{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,20]],"date-time":"2025-08-20T13:16:58Z","timestamp":1755695818102},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,12,19]],"date-time":"2023-12-19T00:00:00Z","timestamp":1702944000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,19]],"date-time":"2023-12-19T00:00:00Z","timestamp":1702944000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Earth Sci Inform"],"published-print":{"date-parts":[[2024,2]]},"DOI":"10.1007\/s12145-023-01190-6","type":"journal-article","created":{"date-parts":[[2023,12,19]],"date-time":"2023-12-19T06:02:27Z","timestamp":1702965747000},"page":"655-678","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A deep learning-based technique for firm classification and domain adaptation in land cover classification using time-series aerial images"],"prefix":"10.1007","volume":"17","author":[{"given":"Indrajit","family":"Kalita","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shounak","family":"Chakraborty","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Talla Giridhara Ganesh","family":"Reddy","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Moumita","family":"Roy","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,12,19]]},"reference":[{"issue":"4","key":"1190_CR1","doi-asserted-by":"publisher","first-page":"597","DOI":"10.1109\/LGRS.2018.2800642","volume":"15","author":"N Ammour","year":"2018","unstructured":"Ammour N, Bashmal L, Bazi Y, Al Rahhal MM, Zuair M (2018) Asymmetric adaptation of deep features for cross-domain classification in remote sensing imagery. IEEE Geosci Remote Sens Lett 15(4):597\u2013601","journal-title":"IEEE Geosci Remote Sens Lett"},{"issue":"11","key":"1190_CR2","doi-asserted-by":"publisher","first-page":"4478","DOI":"10.1109\/JSTARS.2018.2874726","volume":"11","author":"D Ashourloo","year":"2018","unstructured":"Ashourloo D, Shahrabi HS, Azadbakht M, Aghighi H, Matkan AA, Radiom S (2018) A novel automatic method for alfalfa mapping using time series of landsat-8 oli data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 11(11):4478\u20134487","journal-title":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"},{"key":"1190_CR3","doi-asserted-by":"crossref","unstructured":"Bakhti K,\u00a0Djerriri K, Arabi MEA,\u00a0Chaib S, Karoui MS (2019) Improvememt of multi-temporal vegetation modeling using hybrid deep neural networks of multispectral remote sensing images. In: IEEE International Geoscience and Remote Sensing Symposium, pp 1\u20134. IEEE","DOI":"10.1109\/IGARSS40859.2019.8948702"},{"issue":"8","key":"1190_CR4","doi-asserted-by":"publisher","first-page":"1798","DOI":"10.1109\/TPAMI.2013.50","volume":"35","author":"Y Bengio","year":"2013","unstructured":"Bengio Y, Courville A, Vincent P (2013) Representation learning: A review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(8):1798\u20131828","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"4","key":"1190_CR5","doi-asserted-by":"publisher","first-page":"1108","DOI":"10.1109\/TGRS.2008.2007741","volume":"47","author":"L Bruzzone","year":"2009","unstructured":"Bruzzone L, Marconcini M (2009) Toward the automatic updating of land-cover maps by a domain-adaptation SVM classifier and a circular validation strategy. IEEE Trans Geosci Remote Sens 47(4):1108\u20131122","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"2","key":"1190_CR6","doi-asserted-by":"publisher","first-page":"456","DOI":"10.1109\/36.905255","volume":"39","author":"L Bruzzone","year":"2001","unstructured":"Bruzzone L, Prieto DF (2001) Unsupervised retraining of a maximum likelihood classifier for the analysis of multitemporal remote sensing images. IEEE Trans Geosci Remote Sens 39(2):456\u2013460","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"6","key":"1190_CR7","doi-asserted-by":"publisher","first-page":"1822","DOI":"10.1109\/TGRS.2008.916201","volume":"46","author":"G Camps-Valls","year":"2008","unstructured":"Camps-Valls G, Gomez-Chova L, Munoz-Mari J, Rojo-Alvarez JL, Martinez-Ramon M (2008) Kernel-based framework for multitemporal and multisource remote sensing data classification and change