{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T05:26:14Z","timestamp":1777958774399,"version":"3.51.4"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2024,6,28]],"date-time":"2024-06-28T00:00:00Z","timestamp":1719532800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,6,28]],"date-time":"2024-06-28T00:00:00Z","timestamp":1719532800000},"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":["SIViP"],"published-print":{"date-parts":[[2024,9]]},"DOI":"10.1007\/s11760-024-03384-x","type":"journal-article","created":{"date-parts":[[2024,6,28]],"date-time":"2024-06-28T10:02:16Z","timestamp":1719568936000},"page":"7183-7197","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["3ENB2: end-to-end EfficientNetB2 model with online data augmentation for fire detection"],"prefix":"10.1007","volume":"18","author":[{"given":"Ehsanullah","family":"Zia","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hamed","family":"Vahdat-Nejad","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammad Ali","family":"Zeraatkar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Javad Hassannataj","family":"Joloudari","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Seyyed Ali","family":"Hoseini","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,6,28]]},"reference":[{"key":"3384_CR1","doi-asserted-by":"crossref","unstructured":"Ghosh, R., Kumar, A.: A hybrid deep learning model by combining convolutional neural network and recurrent neural network to detect forest fire. Multimedia Tools Appl. 81(27), 38643\u201338660 (2022)","DOI":"10.1007\/s11042-022-13068-8"},{"issue":"1","key":"3384_CR2","first-page":"1","volume":"13","author":"P Dasari","year":"2020","unstructured":"Dasari, P., Reddy, G.K.J., Gudipalli, A.: Forest fire detection using wireless sensor networks. Int. J. Smart Sens. Intell. Syst. 13(1), 1\u20138 (2020)","journal-title":"Int. J. Smart Sens. Intell. Syst."},{"key":"3384_CR3","doi-asserted-by":"publisher","first-page":"103803","DOI":"10.1016\/j.imavis.2019.08.007","volume":"91","author":"F Bu","year":"2019","unstructured":"Bu, F., Gharajeh, M.S.: Intelligent and vision-based fire detection systems: a survey. Image Vis. Comput. 91, 103803 (2019)","journal-title":"Image Vis. Comput."},{"key":"3384_CR4","doi-asserted-by":"crossref","unstructured":"Mahesh, B.: Machine learning algorithms-a review. Int. J. Sci. Res. (IJSR) [Internet]. 9(1), 381\u2013386 (2020)","DOI":"10.21275\/ART20203995"},{"key":"3384_CR5","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/j.firesaf.2015.11.015","volume":"79","author":"SG Kong","year":"2016","unstructured":"Kong, S.G., Jin, D., Li, S., Kim, H.: Fast fire flame detection in surveillance video using logistic regression and temporal smoothing. Fire Saf. J. 79, 37\u201343 (2016)","journal-title":"Fire Saf. J."},{"key":"3384_CR6","doi-asserted-by":"crossref","unstructured":"Khan, A., Hassan, B., Khan, S., Ahmed, R., Abuassba, A.: DeepFire: A novel dataset and deep transfer learning benchmark for forest fire detection. Mobile Inf. Syst. 2022 (2022)","DOI":"10.1155\/2022\/5358359"},{"issue":"2","key":"3384_CR7","doi-asserted-by":"publisher","first-page":"604","DOI":"10.1109\/TNNLS.2020.2979670","volume":"32","author":"DW Otter","year":"2020","unstructured":"Otter, D.W., Medina, J.R., Kalita, J.K.: A survey of the usages of deep learning for natural language processing. IEEE Trans. Neural Networks Learn. Syst. 32(2), 604\u2013624 (2020)","journal-title":"IEEE Trans. Neural Networks Learn. Syst."},{"issue":"1","key":"3384_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-017-0111-6","volume":"5","author":"S Sohangir","year":"2018","unstructured":"Sohangir, S., Wang, D., Pomeranets, A., Khoshgoftaar, T.M.: Big Data: Deep learning for financial sentiment analysis. J. Big Data. 5(1), 1\u201325 (2018)","journal-title":"J. Big Data"},{"key":"3384_CR9","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1016\/j.inffus.2017.10.006","volume":"42","author":"Q Zhang","year":"2018","unstructured":"Zhang, Q., Yang, L.T., Chen, Z., Li, P.: A survey on deep learning for big data. Inform. Fusion. 