{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T19:29:12Z","timestamp":1762543752307,"version":"3.37.3"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,11,17]],"date-time":"2022-11-17T00:00:00Z","timestamp":1668643200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,11,17]],"date-time":"2022-11-17T00:00:00Z","timestamp":1668643200000},"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":["SN COMPUT. SCI."],"DOI":"10.1007\/s42979-022-01467-5","type":"journal-article","created":{"date-parts":[[2022,11,17]],"date-time":"2022-11-17T18:13:01Z","timestamp":1668708781000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Semantic Heads Segmentation and Counting in Crowded Retail Environment with Convolutional Neural Networks Using Top View Depth Images"],"prefix":"10.1007","volume":"4","author":[{"given":"Almustafa","family":"Abed","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4219-227X","authenticated-orcid":false,"given":"Belhassen","family":"Akrout","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ikram","family":"Amous","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,11,17]]},"reference":[{"key":"1467_CR1","doi-asserted-by":"publisher","first-page":"461","DOI":"10.1016\/j.procs.2015.08.064","volume":"58","author":"C Raghavachari","year":"2015","unstructured":"Raghavachari C, Aparna V, Chithira S, Balasubramanian V. A comparative study of vision based human detection techniques in people counting applications. Procedia Comput Sci. 2015;58:461\u20139. https:\/\/doi.org\/10.1016\/j.procs.2015.08.064.","journal-title":"Procedia Comput Sci"},{"issue":"2","key":"1467_CR2","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1007\/s10846-017-0674-7","volume":"91","author":"M Paolanti","year":"2018","unstructured":"Paolanti M, Liciotti D, Pietrini R, Mancini A, Frontoni E. Modelling and forecasting customer navigation in intelligent retail environments. J Intell Robot Syst. 2018;91(2):165\u201380. https:\/\/doi.org\/10.1007\/s10846-017-0674-7.","journal-title":"J Intell Robot Syst"},{"key":"1467_CR3","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/j.patrec.2014.09.013","volume":"53","author":"J Liu","year":"2015","unstructured":"Liu J, Liu Y, Zhang G, Zhu P, Chen YQ. Detecting and tracking people in real time with RGB-D camera. Pattern Recogn Lett. 2015;53:16\u201323. https:\/\/doi.org\/10.1016\/j.patrec.2014.09.013.","journal-title":"Pattern Recogn Lett"},{"key":"1467_CR4","doi-asserted-by":"publisher","unstructured":"Liang B, Zheng L. A survey on human action recognition using depth sensors. In: 2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA), Adelaide, Australia, 2015, pp. 1\u20138. https:\/\/doi.org\/10.1109\/DICTA.2015.7371223.","DOI":"10.1109\/DICTA.2015.7371223"},{"key":"1467_CR5","doi-asserted-by":"publisher","unstructured":"Paolanti M, Sturari M, Mancini A, Zingaretti P, Frontoni E. Mobile robot for retail surveying and inventory using visual and textual analysis of monocular pictures based on deep learning. In: 2017 European conference on mobile robots (ECMR), Paris, 2017, pp. 1\u20136. https:\/\/doi.org\/10.1109\/ECMR.2017.8098666.","DOI":"10.1109\/ECMR.2017.8098666"},{"key":"1467_CR6","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-021-03311-9","author":"B Akrout","year":"2021","unstructured":"Akrout B, Mahdi W. A novel approach for driver fatigue detection based on visual characteristics analysis. J Ambient Intell Human Comput. 