detection. IEEE Trans Geosci Remote Sens 46(6):1822\u20131835","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"1190_CR8","doi-asserted-by":"crossref","unstructured":"Castro J,\u00a0Feitosa R, Happ PN (2018) An hybrid recurrent convolutional neural network for crop type recognition based on multitemporal sar image sequences. In: In proceedings of IEEE International Geoscience and Remote Sensing Symposium, pp 3824\u20133827","DOI":"10.1109\/IGARSS.2018.8517280"},{"key":"1190_CR9","doi-asserted-by":"publisher","first-page":"508","DOI":"10.1016\/j.asoc.2017.12.018","volume":"64","author":"S Chakraborty","year":"2018","unstructured":"Chakraborty S, Roy M (2018) A neural approach under transfer learning for domain adaptation in land-cover classification using two-level cluster mapping. Appl Soft Comput 64:508\u2013525","journal-title":"Appl Soft Comput"},{"key":"1190_CR10","doi-asserted-by":"crossref","unstructured":"Chakraborty S,\u00a0Agarwal N,\u00a0Roy M (2020) A deep semi-supervised approach for multi-label land-cover classification under scarcity of labelled images. In: International Conference on Soft Computing for Problem Solving (SocProS), vol.\u00a010, pp (in press)","DOI":"10.1007\/978-981-16-2712-5_1"},{"key":"1190_CR11","unstructured":"Di Mauro N,\u00a0Vergari A, Basile TMA, Ventola FG,\u00a0Esposito F (2017) End\u2013to\u2013end learning of deep spatio-temporal representations for satellite image time series classification. In: DC@ PKDD\/ECML"},{"issue":"10","key":"1190_CR12","doi-asserted-by":"publisher","first-page":"2980","DOI":"10.1109\/TIP.2011.2134107","volume":"20","author":"B Geng","year":"2011","unstructured":"Geng B, Tao D, Xu C (2011) Daml: Domain adaptation metric learning. IEEE Trans Image Process 20(10):2980\u20132989","journal-title":"IEEE Trans Image Process"},{"key":"1190_CR13","volume-title":"Deep Learning","author":"I Goodfellow","year":"2016","unstructured":"Goodfellow I, Bengio Y, Courville A (2016) Deep Learning. MIT Press"},{"issue":"11","key":"1190_CR14","doi-asserted-by":"publisher","first-page":"2288","DOI":"10.1109\/TPAMI.2013.249","volume":"36","author":"R Gopalan","year":"2014","unstructured":"Gopalan R, Li R, Chellappa R (2014) Unsupervised adaptation across domain shifts by generating intermediate data representations. IEEE Trans Pattern Anal Mach Intell 36(11):2288\u20132302","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1190_CR15","doi-asserted-by":"crossref","unstructured":"Guo Y,\u00a0Jia X,\u00a0Paull D (2017) A domain-transfer support vector machine for multi-temporal remote sensing imagery classification. In: 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp 2215\u20132218","DOI":"10.1109\/IGARSS.2017.8127428"},{"key":"1190_CR16","doi-asserted-by":"crossref","unstructured":"Guo Y,\u00a0Jia X,\u00a0Paull D (2018) Mapping of rice varieties with sentinel-2 data via deep cnn learning in spectral and time domains. In: Digital Image Computing: Techniques and Applications (DICTA), pp 1\u20137","DOI":"10.1109\/DICTA.2018.8615872"},{"key":"1190_CR17","volume-title":"Neural Networks: A Comprehensive Foundation","author":"S Haykin","year":"2007","unstructured":"Haykin S (2007) Neural Networks: A Comprehensive Foundation. New Delhi, Prentice-Hall of India"},{"key":"1190_CR18","unstructured":"Ienco D,\u00a0Gaetano R (2007) Tiselac: time series land cover classification challenge. TiSeLaC: Time Series Land Cover Classification Challenge 2"},{"issue":"7","key":"1190_CR19","doi-asserted-by":"publisher","first-page":"1387","DOI":"10.1109\/LGRS.2015.2402167","volume":"12","author":"M Imani","year":"2015","unstructured":"Imani M, Ghassemian H (2015) Feature extraction using weighted training samples. IEEE Geoscience and Remote Sensing Letters 12(7):1387\u20131386","journal-title":"IEEE Geoscience and Remote Sensing Letters"},{"issue":"2065","key":"1190_CR20","doi-asserted-by":"publisher","first-page":"20150202","DOI":"10.1098\/rsta.2015.0202","volume":"374","author":"IT Jolliffe","year":"2016","unstructured":"Jolliffe IT, Cadima J (2016) Principal component analysis: a review and recent developments. