{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T17:29:49Z","timestamp":1782840589260,"version":"3.54.5"},"reference-count":51,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2024,4,28]],"date-time":"2024-04-28T00:00:00Z","timestamp":1714262400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Shandong Key Research and Development Project","award":["2018GNC110025"],"award-info":[{"award-number":["2018GNC110025"]}]},{"name":"Shandong Key Research and Development Project","award":["42301380"],"award-info":[{"award-number":["42301380"]}]},{"name":"Shandong Key Research and Development Project","award":["TSXZ201712"],"award-info":[{"award-number":["TSXZ201712"]}]},{"name":"Shandong Key Research and Development Project","award":["23-2-1-64-zyyd-jch"],"award-info":[{"award-number":["23-2-1-64-zyyd-jch"]}]},{"name":"Shandong Key Research and Development Project","award":["2023KJ232"],"award-info":[{"award-number":["2023KJ232"]}]},{"name":"National Natural Science Foundation of China","award":["2018GNC110025"],"award-info":[{"award-number":["2018GNC110025"]}]},{"name":"National Natural Science Foundation of China","award":["42301380"],"award-info":[{"award-number":["42301380"]}]},{"name":"National Natural Science Foundation of China","award":["TSXZ201712"],"award-info":[{"award-number":["TSXZ201712"]}]},{"name":"National Natural Science Foundation of China","award":["23-2-1-64-zyyd-jch"],"award-info":[{"award-number":["23-2-1-64-zyyd-jch"]}]},{"name":"National Natural Science Foundation of China","award":["2023KJ232"],"award-info":[{"award-number":["2023KJ232"]}]},{"name":"\u201cTaishan Scholar\u201d Project of Shandong Province","award":["2018GNC110025"],"award-info":[{"award-number":["2018GNC110025"]}]},{"name":"\u201cTaishan Scholar\u201d Project of Shandong Province","award":["42301380"],"award-info":[{"award-number":["42301380"]}]},{"name":"\u201cTaishan Scholar\u201d Project of Shandong Province","award":["TSXZ201712"],"award-info":[{"award-number":["TSXZ201712"]}]},{"name":"\u201cTaishan Scholar\u201d Project of Shandong Province","award":["23-2-1-64-zyyd-jch"],"award-info":[{"award-number":["23-2-1-64-zyyd-jch"]}]},{"name":"\u201cTaishan Scholar\u201d Project of Shandong Province","award":["2023KJ232"],"award-info":[{"award-number":["2023KJ232"]}]},{"name":"Qingdao Natural Science Foundation","award":["2018GNC110025"],"award-info":[{"award-number":["2018GNC110025"]}]},{"name":"Qingdao Natural Science Foundation","award":["42301380"],"award-info":[{"award-number":["42301380"]}]},{"name":"Qingdao Natural Science Foundation","award":["TSXZ201712"],"award-info":[{"award-number":["TSXZ201712"]}]},{"name":"Qingdao Natural Science Foundation","award":["23-2-1-64-zyyd-jch"],"award-info":[{"award-number":["23-2-1-64-zyyd-jch"]}]},{"name":"Qingdao Natural Science Foundation","award":["2023KJ232"],"award-info":[{"award-number":["2023KJ232"]}]},{"name":"Science and Technology Support Plan for Youth Innovation of Colleges and Universities of Shandong Province of China","award":["2018GNC110025"],"award-info":[{"award-number":["2018GNC110025"]}]},{"name":"Science and Technology Support Plan for Youth Innovation of Colleges and Universities of Shandong Province of China","award":["42301380"],"award-info":[{"award-number":["42301380"]}]},{"name":"Science and Technology Support Plan for Youth Innovation of Colleges and Universities of Shandong Province of China","award":["TSXZ201712"],"award-info":[{"award-number":["TSXZ201712"]}]},{"name":"Science and Technology Support Plan for Youth Innovation of Colleges and Universities of Shandong Province of China","award":["23-2-1-64-zyyd-jch"],"award-info":[{"award-number":["23-2-1-64-zyyd-jch"]}]},{"name":"Science and Technology Support Plan for Youth Innovation of Colleges and Universities of Shandong Province of China","award":["2023KJ232"],"award-info":[{"award-number":["2023KJ232"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Soil salinization has seriously affected agricultural production and ecological balance in the Yellow River Delta region. Rapid and accurate monitoring of soil salinity has become an urgent need. Traditional machine learning models tend to fall into local optimal values during the learning process, which reduces their accuracy. This paper introduces Circle map to enhance the crayfish optimization algorithm (COA), which is then integrated with the regularized extreme learning machine (RELM) model, aiming to improve the accuracy of soil salinity content (SSC) inversion in the Yellow River Delta region. We employed Landsat5 TM remote sensing images and measured salinity data to develop spectral indices, such as the band index, salinity index, vegetation index, and comprehensive index, selecting the optimal modeling variable group through Pearson correlation analysis and variable projection importance analysis. The back propagation neural network (BPNN), RELM, and improved crayfish optimization algorithm\u2013regularized extreme learning machine (ICOA-RELM) models were constructed using measured data and selected variable groups for SSC inversion. The results indicate that the ICOA-RELM model enhances the R2 value by an average of about 0.1 compared to other models, particularly those using groups of variables filtered by variable projection importance analysis as input variables, which showed the best inversion effect (test set R2 value of 0.75, MAE of 0.198, RMSE of 0.249). The SSC inversion results indicate a higher salinization degree in the coastal regions of the Yellow River Delta and a lower degree in the inland areas, with moderate saline soil and severe saline soil comprising 48.69% of the total area. These results are consistent with the actual sampling results, which verify the practicability of the model. This paper\u2019s methods and findings introduce an innovative and practical tool for monitoring and managing salinized soils in the Yellow River Delta, offering significant theoretical and practical benefits.<\/jats:p>","DOI":"10.3390\/rs16091565","type":"journal-article","created":{"date-parts":[[2024,4,29]],"date-time":"2024-04-29T04:26:16Z","timestamp":1714364776000},"page":"1565","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Soil Salinity Inversion in Yellow River Delta by Regularized Extreme Learning Machine Based on ICOA"],"prefix":"10.3390","volume":"16","author":[{"given":"Jiajie","family":"Wang","sequence":"first","affiliation":[{"name":"College of Computer Science & Technology, Qingdao University, Qingdao 266071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaopeng","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Computer Science & Technology, Qingdao University, Qingdao 266071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2894-9627","authenticated-orcid":false,"given":"Jiahua","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0133-8447","authenticated-orcid":false,"given":"Xiaodi","family":"Shang","sequence":"additional","affiliation":[{"name":"College of Computer Science & Technology, Qingdao University, Qingdao 266071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuyi","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Computer Science & Technology, Qingdao University, Qingdao 266071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yiping","family":"Feng","sequence":"additional","affiliation":[{"name":"College of Computer Science & Technology, Qingdao University, Qingdao 266071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bingbing","family":"Tian","sequence":"additional","affiliation":[{"name":"College of Computer Science & Technology, Qingdao University, Qingdao 266071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,28]]},"reference":[{"key":"ref_1","unstructured":"Rengasamy, P. (2016). Oxford Research Encyclopedia of Environmental Science, Oxford University Press."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"943","DOI":"10.1007\/s11442-014-1130-2","article-title":"Soil salinization research in China: Advances and prospects","volume":"24","author":"Li","year":"2014","journal-title":"J. Geogr. Sci."},{"key":"ref_3","first-page":"139","article-title":"Analysis on main contributors influencing soil salinization of Yellow River Delta","volume":"24","author":"Xiaomei","year":"2010","journal-title":"J. Soil Water Conserv."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1016\/S0065-2113(08)60795-6","article-title":"Electrical conductivity methods for measuring and mapping soil salinity","volume":"49","author":"Rhoades","year":"1993","journal-title":"Adv. Agron."