2021. https:\/\/doi.org\/10.1007\/s12652-021-03311-9.","journal-title":"J Ambient Intell Human Comput"},{"key":"1467_CR7","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1007\/978-3-319-70742-6_20","volume-title":"New trends in image analysis and processing\u2014ICIAP 2017","author":"D Liciotti","year":"2017","unstructured":"Liciotti D, Paolanti M, Frontoni E, Zingaretti P. People Detection and Tracking from an RGB-D Camera in Top-View Configuration: Review of Challenges and Applications. In: Battiato S, Farinella GM, Leo M, Gallo G, editors. New trends in image analysis and processing\u2014ICIAP 2017, vol. 10590. Cham: Springer International Publishing; 2017. p. 207\u201318. https:\/\/doi.org\/10.1007\/978-3-319-70742-6_20."},{"key":"1467_CR8","doi-asserted-by":"publisher","unstructured":"Liciotti D. TVHeads (Top-View Heads) Dataset. vol. 1, 2018, doi: https:\/\/doi.org\/10.17632\/nz4hy7yrps.1.","DOI":"10.17632\/nz4hy7yrps.1"},{"issue":"10","key":"1467_CR9","doi-asserted-by":"publisher","first-page":"3599","DOI":"10.1109\/TITS.2019.2911128","volume":"20","author":"S Sun","year":"2019","unstructured":"Sun S, Akhtar N, Song H, Zhang C, Li J, Mian A. Benchmark data and method for real-time people counting in cluttered scenes using depth sensors. IEEE Trans Intell Transport Syst. 2019;20(10):3599\u2013612. https:\/\/doi.org\/10.1109\/TITS.2019.2911128.","journal-title":"IEEE Trans Intell Transport Syst"},{"issue":"11","key":"1467_CR10","doi-asserted-by":"publisher","first-page":"11","DOI":"10.3390\/electronics10111293","volume":"10","author":"K Khan","year":"2021","unstructured":"Khan K, et al. Crowd counting using end-to-end semantic image segmentation. Electronics. 2021;10(11):11. https:\/\/doi.org\/10.3390\/electronics10111293.","journal-title":"Electronics"},{"key":"1467_CR11","doi-asserted-by":"publisher","unstructured":"Yosinski J, Clune J, Bengio Y, Lipson H. How transferable are features in deep neural networks?. arXiv, arXiv:1411.1792, 2014. https:\/\/doi.org\/10.48550\/arXiv.1411.1792.","DOI":"10.48550\/arXiv.1411.1792"},{"key":"1467_CR12","unstructured":"Deng J, Dong W, Socher R, Li L-J, Li K, Fei-Fei L. ImageNet: a large-scale hierarchical image database, p. 8."},{"key":"1467_CR13","doi-asserted-by":"publisher","first-page":"327","DOI":"10.1007\/978-3-031-08277-1_27","volume-title":"Intelligent systems and pattern recognition","author":"A Abed","year":"2022","unstructured":"Abed A, Akrout B, Amous I. A novel deep convolutional neural network architecture for customer counting in the retail environment. In: Intelligent systems and pattern recognition. Cham: Springer; 2022. p. 327\u201340."},{"key":"1467_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.micron.2021.103055","volume":"145","author":"B Akrout","year":"2021","unstructured":"Akrout B. A new structure of decision tree based on oriented edges gradient map for circles detection and the analysis of nano-particles. Micron. 2021;145: 103055. https:\/\/doi.org\/10.1016\/j.micron.2021.103055.","journal-title":"Micron"},{"key":"1467_CR15","doi-asserted-by":"publisher","unstructured":"Bondi E, Seidenari L, Bagdanov AD, Del Bimbo A. Real-time people counting from depth imagery of crowded environments. In: 2014 11th IEEE international conference on advanced video and signal based surveillance (AVSS), Seoul, South Korea, 2014, pp. 337\u2013342. https:\/\/doi.org\/10.1109\/AVSS.2014.6918691.","DOI":"10.1109\/AVSS.2014.6918691"},{"key":"1467_CR16","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1016\/j.patrec.2016.05.033","volume":"81","author":"L Del Pizzo","year":"2016","unstructured":"Del Pizzo L, Foggia P, Greco A, Percannella G, Vento M. Counting people by RGB or depth overhead cameras. Pattern Recogn Lett. 2016;81:41\u201350. https:\/\/doi.org\/10.1016\/j.patrec.2016.05.033.","journal-title":"Pattern Recogn Lett"},{"key":"1467_CR17","doi-asserted-by":"publisher","unstructured":"Liciotti D, Paolanti M, Pietrini R, Frontoni E, Zingaretti P. Convolutional networks for semantic heads segmentation using top-view depth data in crowded environment. In: 2018 24th international conference on pattern recognition (ICPR), Beijing, 2018, pp. 1384\u20131389. https:\/\/doi.org\/10.1109\/ICPR.2018.8545397.","DOI":"10.1109\/ICPR.2018.8545397"},{"issue":"3","key":"1467_CR18","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1109\/TCE.2012.6311323","volume":"58","author":"B Mrazovac","year":"2012","unstructured":"Mrazovac B, Bjelica MZ, Kukolj D, Todorovi BM. A human detection method for residential smart energy systems based on zigbee RSSI changes. IEEE Trans Consum Electron. 