42, 146\u2013157 (2018)","journal-title":"Inform. Fusion"},{"key":"3384_CR10","doi-asserted-by":"crossref","unstructured":"Khan, I., Guo, Z., Lim, K., Kim, J., Kwon, Y.-W.: Assessment of indoor risk through deep learning-based object recognition in disaster situations. Multimedia Tools Appl., 1\u201322 (2023)","DOI":"10.1007\/s11042-023-16711-0"},{"key":"3384_CR11","doi-asserted-by":"crossref","unstructured":"Algihab, W., Alawwad, N., Aldawish, A., AlHumoud, S., Arabic speech recognition with deep learning: a review. In: Held as Part of the 21st HCI International Conference, HCII 2019, Orlando, FL, USA, July 26\u201331, 2019, Proceedings, Part I 21, 2019: Springer, pp. 15\u201331 (2019)","DOI":"10.1007\/978-3-030-21902-4_2"},{"key":"3384_CR12","doi-asserted-by":"crossref","unstructured":"Jiao, Z., et al.: A deep learning based forest fire detection approach using UAV and YOLOv3. In: 1st International Conference on Industrial Artificial Intelligence (IAI) 2019: IEEE, pp. 1\u20135 (2019)","DOI":"10.1109\/ICIAI.2019.8850815"},{"key":"3384_CR13","doi-asserted-by":"crossref","unstructured":"Aslan, S., G\u00fcd\u00fckbay, U., T\u00f6reyin, B.U., \u00c7etin, A.E.: Deep convolutional generative adversarial networks for flame detection in video. In: International Conference on Computational Collective Intelligence. Springer, pp. 807\u2013815 (2020)","DOI":"10.1007\/978-3-030-63007-2_63"},{"key":"3384_CR14","doi-asserted-by":"publisher","first-page":"315","DOI":"10.1016\/j.eswa.2018.10.010","volume":"118","author":"A G\u00f3mez-R\u00edos","year":"2019","unstructured":"G\u00f3mez-R\u00edos, A., Tabik, S., Luengo, J., Shihavuddin, A., Krawczyk, B., Herrera, F.: Towards highly accurate coral texture images classification using deep convolutional neural networks and data augmentation. Expert Syst. Appl. 118, 315\u2013328 (2019)","journal-title":"Expert Syst. Appl."},{"key":"3384_CR15","doi-asserted-by":"publisher","first-page":"116114","DOI":"10.1016\/j.eswa.2021.116114","volume":"189","author":"S Majid","year":"2022","unstructured":"Majid, S., Alenezi, F., Masood, S., Ahmad, M., G\u00fcnd\u00fcz, E.S., Polat, K.: Attention based CNN model for fire detection and localization in real-world images. Expert Syst. Appl. 189, 116114 (2022)","journal-title":"Expert Syst. Appl."},{"issue":"10","key":"3384_CR16","doi-asserted-by":"publisher","first-page":"1707","DOI":"10.3390\/jpm12101707","volume":"12","author":"MMA Rahhal","year":"2022","unstructured":"Rahhal, M.M.A., Bazi, Y., Jomaa, R.M., Zuair, M., Melgani, F.: Contrasting EfficientNet, ViT, and gMLP for COVID-19 detection in Ultrasound Imagery. J. Personalized Med. 12(10), 1707 (2022)","journal-title":"J. Personalized Med."},{"key":"3384_CR17","doi-asserted-by":"crossref","unstructured":"Ikromjanov, K., et al.: Region segmentation of whole-slide images for analyzing histological differentiation of prostate adenocarcinoma using ensemble efficientNetB2 U-Net with transfer learning mechanism. Cancers. 15(3), 762 (2023)","DOI":"10.3390\/cancers15030762"},{"key":"3384_CR18","doi-asserted-by":"publisher","first-page":"106316","DOI":"10.1016\/j.compag.2021.106316","volume":"187","author":"L Yang","year":"2021","unstructured":"Yang, L., et al.: A dual attention network based on efficientNet-B2 for short-term fish school feeding behavior analysis in aquaculture. Comput. Electron. Agric. 187, 106316 (2021)","journal-title":"Comput. Electron. Agric."},{"key":"3384_CR19","doi-asserted-by":"publisher","first-page":"101182","DOI":"10.1016\/j.ecoinf.2020.101182","volume":"61","author":"\u00dc Atila","year":"2021","unstructured":"Atila, \u00dc., U\u00e7ar, M., Akyol, K., U\u00e7ar, E.: Plant leaf disease classification using EfficientNet deep learning model. Ecol. Inf. 61, 101182 (2021)","journal-title":"Ecol. Inf."