{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T15:41:10Z","timestamp":1776181270047,"version":"3.50.1"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"19","license":[{"start":{"date-parts":[[2024,4,17]],"date-time":"2024-04-17T00:00:00Z","timestamp":1713312000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,4,17]],"date-time":"2024-04-17T00:00:00Z","timestamp":1713312000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100002322","name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior","doi-asserted-by":"publisher","award":["Financial code - 001"],"award-info":[{"award-number":["Financial code - 001"]}],"id":[{"id":"10.13039\/501100002322","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2024,7]]},"DOI":"10.1007\/s00521-024-09793-w","type":"journal-article","created":{"date-parts":[[2024,4,17]],"date-time":"2024-04-17T10:33:42Z","timestamp":1713350022000},"page":"11643-11657","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Self-organizing maps applied to the analysis and identification of characteristics related to air quality monitoring stations and its pollutants"],"prefix":"10.1007","volume":"36","author":[{"given":"Emanoel L. R.","family":"Costa","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Taiane","family":"Braga","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Leonardo A.","family":"Dias","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"\u00c9dler L.","family":"de Albuquerque","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7536-2506","authenticated-orcid":false,"given":"Marcelo A. C.","family":"Fernandes","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,4,17]]},"reference":[{"key":"9793_CR1","doi-asserted-by":"crossref","first-page":"462","DOI":"10.1016\/S0140-6736(17)32345-0","volume":"391","author":"PJ Landrigan","year":"2017","unstructured":"Landrigan PJ, Fuller R, Acosta NJR, Adeyi O (2017) The lancet commission on pollution and health. Lancet 391:462\u2013512","journal-title":"Lancet"},{"key":"9793_CR2","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1126\/science.aap7711","volume":"359","author":"JG Zivin","year":"2018","unstructured":"Zivin JG, Neidell M (2018) Air pollution\u2019s hidden impacts. Science 359:39\u201340","journal-title":"Science"},{"key":"9793_CR3","doi-asserted-by":"crossref","first-page":"460","DOI":"10.3322\/caac.21632","volume":"70","author":"MC Turner","year":"2020","unstructured":"Turner MC, Andersen ZJ, Diver WR, Gapstur SM, Pope CA III, Prada D, Samet J, Thurston G, Cohen A (2020) Outdoor air pollution and cancer: an overview of the current evidence and public health recommendations. CA Cancer J Clin 70:460\u2013479","journal-title":"CA Cancer J Clin"},{"key":"9793_CR4","first-page":"37","volume":"144","author":"J Zhang","year":"2016","unstructured":"Zhang J, Zhang L, Du M, Zhang W, Huang X, Zhang Y, Yang Y, Zhang JM, Deng S, Shen F, Li Y, Xiao H (2016) Indentifying the major air pollutants base on factor and cluster analysis, a case study in 74 Chinese cities. Science 144:37\u201346","journal-title":"Science"},{"key":"9793_CR5","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1016\/j.scitotenv.2013.01.074","volume":"450","author":"K Zhang","year":"2013","unstructured":"Zhang K, Batterman S (2013) Air pollution and health risks due to vehicle traffic. Sci Total Environ 450:307\u2013316","journal-title":"Sci Total Environ"},{"key":"9793_CR6","doi-asserted-by":"crossref","first-page":"307","DOI":"10.3390\/ijerph15020307","volume":"15","author":"L Bai","year":"2018","unstructured":"Bai L, Wang J, Ma X, Lu H (2018) Air pollution forecasts: an overview. Int J Environ Res Public Health 15:307\u2013316","journal-title":"Int J Environ Res Public Health"},{"key":"9793_CR7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2019\/9753927","volume":"2019","author":"D N\u00fa\u00f1ez-Alonso","year":"2018","unstructured":"N\u00fa\u00f1ez-Alonso D, P\u00e9rez-Arribas LV, Manzoor S, C\u00e1ceres JO (2018) Statistical tools for air pollution assessment: multivariate and spatial analysis studies in the Madrid Region. J Anal Methods Chem 2019:1\u20139","journal-title":"J