2012;58(3):6.","journal-title":"IEEE Trans Consum Electron"},{"issue":"9","key":"1467_CR19","doi-asserted-by":"publisher","first-page":"3991","DOI":"10.1109\/TIE.2012.2206330","volume":"60","author":"J Garcia","year":"2013","unstructured":"Garcia J, Gardel A, Bravo I, Lazaro JL, Martinez M, Rodriguez D. Directional people counter based on head tracking. IEEE Trans Ind Electron. 2013;60(9):3991\u20134000. https:\/\/doi.org\/10.1109\/TIE.2012.2206330.","journal-title":"IEEE Trans Ind Electron"},{"key":"1467_CR20","doi-asserted-by":"publisher","unstructured":"Iguernaissi R, Merad D, Drap P. People counting based on kinect depth data. In: Proceedings of the 7th international conference on pattern recognition applications and methods, Funchal, Madeira, Portugal, 2018, pp. 364\u2013370. https:\/\/doi.org\/10.5220\/0006585703640370.","DOI":"10.5220\/0006585703640370"},{"key":"1467_CR21","doi-asserted-by":"publisher","unstructured":"Wang C, Zhang H, Yang L, Liu S, Cao X. Deep people counting in extremely dense crowds. In: Proceedings of the 23rd ACM international conference on multimedia, Brisbane Australia, 2015, pp. 1299\u20131302. https:\/\/doi.org\/10.1145\/2733373.2806337.","DOI":"10.1145\/2733373.2806337"},{"key":"1467_CR22","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1016\/j.engappai.2015.04.006","volume":"43","author":"M Fu","year":"2015","unstructured":"Fu M, Xu P, Li X, Liu Q, Ye M, Zhu C. Fast crowd density estimation with convolutional neural networks. Eng Appl Artif Intell. 2015;43:81\u20138. https:\/\/doi.org\/10.1016\/j.engappai.2015.04.006.","journal-title":"Eng Appl Artif Intell"},{"key":"1467_CR23","doi-asserted-by":"publisher","unstructured":"Zhang C, Li H, Wang X, Yang X. Cross-scene crowd counting via deep convolutional neural networks. In: 2015 IEEE conference on computer vision and pattern recognition (CVPR), Boston, MA, USA, 2015, pp. 833\u201341. https:\/\/doi.org\/10.1109\/CVPR.2015.7298684.","DOI":"10.1109\/CVPR.2015.7298684"},{"key":"1467_CR24","doi-asserted-by":"publisher","unstructured":"Noh H, Hong S, Han B. Learning Deconvolution Network for Semantic Segmentation. In: 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile, 2015, pp. 1520\u20138. https:\/\/doi.org\/10.1109\/ICCV.2015.178.","DOI":"10.1109\/ICCV.2015.178"},{"key":"1467_CR25","unstructured":"Simonyan K, Zisserman A. Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv:1409.1556 [cs]. 2015. http:\/\/arxiv.org\/abs\/1409.1556. Accessed 2 Apr 2021"},{"issue":"2","key":"1467_CR26","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","volume":"88","author":"M Everingham","year":"2010","unstructured":"Everingham M, Van Gool L, Williams CKI, Winn J, Zisserman A. The Pascal visual object classes (VOC) challenge. Int J Comput Vis. 2010;88(2):303\u201338. https:\/\/doi.org\/10.1007\/s11263-009-0275-4.","journal-title":"Int J Comput Vis"},{"key":"1467_CR27","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical image computing and computer-assisted intervention\u2014MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation. In: Navab N, Hornegger J, Wells WM, Frangi AF, editors. Medical image computing and computer-assisted intervention\u2014MICCAI 2015, vol. 9351. Cham: Springer International Publishing; 2015. p. 234\u201341. https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28."