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 374(2065):20150202","journal-title":"Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences"},{"issue":"2","key":"1190_CR21","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1109\/TAI.2020.3043724","volume":"1","author":"I Kalita","year":"2020","unstructured":"Kalita I, Roy M (2020) Deep neural network-based heterogeneous domain adaptation using ensemble decision making in land cover classification. IEEE Trans Artif Intell 1(2):167\u2013180","journal-title":"IEEE Trans Artif Intell"},{"issue":"12","key":"1190_CR22","doi-asserted-by":"publisher","first-page":"4604","DOI":"10.1109\/JSTARS.2018.2880783","volume":"11","author":"M Kim","year":"2018","unstructured":"Kim M, Lee J, Han D, Shin M, Im J, Lee J, Quackenbush LJ, Gu Z (2018) Convolutional neural network-based land cover classification using 2-D spectral reflectance curve graphs with multitemporal satellite imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 11(12):4604\u20134617","journal-title":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"},{"key":"1190_CR23","unstructured":"Krizhevsky A,\u00a0Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: International Conference on Neural Information Processing Systems - Vol 1, pp 1097\u20131105. ACM"},{"issue":"1","key":"1190_CR24","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1214\/aoms\/1177729694","volume":"22","author":"S Kullback","year":"1951","unstructured":"Kullback S, Leibler RA (1951) On information and sufficiency. The Annals of Mathematical Statistics 22(1):79\u201386","journal-title":"The Annals of Mathematical Statistics"},{"issue":"5","key":"1190_CR25","doi-asserted-by":"publisher","first-page":"778","DOI":"10.1109\/LGRS.2017.2681128","volume":"14","author":"N Kussul","year":"2017","unstructured":"Kussul N, Lavreniuk M, Skakun S, Shelestov A (2017) Deep learning classification of land cover and crop types using remote sensing data. IEEE Geosci Remote Sens Lett 14(5):778\u2013782","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"1190_CR26","doi-asserted-by":"crossref","unstructured":"Lavreniuk M,\u00a0Kussul N,\u00a0Novikov A (2018) Deep learning crop classification approach based on sparse coding of time series of satellite data. In: In proceedings of the IEEE International Geoscience and Remote Sensing Symposium, pp 4812\u20134815","DOI":"10.1109\/IGARSS.2018.8518263"},{"key":"1190_CR27","doi-asserted-by":"crossref","unstructured":"Liang P, Shi W, Zhang X (2018) Remote sensing image classification based on stacked denoising autoencoder. Remote Sens 10(1):","DOI":"10.3390\/rs10010016"},{"key":"1190_CR28","doi-asserted-by":"crossref","unstructured":"Lv Q, Dou Y, Niu X, Xu J, Xu J, Xia F (2015) Urban land use and land cover classification using remotely sensed sar data through deep belief networks. J Sensors 2015","DOI":"10.1155\/2015\/538063"},{"key":"1190_CR29","unstructured":"Maaten L,\u00a0Hinton G (2008) Visualizing data using t-SNE. J Mach Learn Res 9(Nov):2579\u20132605"},{"issue":"7","key":"1190_CR30","doi-asserted-by":"publisher","first-page":"3550","DOI":"10.1109\/TGRS.2014.2377785","volume":"53","author":"G Matasci","year":"2015","unstructured":"Matasci G, Volpi M, Kanevski M, Bruzzone L, Tuia D (2015) Semisupervised transfer component analysis for domain adaptation in remote sensing image classification. IEEE Trans Geosci Remote Sens 53(7):3550\u20133564","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"1190_CR31","doi-asserted-by":"crossref","unstructured":"McClellan, DeWitt, Hemmer, Matheson, Moe (1989) Multispectral image-processing with a three-layer backpropagation network. In: The Proceedings of the IEEE International Joint Conference on Neural Networks, pp 151\u2013153","DOI":"10.1109\/IJCNN.1989.118573"},{"issue":"6","key":"1190_CR32","doi-asserted-by":"publisher","first-page":"1881","DOI":"10.1109\/TGRS.2007.895836","volume":"45","author":"SK Meher","year":"2007","unstructured":"Meher SK, Shankar BU, Ghosh A (2007) Wavelet-Feature-Based classifiers for multispectral remote-sensing images. IEEE Trans Geosci Remote Sens 45(6):1881\u20131886","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"1190_CR33","unstructured":"Nguyen