Anal Methods Chem"},{"key":"9793_CR8","first-page":"1","volume":"125","author":"D Tian","year":"2020","unstructured":"Tian D, Fan J, Jin H, Mao H, Geng D, Hou S, Zhang P, Zhang Y (2020) Characteristic and spatiotemporal variation of air pollution in northern China based on correlation analysis and clustering analysis of five air pollutants. J Geophys Res Atmosph 125:1\u201312","journal-title":"J Geophys Res Atmosph"},{"key":"9793_CR9","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1016\/j.physa.2018.02.160","volume":"502","author":"P Manimaran","year":"2018","unstructured":"Manimaran P, Narayana AC (2018) Multifractal detrended cross-correlation analysis on air pollutants of University of Hyderabad Campus, India. Phys A 502:228\u2013235","journal-title":"Phys A"},{"key":"9793_CR10","doi-asserted-by":"crossref","first-page":"360","DOI":"10.3390\/ijerph17010360","volume":"17","author":"Y Bai","year":"2020","unstructured":"Bai Y, Jin X, Wang XY, Wang J, Xu J (2020) Dynamic correlation analysis method of air pollutants in spatio-temporal analysis. Int J Environ Res Public Health 17:360","journal-title":"Int J Environ Res Public Health"},{"key":"9793_CR11","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.envint.2015.11.003","volume":"86","author":"S Zhao","year":"2016","unstructured":"Zhao S, Yu Y, Yin D, He J, Liu N, Qu J, Xiao J (2016) Annual and diurnal variations of gaseous and particulate pollutants in 31 provincial capital cities based on in situ air quality monitoring data from China National Environmental Monitoring Center. Environ Int 86:92\u2013106","journal-title":"Environ Int"},{"key":"9793_CR12","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1007\/s11869-016-0432-1","volume":"10","author":"D Yin","year":"2016","unstructured":"Yin D, Zhao S, Qu J (2016) Spatial and seasonal variations of gaseous and particulate matter pollutants in 31 provincial capital cities, China. Air Qual Atmosph Health 10:359\u2013370","journal-title":"Air Qual Atmosph Health"},{"key":"9793_CR13","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1016\/j.buildenv.2018.06.038","volume":"147","author":"C Li","year":"2019","unstructured":"Li C, Wang Z, Li B, Peng Z, Fu Q (2019) Investigating the relationship between air pollution variation and urban form. Build Environ 147:559\u2013568","journal-title":"Build Environ"},{"key":"9793_CR14","doi-asserted-by":"crossref","first-page":"553","DOI":"10.1080\/00032719.2014.951448","volume":"48","author":"N Peri\u0161","year":"2015","unstructured":"Peri\u0161 N, Buljac M, Brali\u0107 M, Buzuk M, Brini\u0107 S, Plazibat I (2015) Characterization of the air quality in split, croatia focusing upon fine and coarse particulate matter analysis. Anal Lett 48:553\u2013565","journal-title":"Anal Lett"},{"key":"9793_CR15","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1016\/j.atmosenv.2018.07.040","volume":"190","author":"C Wang","year":"2018","unstructured":"Wang C, Zhao L, Sun W, Xue J, Xie Y (2018) Identifying redundant monitoring stations in an air quality monitoring network. Atmos Environ 190:256\u2013268","journal-title":"Atmos Environ"},{"issue":"9","key":"9793_CR16","doi-asserted-by":"crossref","first-page":"22863","DOI":"10.1007\/s11356-022-23686-2","volume":"30","author":"S Samani","year":"2023","unstructured":"Samani S, Vadiati M, Nejatijahromi Z, Etebari B, Kisi O (2023) Groundwater level response identification by hybrid wavelet-machine learning conjunction models using meteorological data. Environ Sci Pollut Res 30(9):22863\u201322884","journal-title":"Environ Sci Pollut Res"},{"key":"9793_CR17","doi-asserted-by":"crossref","first-page":"1895","DOI":"10.1007\/s11600-022-00964-8","volume":"71","author":"S Samani","year":"2022","unstructured":"Samani S, Vadiati M, Delkash M, Bonakdari H (2022) A hybrid wavelet-machine learning model for qanat water flow prediction. Acta Geophys 71:1895","journal-title":"Acta Geophys"},{"key":"9793_CR18","doi-asserted-by":"crossref","first-page":"1151","DOI":"10.1162\/NECO_a_00947","volume":"29","author":"HuB Ran Zhi-Yong","year":"2017","unstructured":"Ran Zhi-Yong HuB (2017) Parameter identifiability in statistical machine learning: a review. Neural Comput 29:1151\u20131203","journal-title":"Neural Comput"},{"key":"9793_CR19","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-642-56927-2","volume-title":"Self-organizing maps","author":"T Kohonen","year":"2001","unstructured":"Kohonen T (2001) Self-organizing maps, 3rd edn. Springer-Verlag, Berlin, Germany","edition":"3"},{"key":"9793_CR20","volume-title":"An introduction to self-organizing maps","author":"U Asan","year":"2012","unstructured":"Asan U, Ercan S (2012) An introduction to self-organizing maps, 3rd edn. Atlantis Press, Paris, France","edition":"3"},{"key":"9793_CR21","doi-asserted-by":"crossref","first-page":"1610","DOI":"10.1007\/s11442-019-1644-8","volume":"29","author":"X Liao","year":"2019","unstructured":"Liao X, Tao H, Gong X, Li Y (2019) Exploring the database of a soil environmental survey using a geo-self-organizing map: a pilot study. J Geog Sci 29:1610\u20131624","journal-title":"J Geog Sci"},{"key":"9793_CR22","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1111\/sum.12169","volume":"31","author":"D Rivera","year":"2015","unstructured":"Rivera D, Sandoval M, Godoy A (2015) Exploring soil databases: a self-organizing map approach. Soil Use Manag 31:121\u2013131","journal-title":"Soil Use Manag"},{"key":"9793_CR23","doi-asserted-by":"crossref","first-page":"5805","DOI":"10.1007\/s12665-015-4598-x","volume":"74","author":"HY Zhou","year":"2015","unstructured":"Zhou HY, Wang XS, Shan AQ (2015) Discriminating soil-contamination sources using combination of magnetic parameters. Environ Earth Sci 74:5805\u20135811","journal-title":"Environ Earth Sci"},{"key":"9793_CR24","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1016\/j.jhydrol.2018.12.031","volume":"569","author":"K Lee","year":"2019","unstructured":"Lee K, Yun S, Yu S, Kim K, Lee J, Lee S (2019) The combined use of self-organizing map technique and fuzzy c-means clustering to evaluate urban groundwater quality in Seoul metropolitan city, South Korea. J Hydrol 569:685\u2013697","journal-title":"J Hydrol"},{"key":"9793_CR25","doi-asserted-by":"crossref","first-page":"1446","DOI":"10.1016\/j.scitotenv.2018.02.163","volume":"628","author":"T Li","year":"2018","unstructured":"Li T, Sun G, Yang C, Liang K, Ma S, Huang L (2018) Using self-organizing map for coastal water quality classification: towards a better understanding of patterns and processes. Sci Total Environ 628:1446\u20131459","journal-title":"Sci Total Environ"},{"key":"9793_CR26","volume":"11","author":"R Chea","year":"2016","unstructured":"Chea R, Grenouillet G, Lek S (2016) Evidence of water quality degradation in lower mekong basin revealed by self-organizing map. Public Lib Sci 11:e0145527","journal-title":"Public Lib Sci"},{"key":"9793_CR27","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.agee.2018.11.021","volume":"272","author":"Y Li","year":"2019","unstructured":"Li Y, Wright A, Liu H, Wang J, Wang G, Wu Y, Dai L (2019) Land use pattern, irrigation, and fertilization effects of rice-wheat rotation on water quality of ponds by using self-organizing map in agricultural watersheds. Agric Ecosyst Environ 272:155\u2013164","journal-title":"Agric Ecosyst Environ"},{"key":"9793_CR28","doi-asserted-by":"crossref","first-page":"591","DOI":"10.1016\/j.scitotenv.2015.11.063","volume":"543","author":"P Zhou","year":"2016","unstructured":"Zhou P, Huang J, Pontius RG, Hong H (2016) New insight into the correlations between land use and water quality in a coastal watershed of China: Does point source pollution weaken it? Sci Total Environ 543:591\u2013600","journal-title":"Sci Total Environ"},{"key":"9793_CR29","first-page":"487","volume":"55","author":"I Osemwegie","year":"2017","unstructured":"Osemwegie I, Niamien-Ebrottie J, Kon\u00e9 M, Ouattara A, Bi\u00e9mi J, Reichert B (2017) Characterization of phytoplankton assemblages in a tropical coastal environment using Kohonen self-organizing map. Sci Total Environ 55:487\u2013499","journal-title":"Sci Total Environ"},{"key":"9793_CR30","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.atmosenv.2017.08.014","volume":"167","author":"B