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"105561","DOI":"10.1016\/j.catena.2021.105561","article-title":"Evolution of soil salinization under the background of landscape patterns in the irrigated northern slopes of Tianshan Mountains, Xinjiang, China","volume":"206","author":"Zhuang","year":"2021","journal-title":"CATENA"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S0034-4257(02)00188-8","article-title":"Remote sensing of soil salinity: Potentials and constraints","volume":"85","author":"Metternicht","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.pce.2010.12.004","article-title":"Characterizing soil salinity in irrigated agriculture using a remote sensing approach","volume":"55","author":"Abbas","year":"2013","journal-title":"Phys. Chem. Earth Parts A\/B\/C"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1080\/24749508.2018.1438744","article-title":"Remote sensing approach to assess salt-affected soils in the north-east part of Tadla plain, Morocco","volume":"2","author":"Ennaji","year":"2018","journal-title":"Geol. Ecol. Landscapes"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"106387","DOI":"10.1016\/j.agwat.2020.106387","article-title":"Soil salinity assessment using vegetation indices derived from Sentinel-2 multispectral data. Application to Lez\u00edria Grande, Portugal","volume":"241","author":"Ramos","year":"2020","journal-title":"Agric. Water Manag."},{"key":"ref_10","first-page":"205","article-title":"Extraction and analysis of soil salinization information of Alar reclamation area based on spectral index modeling","volume":"35","author":"Yunhao","year":"2023","journal-title":"Remote Sens. Nat. Resour."},{"key":"ref_11","first-page":"55","article-title":"Retrieving coastal soil saline based on landsat image in Chongming Dongtan","volume":"20","author":"Wang","year":"2018","journal-title":"J. Agric. Sci. Technol."},{"key":"ref_12","first-page":"349","article-title":"Remote sensing inversion and dynamic monitoring of soil salt in coastal saline area","volume":"35","author":"Zhang","year":"2018","journal-title":"J. Agric. Resour. Environ."},{"key":"ref_13","first-page":"102","article-title":"Soil salinity distribution based on remote sensing and its effect on crop growth in Hetao Irrigation District","volume":"34","author":"Huang","year":"2018","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_14","first-page":"1255","article-title":"PLSR-Based hyperspectral remote sensing retrieval of soil salinity of ChaKa-GongHe basin in QingHai province","volume":"47","author":"Weng","year":"2010","journal-title":"Acta Pedofil. Sin"},{"key":"ref_15","first-page":"598","article-title":"Multispectral remote sensing inversion and seasonal difference in soil salinity of cotton field in typical oasis irrigation area","volume":"40","author":"Liu","year":"2023","journal-title":"J. Agric. Resour. Environ."},{"key":"ref_16","first-page":"107","article-title":"Remote sensing inversion of saline soil salinity based on modified vegetation index in estuary area of Yellow River","volume":"31","author":"Hongyan","year":"2015","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_17","first-page":"61","article-title":"Machine Learning Inversion Model of Soil Salinity in the Yellow River Delta Based on Field Hyperspectral and UAV Multispectral Data","volume":"4","author":"Chengzhi","year":"2022","journal-title":"Smart Agric."},{"key":"ref_18","first-page":"867","article-title":"Estimation of soil electrical conductivity based on spectral index and machine learning algorithm","volume":"57","author":"Cao","year":"2020","journal-title":"Acta. Pedofil. Sin"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"107379","DOI":"10.1016\/j.knosys.2021.107379","article-title":"Improving streamflow prediction using a new hybrid ELM model combined with hybrid particle swarm optimization and grey wolf optimization","volume":"230","author":"Adnan","year":"2021","journal-title":"Knowl.-Based Syst."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1007\/s43393-022-00115-6","article-title":"Advances and applications of machine learning and intelligent optimization algorithms in genome-scale metabolic network models","volume":"3","author":"Bai","year":"2023","journal-title":"Syst. Microbiol. Biomanuf."