PL,\u00a0Ji Y et\u00a0al (2019) Deep convolutional lstm network-based traffic matrix prediction with partial information. In: IFIP\/IEEE Symposium on Integrated Network and Service Management (IM), pp 261\u2013269. IEEE"},{"issue":"3","key":"1190_CR34","first-page":"354","volume":"13","author":"E Othman","year":"2016","unstructured":"Othman E, Bazi Y, Alajlan N, AlHichri H, Melgani F (2016) Three-layer convex network for domain adaptation in multitemporal vhr images. IEEE Geosci Remote Sens Lett 13(3):354\u2013358","journal-title":"IEEE Geosci Remote Sens Lett"},{"issue":"8","key":"1190_CR35","doi-asserted-by":"publisher","first-page":"4441","DOI":"10.1109\/TGRS.2017.2692281","volume":"55","author":"E Othman","year":"2017","unstructured":"Othman E, Bazi Y, Melgani F, Alhichri H, Alajlan N, Zuair M (2017) Domain adaptation network for cross-scene classification. IEEE Trans Geosci Remote Sens 55(8):4441\u20134456","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"2","key":"1190_CR36","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1109\/TNN.2010.2091281","volume":"22","author":"SJ Pan","year":"2011","unstructured":"Pan SJ, Tsang IW, Kwok JT, Yang Q (2011) Domain adaptation via transfer component analysis. IEEE Transactions on Neural Networks 22(2):199\u2013210","journal-title":"IEEE Transactions on Neural Networks"},{"key":"1190_CR37","doi-asserted-by":"crossref","unstructured":"Postadjian T, Le Bris A, Sahbi H, Malle C (2018) Domain adaptation for large scale classification of very high resolution satellite images with deep convolutional neural networks. In: IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp 3623\u20133626","DOI":"10.1109\/IGARSS.2018.8518799"},{"key":"1190_CR38","doi-asserted-by":"crossref","unstructured":"Riz E,\u00a0Demir B,\u00a0Bruzzone L (2016) Domain adaptation based on deep denoising auto-encoders for classification of remote sensing images. In: The Proceedings of the SPIE Image and Signal Processing for Remote Sensing","DOI":"10.1117\/12.2241982"},{"key":"1190_CR39","unstructured":"Rukundo O, Maharaj BT (2014) Optimization of image interpolation based on nearest neighbour algorithm. International Conference on Computer Vision Theory and Applications (VISAPP) 1:641\u2013647"},{"key":"1190_CR40","doi-asserted-by":"crossref","unstructured":"Ru\u00dfwurm M,\u00a0K\u00f6rner M (2018) Multi\u2013temporal land cover classification with sequential recurrent encoders. ISPRS International Journal of Geo-Information 7 (4)","DOI":"10.3390\/ijgi7040129"},{"issue":"4","key":"1190_CR41","doi-asserted-by":"publisher","first-page":"549","DOI":"10.1109\/LGRS.2017.2657778","volume":"14","author":"GJ Scott","year":"2017","unstructured":"Scott GJ, England MR, Starms WA, Marcum RA, Davis CH (2017) Training deep convolutional neural networks for land-cover classification of high-resolution imagery. IEEE Geosci Remote Sens Lett 14(4):549\u2013553","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"1190_CR42","doi-asserted-by":"crossref","unstructured":"Senthilnath J, Omkar SN, Mani V, Tejovanth N, Diwakar PG, Shenoy A (2011) Multi-spectral satellite image classification using glowworm swarm optimization. In: The Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, pages 47\u201350","DOI":"10.1109\/IGARSS.2011.6048894"},{"key":"1190_CR43","unstructured":"Shi X,\u00a0Chen Z,\u00a0Wang H,\u00a0Yeung D,\u00a0Wong W,\u00a0Woo W (2015) Convolutional lstm network: A machine learning approach for precipitation nowcasting. In: Proceedings of the 28th International Conference on Neural Information Processing Systems \u2013 Volume 1, NIPS\u201915, page 802\u2013810, Cambridge, MA, USA. MIT Press"},{"issue":"2","key":"1190_CR44","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1109\/MGRS.2016.2548504","volume":"4","author":"D Tuia","year":"2016","unstructured":"Tuia D, Persello C, Bruzzone L (2016) Domain adaptation for the classification of remote sensing data: An overview of recent advances. IEEE Geoscience and Remote Sensing Magazine 4(2):41\u201357","journal-title":"IEEE Geoscience and Remote Sensing