Zhong","year":"2017","unstructured":"Zhong B, Wang L, Liang T, Xing B (2017) Pollution level and inhalation exposure of ambient aerosol fluoride as affected by polymetallic rare earth mining and smelting in Baotou, north China. Atmos Environ 167:40\u201348","journal-title":"Atmos Environ"},{"key":"9793_CR31","doi-asserted-by":"crossref","first-page":"1211","DOI":"10.1002\/joc.4770","volume":"37","author":"N Jiang","year":"2017","unstructured":"Jiang N, Scorgie Y, Hart M, Riley ML, Crawford J, Beggs PJ, Edwards GC, Chang L, Salter D, Virgilio GD (2017) Visualising the relationships between synoptic circulation type and air quality in Sydney, a subtropical coastal-basin environment. Int J Climatol 37:1211\u20131228","journal-title":"Int J Climatol"},{"key":"9793_CR32","doi-asserted-by":"crossref","first-page":"3563","DOI":"10.5194\/amt-8-3563-2015","volume":"8","author":"V Moosavi","year":"2015","unstructured":"Moosavi V, Aschwanden G, Velasco E (2015) Finding candidate locations for aerosol pollution monitoring at street level using a data-driven methodology. Atmosph Measurem Tech 8:3563\u20133575","journal-title":"Atmosph Measurem Tech"},{"key":"9793_CR33","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/j.jhazmat.2015.05.015","volume":"297","author":"S Kwon","year":"2015","unstructured":"Kwon S, Jeong W, Park D, Kim K, Cho KH (2015) A multivariate study for characterizing particulate matter (PM10, PM2.5, and PM1) in Seoul metropolitan subway stations, Korea. J Hazard Mater 297:295\u2013303","journal-title":"J Hazard Mater"},{"key":"9793_CR34","doi-asserted-by":"crossref","DOI":"10.1007\/978-981-15-9605-6","volume":"736","author":"F Chang","year":"2020","unstructured":"Chang F, Chang L, Kang C, Wang Y, Huang A (2020) Explore spatio-temporal PM2.5 features in northern Taiwan using machine learning techniques. Sci Environ 736:139656","journal-title":"Sci Environ"},{"key":"9793_CR35","doi-asserted-by":"publisher","DOI":"10.1007\/s10661-020-08552-3","author":"D Li","year":"2020","unstructured":"Li D, Liao Y (2020) Pollution zone identification research during ozone pollution processes. Environ Monitor Assessment. https:\/\/doi.org\/10.1007\/s10661-020-08552-3","journal-title":"Environ Monitor Assessment"},{"key":"9793_CR36","volume":"250","author":"L Gao","year":"2023","unstructured":"Gao L, Zhang W, Liu Q, Lin X, Huang Y, Zhang X (2023) Machine learning based on the graph convolutional self-organizing map method increases the accuracy of pollution source identification: A case study of trace metal(loid)s in soils of Jiangmen City, south China. Ecotoxicol Environ Saf 250:114467","journal-title":"Ecotoxicol Environ Saf"},{"key":"9793_CR37","doi-asserted-by":"crossref","DOI":"10.1016\/j.scitotenv.2023.163084","volume":"878","author":"S Licen","year":"2023","unstructured":"Licen S, Astel A, Tsakovski S (2023) Self-organizing map algorithm for assessing spatial and temporal patterns of pollutants in environmental compartments: A review. Sci Total Environ 878:163084","journal-title":"Sci Total Environ"},{"key":"9793_CR38","doi-asserted-by":"crossref","DOI":"10.1016\/j.scitotenv.2023.163084","volume":"878","author":"S Licen","year":"2023","unstructured":"Licen S, Astel A, Tsakovski S (2023) Self-organizing map algorithm for assessing spatial and temporal patterns of pollutants in environmental compartments: A review. Sci Total Environ 878:163084","journal-title":"Sci Total Environ"},{"key":"9793_CR39","unstructured":"Brazilian Institute of Geography and Statistics (IBGE). Brazilian Census 2020. Brazilian Institute of Geography and Statistics, 2020. Bras\u00edlia, Brazil: IBGE. Available online: https:\/\/www.ibge.gov.br\/en\/statistics\/social\/population\/25071-2020-census.html?