},{"key":"1467_CR28","unstructured":"Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation, p. 10."},{"key":"1467_CR29","unstructured":"Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. In: Proceedings of the 25th international conference on neural information processing systems\u2014Volume 1, Red Hook, NY, USA, 2012, pp. 1097\u2013105."},{"key":"1467_CR30","doi-asserted-by":"publisher","unstructured":"Szegedy C et al. Going deeper with convolutions. In: 2015 IEEE conference on computer vision and pattern recognition (CVPR), 2015, pp. 1\u20139. https:\/\/doi.org\/10.1109\/CVPR.2015.7298594.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"1467_CR31","unstructured":"Badrinarayanan V, Kendall A, Cipolla R. SegNet: a deep convolutional encoder-decoder architecture for image segmentation. arXiv:1511.00561 [cs], 2016. http:\/\/arxiv.org\/abs\/1511.00561. Accessed 01 Apr 2021."},{"key":"1467_CR32","doi-asserted-by":"publisher","unstructured":"He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), Las Vegas, NV, USA, 2016, pp. 770\u20138. https:\/\/doi.org\/10.1109\/CVPR.2016.90.","DOI":"10.1109\/CVPR.2016.90"},{"key":"1467_CR33","unstructured":"Lin T-Y et al. Microsoft COCO: common objects in context. arXiv, 2015. http:\/\/arxiv.org\/abs\/1405.0312. Accessed 19 May 2022."},{"key":"1467_CR34","unstructured":"Chen L-C, Zhu Y, Papandreou G, Schroff F, Adam H. Encoder-decoder with atrous separable convolution for semantic image segmentation. arXiv:1802.02611 [cs], 2018. http:\/\/arxiv.org\/abs\/1802.02611. Accessed 26 Jan 2022."},{"key":"1467_CR35","unstructured":"Chen L-C, Papandreou G, Schroff F, Adam H. Rethinking atrous convolution for semantic image segmentation. arXiv:1706.05587 [cs], 2017. http:\/\/arxiv.org\/abs\/1706.05587. Accessed 22 Sep 2021."},{"issue":"9","key":"1467_CR36","doi-asserted-by":"publisher","first-page":"2627","DOI":"10.1109\/TCSVT.2018.2803115","volume":"29","author":"MB Shami","year":"2019","unstructured":"Shami MB, Maqbool S, Sajid H, Ayaz Y, Cheung S-CS. People counting in dense crowd images using sparse head detections. IEEE Trans Circuits Syst Video Technol. 2019;29(9):2627\u201336. https:\/\/doi.org\/10.1109\/TCSVT.2018.2803115.","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"1467_CR37","doi-asserted-by":"publisher","unstructured":"Nogueira V, Oliveira H, Augusto Silva J, Vieira T, Oliveira K. RetailNet: a deep learning approach for people counting and hot spots detection in retail stores. In: 2019 32nd SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), Rio de Janeiro, Brazil, 2019, pp. 155\u201362. https:\/\/doi.org\/10.1109\/SIBGRAPI.2019.00029.","DOI":"10.1109\/SIBGRAPI.2019.00029"},{"key":"1467_CR38","doi-asserted-by":"crossref","unstructured":"Takikawa T, Acuna D, Jampani V, Fidler S. Gated-SCNN: Gated Shape CNNs for Semantic Segmentation. arXiv:1907.05740 [cs], 2019. http:\/\/arxiv.org\/abs\/1907.05740. Accessed 06 Dec 2021.","DOI":"10.1109\/ICCV.2019.00533"},{"key":"1467_CR39","doi-asserted-by":"publisher","unstructured":"He J, Wu X, Yang J, Hu W. CPSPNet: Crowd counting via semantic segmentation framework. In: 2020 IEEE 32nd international conference on tools with artificial intelligence (ICTAI), 2020, pp. 1104\u201310. https:\/\/doi.org\/10.1109\/ICTAI50040.2020.00168.","DOI":"10.1109\/ICTAI50040.2020.00168"},{"key":"1467_CR40","doi-asserted-by":"publisher","unstructured":"Yao Y, Zhang X, Liang Y, Zhang X, Shen F, Zhao J. A real-time pedestrian counting system based on RGB-D. In: 2020 12th international conference on advanced computational intelligence (ICACI), 2020, pp. 110\u20137. https:\/\/doi.org\/10.1109\/ICACI49185.2020.9177816.","DOI":"10.1109\/ICACI49185.2020.9177816"},{"key":"1467_CR41","doi-asserted-by":"publisher","unstructured":"Min F, Wang Y, Zhu S. People counting based on multi-scale region adaptive segmentation