Magazine"},{"key":"1190_CR45","unstructured":"Xingjian S,\u00a0Chen Z,\u00a0Wang H,\u00a0Yeung D,\u00a0Wong W,\u00a0Woo W (2015) Convolutional lstm network: A machine learning approach for precipitation nowcasting. In: Advances in neural information processing systems, pp 802\u2013810"},{"issue":"1","key":"1190_CR46","doi-asserted-by":"publisher","first-page":"288","DOI":"10.1109\/TCYB.2016.2633306","volume":"48","author":"K Yan","year":"2017","unstructured":"Yan K, Kou L, Zhang D (2017) Learning domain-invariant subspace using domain features and independence maximization. IEEE Trans Cybern 48(1):288\u2013299","journal-title":"IEEE Trans Cybern"},{"issue":"9","key":"1190_CR47","doi-asserted-by":"publisher","first-page":"1775","DOI":"10.1080\/01431160110075802","volume":"23","author":"X Yang","year":"2002","unstructured":"Yang X, Lo CP (2002) Using a time series of satellite imagery to detect land use and land cover changes in the atlanta, georgia metropolitan area. Int J Remote Sens 23(9):1775\u20131798","journal-title":"Int J Remote Sens"},{"key":"1190_CR48","doi-asserted-by":"crossref","unstructured":"Yang Y,\u00a0Newsam S (2010) Bag\u2013of\u2013visual\u2013words and spatial extensions for land-use classification. In: SIGSPATIAL International Conference on Advances in Geographic Information Systems, pages 270\u2013279. ACM","DOI":"10.1145\/1869790.1869829"},{"issue":"11","key":"1190_CR49","doi-asserted-by":"publisher","first-page":"7920","DOI":"10.1109\/TGRS.2020.2985072","volume":"58","author":"J Zhang","year":"2020","unstructured":"Zhang J, Liu J, Pan B, Shi Z (2020) Domain adaptation based on correlation subspace dynamic distribution alignment for remote sensing image scene classification. IEEE Transactions on Geoscience and Remote Sensing 58(11):7920\u20137930","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"issue":"1","key":"1190_CR50","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1007\/s11704-015-4478-2","volume":"10","author":"Y Zheng","year":"2014","unstructured":"Zheng Y, Liu Q, Chen E, Ge Y, Zhao JL (2014) Exploiting multi-channels deep convolutional neural networks for multivariate time series classification. Front Comput Sci 10(1):96\u2013112","journal-title":"Front Comput Sci"},{"key":"1190_CR51","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1016\/j.patrec.2015.12.015","volume":"83","author":"L Zhu","year":"2016","unstructured":"Zhu L, Ma L (2016) Class centroid alignment based domain adaptation for classification of remote sensing images. Pattern Recogn Lett 83:124\u2013132","journal-title":"Pattern Recogn Lett"},{"key":"1190_CR52","doi-asserted-by":"publisher","first-page":"3251","DOI":"10.1109\/JSTARS.2021.3055784","volume":"14","author":"Y Zhu","year":"2021","unstructured":"Zhu Y, Gei\u00df C, So E, Jin Y (2021) Multitemporal relearning with convolutional lstm models for land use classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14:3251\u20133265","journal-title":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"}],"container-title":["Earth Science Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12145-023-01190-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12145-023-01190-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12145-023-01190-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,27]],"date-time":"2024-01-27T11:31:45Z","timestamp":1706355105000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12145-023-01190-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,19]]},"references-count":52,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,2]]}},"alternative-id":["1190"],"URL":"https:\/\/doi.org\/10.1007\/s12145-023-01190-6","relation":{},"ISSN":["1865-0473","1865-0481"],"issn-type":[{"value":"1865-0473","type":"print"},{"value":"1865-0481","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,19]]},"assertion":[{"value":"1 September 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 December 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 December 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no relevant financial or non-financial interests to disclose.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}