= &t=o-que-e"},{"key":"9793_CR40","volume-title":"Geografia de Salvador","author":"AD Andrade","year":"2009","unstructured":"Andrade AD, Brand\u00e3o PRB (2009) Geografia de Salvador, 2nd edn. Salvador, Brazil, EDUFBA","edition":"2"},{"key":"9793_CR41","volume-title":"Neural networks and learning machines","author":"S Haykin","year":"2009","unstructured":"Haykin S (2009) Neural networks and learning machines, 3rd edn. New Jersey, USA, Pearson Education","edition":"3"},{"key":"9793_CR42","doi-asserted-by":"crossref","first-page":"586","DOI":"10.1109\/72.846731","volume":"11","author":"J Vesanto","year":"2000","unstructured":"Vesanto J, Alhoniemi E (2000) Clustering of the self-organizing map. IEEE Trans Neural Networks 11:586\u2013600","journal-title":"IEEE Trans Neural Networks"},{"key":"9793_CR43","doi-asserted-by":"crossref","unstructured":"Davies DL, Bouldin DWA (1979) Cluster separation measure. IEEE Transactions on pattern analysis and machine intelligence. PAMI-1, 224\u2013227","DOI":"10.1109\/TPAMI.1979.4766909"},{"key":"9793_CR44","volume-title":"Multivariate data analysis","author":"JF Hair","year":"2009","unstructured":"Hair JF (2009) Multivariate data analysis, 7th edn. Prentice Hall, New Jersey, USA","edition":"7"},{"key":"9793_CR45","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.jvolgeores.2016.04.014","volume":"320","author":"K Unglert","year":"2016","unstructured":"Unglert K, Radi\u0107 V, Jellinek AM (2016) Principal component analysis vs. self-organizing maps combined with hierarchical clustering for pattern recognition in volcano seismic spectra. J Volcanol Geothermal Res 320:58\u201374","journal-title":"J Volcanol Geothermal Res"},{"key":"9793_CR46","doi-asserted-by":"publisher","DOI":"10.1109\/TCE.2024.3354073","author":"L Wang","year":"2024","unstructured":"Wang L, Jin X, Huang Z, Zhu H, Chen Z (2024) Short-Term PM2.5 prediction based on multi-modal meteorological data for consumer-grade meteorological electronic systems. IEEE Trans. https:\/\/doi.org\/10.1109\/TCE.2024.3354073","journal-title":"IEEE Trans"},{"key":"9793_CR47","doi-asserted-by":"crossref","unstructured":"Elmi Abdi M, Ahmad D, Abd Ghani IF (2024) Correlation study on water quality and indoor environment parameters of aquaponic systems using statistical and machine learning techniques. SSRN","DOI":"10.2139\/ssrn.4716148"},{"key":"9793_CR48","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-023-09622-7","author":"W Dang","year":"2024","unstructured":"Dang W, Kim S, Park SJ, Xu W (2024) The impact of economic and IoT technologies on air pollution: an AI-based simulation equation model using support vector machines. Soft Comput. https:\/\/doi.org\/10.1007\/s00500-023-09622-7","journal-title":"Soft Comput"},{"key":"9793_CR49","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1002\/wics.101","volume":"2","author":"H Abdi","year":"2010","unstructured":"Abdi H, Williams LJ (2010) Principal component analysis. WIREs. Comput Stat 2:433\u2013459","journal-title":"Comput Stat"},{"key":"9793_CR50","doi-asserted-by":"crossref","DOI":"10.1016\/j.jhydrol.2020.125581","volume":"591","author":"AH Baghanam","year":"2020","unstructured":"Baghanam AH, Nourani V, Aslani H, Taghipour H (2020) Spatiotemporal variation of water pollution near landfill site: application of clustering methods to assess the admissibility of LWPI. J Hydrol 591:125581","journal-title":"J Hydrol"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-09793-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-024-09793-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-09793-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,25]],"date-time":"2024-06-25T11:18:42Z","timestamp":1719314322000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-024-09793-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,17]]},"references-count":50,"journal-issue":{"issue":"19","published-print":{"date-parts":[[2024,7]]}},"alternative-id":["9793"],"URL":"https:\/\/doi.org\/10.1007\/s00521-024-09793-w","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,17]]},"assertion":[{"value":"14 December 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 March 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 April 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declaration"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"All authors agreed with the content and gave explicit consent to submit.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}]}}