},{"key":"3384_CR20","first-page":"1","volume":"2022","author":"A Khan","year":"2022","unstructured":"Khan, A., Hassan, B., Khan, S., Ahmed, R., Abuassba, A.: DeepFire: A novel dataset and deep transfer learning benchmark for forest fire detection. Mob. Inform. Syst. 2022, 1\u201314 (2022)","journal-title":"Mob. Inform. Syst."},{"key":"3384_CR21","doi-asserted-by":"crossref","unstructured":"Khan, S., Khan, A.: Ffirenet: Deep learning based forest fire classification and detection in smart cities. Symmetry. 14(10), 2155 (2022)","DOI":"10.3390\/sym14102155"},{"key":"3384_CR22","doi-asserted-by":"publisher","first-page":"117407","DOI":"10.1016\/j.eswa.2022.117407","volume":"203","author":"S Dogan","year":"2022","unstructured":"Dogan, S., et al.: Automated accurate fire detection system using ensemble pretrained residual network. Expert Syst. Appl. 203, 117407 (2022)","journal-title":"Expert Syst. Appl."},{"key":"3384_CR23","doi-asserted-by":"crossref","unstructured":"Najab, A., Khan, I., Arshad, M., Ahmad, F.: Classification of settlements in satellite images using holistic feature extraction. In: 12th International Conference on Computer Modelling and Simulation, 2010. IEEE, pp. 267\u2013271 (2010)","DOI":"10.1109\/UKSIM.2010.57"},{"issue":"7","key":"3384_CR24","doi-asserted-by":"publisher","first-page":"9819","DOI":"10.1007\/s11042-022-13043-3","volume":"82","author":"R Vikram","year":"2023","unstructured":"Vikram, R., Sinha, D.: A multimodal framework for forest fire detection and monitoring. Multimedia Tools Appl. 82(7), 9819\u20139842 (2023)","journal-title":"Multimedia Tools Appl."},{"key":"3384_CR25","doi-asserted-by":"publisher","first-page":"418","DOI":"10.1016\/j.procs.2020.04.044","volume":"171","author":"A Jadon","year":"2020","unstructured":"Jadon, A., Varshney, A., Ansari, M.S.: Low-complexity high-performance deep learning model for real-time low-cost embedded fire detection systems. Procedia Comput. Sci. 171, 418\u2013426 (2020)","journal-title":"Procedia Comput. Sci."},{"key":"3384_CR26","doi-asserted-by":"crossref","unstructured":"Xu, R., Lin, H., Lu, K., Cao, L., Liu, Y.: A forest fire detection system based on ensemble learning. Forests. 12(2), 217 (2021)","DOI":"10.3390\/f12020217"},{"key":"3384_CR27","doi-asserted-by":"publisher","first-page":"9083","DOI":"10.1007\/s11042-019-07785-w","volume":"79","author":"F Saeed","year":"2020","unstructured":"Saeed, F., Paul, A., Karthigaikumar, P., Nayyar, A.: Convolutional neural network based early fire detection. Multimedia Tools Appl. 79, 9083\u20139099 (2020)","journal-title":"Multimedia Tools Appl."},{"issue":"6","key":"3384_CR28","doi-asserted-by":"publisher","first-page":"2419","DOI":"10.1007\/s10694-019-00872-2","volume":"55","author":"AS Pundir","year":"2019","unstructured":"Pundir, A.S., Raman, B.: Dual deep learning model for image based smoke detection. Fire Technol. 55(6), 2419\u20132442 (2019)","journal-title":"Fire Technol."},{"key":"3384_CR29","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1016\/j.envsoft.2014.03.003","volume":"57","author":"M Rodrigues","year":"2014","unstructured":"Rodrigues, M., De la Riva, J.: An insight into machine-learning algorithms to model human-caused wildfire occurrence. Environ. Model. Softw. 57, 192\u2013201 (2014)","journal-title":"Environ. Model. Softw."},{"issue":"1","key":"3384_CR30","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1007\/s11760-023-02728-3","volume":"18","author":"Z Hong","year":"2024","unstructured":"Hong, Z., Hamdan, E., Zhao, Y., Ye, T., Pan, H., Cetin, A.E.: Wildfire detection via transfer learning: A survey. Signal. Image Video Process. 18(1), 207\u2013214 (2024)","journal-title":"Signal. Image Video Process."},{"key":"3384_CR31","doi-asserted-by":"publisher","first-page":"100625","DOI":"10.1016\/j.csite.2020.100625","volume":"19","author":"P Li","year":"2020","unstructured":"Li, P., Zhao, W.: Image fire detection algorithms based on convolutional neural networks. Case Stud. Therm. Eng. 19, 100625 (2020)","journal-title":"Case Stud. Therm. Eng."