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.neucom.2016.09.092","article-title":"An improved incremental constructive single-hidden-layer feedforward networks for extreme learning machine based on particle swarm optimization","volume":"228","author":"Han","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"103980","DOI":"10.1016\/j.engappai.2020.103980","article-title":"Evolutionary Extreme Learning Machine with novel activation function for credit scoring","volume":"96","author":"Tripathi","year":"2020","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1635","DOI":"10.1007\/s11069-022-05361-4","article-title":"Drought prediction in the Yunnan\u2013Guizhou Plateau of China by coupling the estimation of distribution algorithm and the extreme learning machine","volume":"113","author":"Li","year":"2022","journal-title":"Nat. Hazards"},{"key":"ref_24","first-page":"190","article-title":"Drought prediction model based on genetic algorithm optimization support vector machine (SVM)","volume":"44","author":"Chi","year":"2013","journal-title":"J. Shenyang Agric. Univ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"119468","DOI":"10.1016\/j.jclepro.2019.119468","article-title":"Random forest regression evaluation model of regional flood disaster resilience based on the whale optimization algorithm","volume":"250","author":"Liu","year":"2020","journal-title":"J. Clean. Prod."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"6038","DOI":"10.1109\/JSTARS.2023.3284019","article-title":"Soil Salinity Inversion Model Based on BPNN Optimization Algorithm for UAV Multispectral Remote Sensing","volume":"16","author":"Zhao","year":"2023","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_27","first-page":"1662","article-title":"Retrieval of soil salinity content based on random forests regression optimized by Bayesian optimization algorithm and genetic algorithm","volume":"23","author":"Yang","year":"2021","journal-title":"J. Geo-Inf. Sci."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1007\/s12665-021-09752-x","article-title":"Soil salinity inversion based on novel spectral index","volume":"80","author":"Zhou","year":"2021","journal-title":"Environ. Earth Sci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"107618","DOI":"10.1016\/j.agwat.2022.107618","article-title":"Estimate soil moisture of maize by combining support vector machine and chaotic whale optimization algorithm","volume":"267","author":"He","year":"2022","journal-title":"Agric. Water Manag."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1919","DOI":"10.1007\/s10462-023-10567-4","article-title":"Crayfish optimization algorithm","volume":"56","author":"Jia","year":"2023","journal-title":"Artif. Intell. Rev."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"406","DOI":"10.1016\/S0034-4257(01)00321-2","article-title":"Field-derived spectra of salinized soils and vegetation as indicators of irrigation-induced soil salinization","volume":"80","author":"Dehaan","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2051","DOI":"10.1080\/01431169208904252","article-title":"The selection of the best possible Landsat TM band combination for delineating salt-affected soils","volume":"13","author":"Dwivedi","year":"1992","journal-title":"Int. J. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1016\/j.geoderma.2014.07.028","article-title":"Monitoring and evaluating spatial variability of soil salinity in dry and wet seasons in the Werigan\u2013Kuqa Oasis, China, using remote sensing and electromagnetic induction instruments","volume":"235","author":"Ding","year":"2014","journal-title":"Geoderma"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/j.geoderma.2005.10.009","article-title":"Detecting salinity hazards within a semiarid context by means of combining soil and remote-sensing data","volume":"134","author":"Nicolas","year":"2006","journal-title":"Geoderma"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2795","DOI":"10.1080\/00103620802432717","article-title":"Characterization of slightly and moderately saline and sodic soils in irrigated agricultural land using simulated data of advanced land imaging (EO-1) sensor","volume":"39","author":"Bannari","year":"2008","journal-title":"Commun. Soil Sci. Plant Anal."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"24269","DOI":"10.1007\/s11356-021-17677-y","article-title":"Monitoring soil salinization and its spatiotemporal variation at different depths across the Yellow River Delta based on remote sensing data with multi-parameter optimization","volume":"29","author":"Cheng","year":"2022","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.geoderma.2014.03.025","article-title":"Assessing soil salinity using soil salinity and vegetation indices derived from IKONOS high-spatial resolution imageries: Applications in a date palm dominated region","volume":"230","author":"Allbed","year":"2014","journal-title":"Geoderma"},{"key":"ref_38","first-page":"211","article-title":"Remote sensing extraction of soil salinity in Yellow River Delta Kenli County based on feature space","volume":"35","author":"Bian","year":"2020","journal-title":"Remote Sens. Technol. Appl."