"Article 100198"},"PeriodicalIF":2.6,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142426818","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":"An open-source, QGIS-based solution for digital geological mapping: GEOL-QMAPS","authors":"Julien Perret,&nbsp;Mark W. Jessell,&nbsp;Eliott Bétend","doi":"10.1016/j.acags.2024.100197","DOIUrl":"10.1016/j.acags.2024.100197","url":null,"abstract":"<div><div>Digital geological mapping has experienced significant growth over the past three decades due to the advent of commercial geographical information systems, advances in global positioning systems, and the availability of portable hand-held devices, such as mobile personal computers (PCs), smartphones, and tablets. Numerous software packages have been developed to collect, combine, organise, visualise, publish, and share field data with enhanced spatial accuracy and minimal post-field processing. However, many of these tools are not open-source or are not made available to the geoscientific community, remaining specific to given mapping projects or organisations.</div><div>In this contribution we introduce GEOL-QMAPS, an open-source, QGIS-based solution promoting digital geological mapping in a harmonised, comprehensive and flexible way. It can be used in the field with a tablet PC or <em>via</em> the QGIS-based QField app on iOS or Android mobile devices, enabling synchronisation with desktop QGIS and the creation of field databases. Designed as a general solution, the GEOL-QMAPS solution consists of a QGIS field data entry template and a custom QGIS plugin, both available on free-access online repositories. The plugin allows for the adaptation of dictionaries (i.e., lists of attributes describing geological features), initially set to international nomenclatures, to the guidelines of different mapping projects. The solution also facilitates the loading and consultation of relevant legacy geodatasets (<em>e.g.,</em> preexisting field data, geochemical, geophysical maps or punctual datasets). A fact map, created from field data collected across the Archean Sula-Kangari greenstone belt in Sierra Leone, demonstrates the solution's advantages in terms of post-field processing and raw field data sharing.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"24 ","pages":"Article 100197"},"PeriodicalIF":2.6,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142426819","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":"Deep learning for real-time P-wave detection: A case study in Indonesia's earthquake early warning system","authors":"Adi Wibowo ,&nbsp;Leni Sophia Heliani ,&nbsp;Cecep Pratama ,&nbsp;David Prambudi Sahara ,&nbsp;Sri Widiyantoro ,&nbsp;Dadan Ramdani ,&nbsp;Mizan Bustanul Fuady Bisri ,&nbsp;Ajat Sudrajat ,&nbsp;Sidik Tri Wibowo ,&nbsp;Satriawan Rasyid Purnama","doi":"10.1016/j.acags.2024.100194","DOIUrl":"10.1016/j.acags.2024.100194","url":null,"abstract":"<div><p>Detecting seismic events in real-time for prompt alerts and responses is a challenging task that requires accurately capturing P-wave arrivals. This task becomes even more challenging in regions like Indonesia, where widely spaced seismic stations exist. The wide station spacing makes associating the seismic signals with specific even more difficult. This paper proposes a novel deep learning-based model with three convolutional layers, enriched with dual attention mechanisms—Squeeze, Excitation, and Transformer Encoder (CNN-SE-T) —to refine feature extraction and improve detection sensitivity. We have integrated several post-processing techniques to further bolster the model's robustness against noise. We conducted comprehensive evaluations of our method using three diverse datasets: local earthquake data from East Java, the publicly available Seismic Waveform Data (STEAD), and a continuous waveform dataset spanning 12 h from multiple Indonesian seismic stations. The performance of the CNN-SE-T P-wave detection model yielded exceptionally high F1 scores of 99.10% for East Java, 92.64% for STEAD, and 80% for the 12-h continuous waveforms across Indonesia's network, demonstrating the model's effectiveness and potential for real-world application in earthquake early warning systems.</p></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"24 ","pages":"Article 100194"},"PeriodicalIF":2.6,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2590197424000417/pdfft?md5=b20faddd2c5b63b0d46b89310f92cfaf&pid=1-s2.0-S2590197424000417-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142161200","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":"Integration of multi-temporal SAR data and robust machine learning models for improvement of flood susceptibility assessment in the southwest coast of India","authors":"Pankaj Prasad ,&nbsp;Sourav Mandal ,&nbsp;Sahil Sandeep Naik ,&nbsp;Victor Joseph Loveson ,&nbsp;Simanku Borah ,&nbsp;Priyankar Chandra ,&nbsp;Karthik Sudheer","doi":"10.1016/j.acags.2024.100189","DOIUrl":"10.1016/j.acags.2024.100189","url":null,"abstract":"<div><p>The flood hazards in the southwest coastal region of India in 2018 and 2020 resulted in numerous casualties and the displacement of over a million people from their homes. In order to mitigate the loss of life and resources caused by recurrent major and minor flood events, it is imperative to develop a comprehensive spatial flood zonation map of the entire area. Therefore, the main aim of the present study is to prepare a flood susceptible map of the southwest coastal region of India using synthetic-aperture radar (SAR) data and robust machine learning algorithms. Accurate flood and non-flood locations have been identified from the multi-temporal Sentinel-1 images. These flood locations