},{"issue":"4","key":"3384_CR32","doi-asserted-by":"publisher","first-page":"923609","DOI":"10.1155\/2014\/923609","volume":"10","author":"Y-H Kim","year":"2014","unstructured":"Kim, Y.-H., Kim, A., Jeong, H.-Y.: RGB color model based the fire detection algorithm in video sequences on wireless sensor network. Int. J. Distrib. Sens. Netw. 10(4), 923609 (2014)","journal-title":"Int. J. Distrib. Sens. Netw."},{"issue":"2","key":"3384_CR33","doi-asserted-by":"publisher","first-page":"176","DOI":"10.1016\/j.jvcir.2006.12.003","volume":"18","author":"T Celik","year":"2007","unstructured":"Celik, T., Demirel, H., Ozkaramanli, H., Uyguroglu, M.: Fire detection using statistical color model in video sequences. J. Vis. Commun. Image Represent. 18(2), 176\u2013185 (2007)","journal-title":"J. Vis. Commun. Image Represent."},{"issue":"9","key":"3384_CR34","doi-asserted-by":"publisher","first-page":"149","DOI":"10.5772\/58821","volume":"11","author":"P Gomes","year":"2014","unstructured":"Gomes, P., Santana, P., Barata, J.: A vision-based approach to fire detection. Int. J. Adv. Rob. Syst. 11(9), 149 (2014)","journal-title":"Int. J. Adv. Rob. Syst."},{"key":"3384_CR35","doi-asserted-by":"crossref","unstructured":"Messias, P., Sousa, M.J., Moutinho, A.: Color-based superpixel semantic segmentation for fire data annotation. In: IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2021. IEEE, pp. 1\u20137 (2021)","DOI":"10.1109\/FUZZ45933.2021.9494421"},{"key":"3384_CR36","doi-asserted-by":"crossref","unstructured":"Tang, Z., Gao, Y., Karlinsky, L., Sattigeri, P., Feris, R., Metaxas, D.: OnlineAugment: Online data augmentation with less domain knowledge. In: Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, Proceedings, Part VII 16, 2020. Springer, pp. 313\u2013329 (2020)","DOI":"10.1007\/978-3-030-58571-6_19"},{"issue":"1","key":"3384_CR37","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-019-0197-0","volume":"6","author":"C Shorten","year":"2019","unstructured":"Shorten, C., Khoshgoftaar, T.M.: A survey on image data augmentation for deep learning. J. big data. 6(1), 1\u201348 (2019)","journal-title":"J. big data"},{"key":"3384_CR38","doi-asserted-by":"crossref","unstructured":"LeCun, Y., Kavukcuoglu, K., Farabet, C.: Convolutional networks and applications in vision. In: Proceedings of 2010 IEEE International Symposium on Circuits and Systems. IEEE, pp. 253\u2013256 (2010)","DOI":"10.1109\/ISCAS.2010.5537907"},{"key":"3384_CR39","unstructured":"You, K., Liu, Y., Wang, J., Long, M.: Logme: Practical assessment of pre-trained models for transfer learning. In: International Conference on Machine Learning. PMLR, pp. 12133\u201312143 (2021)"},{"key":"3384_CR40","doi-asserted-by":"publisher","first-page":"14078","DOI":"10.1109\/ACCESS.2021.3051085","volume":"9","author":"H Alhichri","year":"2021","unstructured":"Alhichri, H., Alswayed, A.S., Bazi, Y., Ammour, N., Alajlan, N.A.: Classification of remote sensing images using EfficientNet-B3 CNN model with attention. IEEE Access. 9, 14078\u201314094 (2021)","journal-title":"IEEE Access."},{"key":"3384_CR41","doi-asserted-by":"publisher","first-page":"105403","DOI":"10.1016\/j.engappai.2022.105403","volume":"116","author":"ZA Khan","year":"2022","unstructured":"Khan, Z.A., Hussain, T., Ullah, F.U.M., Gupta, S.K., Lee, M.Y., Baik, S.W.: Randomly initialized CNN with densely connected stacked autoencoder for efficient fire detection. Eng. Appl. Artif. Intell. 116, 105403 (2022)","journal-title":"Eng. Appl. Artif. Intell."},{"key":"3384_CR42","unstructured":"Tan, M., Le, Q.: Efficientnet: Rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning. PMLR, pp. 6105\u20136114 (2019)"},{"key":"3384_CR43","doi-asserted-by":"publisher","first-page":"104737","DOI":"10.1016\/j.engappai.2022.104737","volume":"110","author":"L Huang","year":"2022","unstructured":"Huang, L., Liu, G., Wang, Y., Yuan, H., Chen, T.: Fire detection in video surveillances using convolutional neural networks and wavelet transform. Eng. Appl. Artif. Intell. 