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/S0034-4257(00)00205-4","article-title":"Narrowband to broadband conversions of land surface albedo I: Algorithms","volume":"76","author":"Liang","year":"2001","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1007\/s12524-019-01100-8","article-title":"Estimating soil salinity in the dried lake bed of Urmia Lake using optical Sentinel-2 images and nonlinear regression models","volume":"48","author":"Farahmand","year":"2020","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_41","first-page":"5005","article-title":"Inversion of soil salinity in coastal winter wheat growing area based on sentinel satellite and unmanned aerial vehicle multi-spectrum\u2014A case study in Kenli district of the Yellow River delta","volume":"53","author":"Xi","year":"2020","journal-title":"Sci. Agric. Sin."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1745","DOI":"10.1080\/01431160701395195","article-title":"Detecting date palm trees health and vegetation greenness change on the eastern coast of the United Arab Emirates using SAVI","volume":"29","author":"Alhammadi","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"644","DOI":"10.1016\/j.jaridenv.2005.08.005","article-title":"Mapping soil salinity using a combined spectral response index for bare soil and vegetation: A case study in the former lake Texcoco, Mexico","volume":"65","author":"Siebe","year":"2006","journal-title":"J. Arid. Environ."},{"key":"ref_44","first-page":"499","article-title":"Soil salinization in the irrigated area of the Manas River basin based on MSAVI-SI feature space","volume":"33","author":"Zhang","year":"2016","journal-title":"Arid Zone Res."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"3716","DOI":"10.1016\/j.neucom.2011.06.013","article-title":"Regularized extreme learning machine for regression problems","volume":"74","year":"2011","journal-title":"Neurocomputing"},{"key":"ref_46","first-page":"127","article-title":"Regional soil salinity monitoring based on multi-source collaborative remote sensing data","volume":"49","author":"Feng","year":"2018","journal-title":"Trans. Chin. Soc. Agric. Mach."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"107301","DOI":"10.1016\/j.catena.2023.107301","article-title":"The salinization process and its response to the combined processes of climate change\u2013human activity in the Yellow River Delta between 1984 and 2022","volume":"231","author":"Guo","year":"2023","journal-title":"Catena"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"111260","DOI":"10.1016\/j.rse.2019.111260","article-title":"Global mapping of soil salinity change","volume":"231","author":"Ivushkin","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"3795","DOI":"10.1007\/s11053-021-09876-8","article-title":"Remote sensing inversion of saline and alkaline land based on an improved seagull optimization algorithm and the two-hidden-layer extreme learning machine","volume":"30","author":"Xiao","year":"2021","journal-title":"Nat. Resour. Res."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"74340","DOI":"10.1007\/s11356-023-27516-x","article-title":"Soil salinity prediction using hybrid machine learning and remote sensing in Ben Tre province on Vietnam\u2019s Mekong River Delta","volume":"30","author":"Nguyen","year":"2023","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_51","first-page":"102","article-title":"Environmental sensitive variable optimization and machine learning algorithm using in soil salt prediction at oasis","volume":"34","author":"Wang","year":"2018","journal-title":"Trans. Chin. Soc. Agric. Eng"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/9\/1565\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:35:12Z","timestamp":1760106912000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/9\/1565"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,28]]},"references-count":51,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2024,5]]}},"alternative-id":["rs16091565"],"URL":"https:\/\/doi.org\/10.3390\/rs16091565","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,28]]}}}