are correlated with sixteen flood conditioning geo-environmental variables. The Boruta algorithm has been applied to determine the importance of each flood conditioning parameter. Six efficient machine learning models, namely support vector machine (SVM), k-nearest neighbors (KNN), artificial neural network (ANN), random forest (RF), partial least squares (PLS) and penalized discriminant analysis (PDA) have been applied to delineate the flood susceptible areas of the study region. The performance of the models has been evaluated using several statistical criteria, including area under curve (AUC), overall accuracy, specificity, sensitivity and kappa index. The results have revealed that all models have performed more than 90% of AUC due to the high precision of radar data. However, the RF and SVM models have outperformed other models in terms of all statistical parameters. The findings have identified approximately 13% of the study region as highly vulnerable to flood hazards, emphasizing the need for proper planning and management in these areas.</p></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"24 ","pages":"Article 100189"},"PeriodicalIF":2.6,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2590197424000363/pdfft?md5=c335020c63eb9eda70216e7662e23b2d&pid=1-s2.0-S2590197424000363-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142161206","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":"POSIT: An automated tool for detecting and characterizing diverse morphological features in raster data - Application to pockmarks, mounds, and craters","authors":"José J. Alonso del Rosario ,&nbsp;Ariadna Canari ,&nbsp;Elízabeth Blázquez Gómez ,&nbsp;Sara Martínez-Loriente","doi":"10.1016/j.acags.2024.100190","DOIUrl":"10.1016/j.acags.2024.100190","url":null,"abstract":"<div><p>Accurate detection and characterization of seafloor morphologies are crucial for marine researchers and industries involved in underwater mapping, environmental monitoring, or resource exploration. Although their detection has relied on visual inspection of detailed bathymetries, few efforts to automate the process can be found in the literature. This study presents a novel MatLab computer code called POSIT (Feature Signature Detection) based on the convolution and correlation with a structural element containing the shape to search for. POSIT is successfully tested on both synthetic and real datasets, encompassing marine and terrestrial digital elevation models of different resolution and on a digital image. The centroids of submarine pockmarks and mounds, terrestrial volcanic craters and lunar craters are calculated with zero dispersion and perfect location, and their geometric parameters and confidence are provided.</p></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"23 ","pages":"Article 100190"},"PeriodicalIF":2.6,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2590197424000375/pdfft?md5=dc86b3aae122c80855ab41c6633e87ec&pid=1-s2.0-S2590197424000375-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142096878","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":"Resolution enhancement and segmentation of polar bubbly ice micro CT scans via 3D convolutional neural network","authors":"Faramarz Bagherzadeh ,&nbsp;Johannes Freitag ,&nbsp;Udo Frese ,&nbsp;Frank Wilhelms","doi":"10.1016/j.acags.2024.100193","DOIUrl":"10.1016/j.acags.2024.100193","url":null,"abstract":"<div><p>Accurate segmentation of 3D micro CT scans is a key step in the process of analysis of the microstructure of porous materials. In polar ice core studies, the environmental effects on the firn column could be detected if the microstructure is digitized accurately. The most challenging task is to obtain the microstructure parameters of the bubbly ice section. To identify the minimum, necessary resolution, the bubbly ice micro CT scans with different resolutions (120, 60, 30, 12 <span><math><mrow><mi>μ</mi><mi>m</mi></mrow></math></span>) were compared object-wise via a region pairing algorithm. When the minimum resolution was found to be 60 <span><math><mrow><mi>μ</mi><mi>m</mi></mrow></math></span>, for generating the training/validation dataset, 4 ice core samples from a depth range of 96 to 108 meters (bubbly ice) were scanned with 120 <span><math><mrow><mi>μ</mi><mi>m</mi></mrow></math></span> (input images) and another time with 4 times higher resolution (30 <span><math><mrow><mi>μ</mi><mi>m</mi></mrow></math></span>) to build ground truth. A specific pipeline was designed with non-rigid image registration to create an accurate ground truth from 4 times higher resolution scans. Then, two SOTA deep learning models (3D-Unet and FCN) were trained and later validated to perform super-resolution segmentation by taking input of <span><math><mrow><mn>120</mn><mspace></mspace><mi>μ</mi><mi>m</mi></mrow></math></span> resolution data and giving the output of binary segmented with two times higher resolution (<span><math><mrow><mn>60</mn><mspace></mspace><mi>μ</mi><mi>m</mi></mrow></math></span>). Finally, the outputs of CNN models were compared with traditional rule-based and unsupervised methods on blind test data. It is observed the 3D-Unet can segment low-resolution scans with an accuracy of 96% and an f1-score of 80.8% while preserving microstructure having less than 2% error in porosity and SSA.</p></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"23 ","pages":"Article 100193"},"PeriodicalIF":2.6,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2590197424000405/pdfft?md5=436bc0a47d2a2e990851e57a7c794d0b&pid=1-s2.0-S2590197424000405-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142158096","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}