110, 104737 (2022)","journal-title":"Eng. Appl. Artif. Intell."},{"key":"3384_CR44","unstructured":"Selvaraju, R.R., Das, A., Vedantam, R., Cogswell, M., Parikh, D., Batra, D.: Grad-CAM: Why did you say that? arXiv preprint arXiv:1611.07450 (2016)"},{"key":"3384_CR45","doi-asserted-by":"publisher","first-page":"236","DOI":"10.1016\/j.sigpro.2018.12.006","volume":"157","author":"M Mafi","year":"2019","unstructured":"Mafi, M., Martin, H., Cabrerizo, M., Andrian, J., Barreto, A., Adjouadi, M.: A comprehensive survey on impulse and gaussian denoising filters for digital images. Sig. Process. 157, 236\u2013260 (2019)","journal-title":"Sig. Process."},{"issue":"6221","key":"3384_CR46","doi-asserted-by":"publisher","first-page":"509","DOI":"10.1126\/science.aaa1465","volume":"347","author":"A Acquisti","year":"2015","unstructured":"Acquisti, A., Brandimarte, L., Loewenstein, G.: Privacy and human behavior in the age of information. Science. 347(6221), 509\u2013514 (2015)","journal-title":"Science"},{"key":"3384_CR47","doi-asserted-by":"publisher","first-page":"689","DOI":"10.1007\/s11023-018-9482-5","volume":"28","author":"L Floridi","year":"2018","unstructured":"Floridi, L., et al.: AI4People\u2014an ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Mind. Mach. 28, 689\u2013707 (2018)","journal-title":"Mind. Mach."},{"issue":"11","key":"3384_CR48","doi-asserted-by":"publisher","first-page":"1050","DOI":"10.1038\/nbt.4007","volume":"35","author":"PR Wolpe","year":"2017","unstructured":"Wolpe, P.R., Rommelfanger, K.S., Drafting: o. t. B. W. groups, ethical principles for the use of human cellular biotechnologies. Nat. Biotechnol. 35(11), 1050\u20131058 (2017)","journal-title":"Nat. Biotechnol."},{"key":"3384_CR49","unstructured":"Jadon, A., Omama, M., Varshney, A., Ansari, M.S., Sharma, R.: FireNet: a specialized lightweight fire & smoke detection model for real-time IoT applications, arXiv preprint arXiv:11922, 2019 (1905)"},{"issue":"4","key":"3384_CR50","doi-asserted-by":"publisher","first-page":"121","DOI":"10.4316\/AECE.2018.04015","volume":"18","author":"A Namozov","year":"2018","unstructured":"Namozov, A., Im Cho, Y.: An efficient deep learning algorithm for fire and smoke detection with limited data. Adv. Electr. Comput. Eng. 18(4), 121\u2013128 (2018)","journal-title":"Adv. Electr. Comput. Eng."},{"key":"3384_CR51","doi-asserted-by":"crossref","unstructured":"Dua, M., Kumar, M., Charan, G.S., Ravi, P.S.: An improved approach for fire detection using deep learning models. In: International Conference on Industry 4.0 Technology (I4Tech), 2020. IEEE, pp. 171\u2013175 (2020)","DOI":"10.1109\/I4Tech48345.2020.9102697"}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-024-03384-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-024-03384-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-024-03384-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,10]],"date-time":"2024-08-10T10:28:04Z","timestamp":1723285684000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-024-03384-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,28]]},"references-count":51,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2024,9]]}},"alternative-id":["3384"],"URL":"https:\/\/doi.org\/10.1007\/s11760-024-03384-x","relation":{},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"value":"1863-1703","type":"print"},{"value":"1863-1711","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,28]]},"assertion":[{"value":"8 March 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 June 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 June 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 June 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}