and depth neural network. In: Proceedings of the 2020 3rd international conference on artificial intelligence and pattern recognition, Xiamen China, 2020, pp. 79\u201383. doi: https:\/\/doi.org\/10.1145\/3430199.3430201.","DOI":"10.1145\/3430199.3430201"},{"issue":"12","key":"1467_CR42","doi-asserted-by":"publisher","first-page":"12","DOI":"10.3390\/app11125503","volume":"11","author":"M Gochoo","year":"2021","unstructured":"Gochoo M, Rizwan SA, Ghadi YY, Jalal A, Kim K. A systematic deep learning based overhead tracking and counting system using RGB-D remote cameras. Appl Sci. 2021;11(12):12. https:\/\/doi.org\/10.3390\/app11125503.","journal-title":"Appl Sci"},{"key":"1467_CR43","doi-asserted-by":"publisher","unstructured":"Im D, Han D, Choi S, Kang S, Yoo H-J. DT-CNN: dilated and transposed convolution neural network accelerator for real-time image segmentation on mobile devices. In: 2019 IEEE international symposium on circuits and systems (ISCAS), Sapporo, Japan, 2019, pp. 1\u20135. https:\/\/doi.org\/10.1109\/ISCAS.2019.8702243.","DOI":"10.1109\/ISCAS.2019.8702243"},{"key":"1467_CR44","unstructured":"Wu H, Zhang J, Huang K, Liang K, Yu Y. FastFCN: rethinking dilated convolution in the backbone for semantic segmentation. arXiv:1903.11816 [cs], 2019. http:\/\/arxiv.org\/abs\/1903.11816. Accessed 31 Jan 2022."},{"key":"1467_CR45","doi-asserted-by":"crossref","unstructured":"Zhu X, Cheng D, Zhang Z, Lin S, Dai J. An Empirical study of spatial attention mechanisms in deep networks. arXiv:1904.05873 [cs], 2019. http:\/\/arxiv.org\/abs\/1904.05873. Accessed 04 Feb 2022.","DOI":"10.1109\/ICCV.2019.00679"},{"key":"1467_CR46","unstructured":"Dumoulin V, Visin F. A guide to convolution arithmetic for deep learning. arXiv:1603.07285 [cs, stat], 2018. http:\/\/arxiv.org\/abs\/1603.07285. Accessed 31 Jan 2022."},{"key":"1467_CR47","unstructured":"Chen L-C, Papandreou G, Kokkinos I, Murphy K, Yuille AL. DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. arXiv:1606.00915 [cs], 2017. http:\/\/arxiv.org\/abs\/1606.00915. Accessed 26 Jan 2022."},{"key":"1467_CR48","unstructured":"Kingma DP, Ba J. Adam: a method for stochastic optimization. arXiv:1412.6980 [cs]. 2017. http:\/\/arxiv.org\/abs\/1412.6980. Accessed 15 Feb 2022."},{"issue":"2","key":"1467_CR49","doi-asserted-by":"publisher","first-page":"125","DOI":"10.3390\/info11020125","volume":"11","author":"A Buslaev","year":"2020","unstructured":"Buslaev A, Iglovikov VI, Khvedchenya E, Parinov A, Druzhinin M, Kalinin AA. Albumentations: fast and flexible image augmentations. Information. 2020;11(2):125. https:\/\/doi.org\/10.3390\/info11020125.","journal-title":"Information"},{"key":"1467_CR50","doi-asserted-by":"publisher","unstructured":"Jaccard P. \u00c9tude comparative de la distribution florale dans une portion des Alpes et du Jura. 1901, https:\/\/doi.org\/10.5169\/SEALS-266450.","DOI":"10.5169\/SEALS-266450"},{"key":"1467_CR51","doi-asserted-by":"publisher","DOI":"10.7717\/peerj.453","volume":"2","author":"S van der Walt","year":"2014","unstructured":"van der Walt S, et al. scikit-image: image processing in Python. PeerJ. 2014;2: e453. https:\/\/doi.org\/10.7717\/peerj.453.","journal-title":"PeerJ"}],"container-title":["SN Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-022-01467-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42979-022-01467-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-022-01467-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,7]],"date-time":"2023-01-07T22:25:42Z","timestamp":1673130342000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42979-022-01467-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,17]]},"references-count":51,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["1467"],"URL":"https:\/\/doi.org\/10.1007\/s42979-022-01467-5","relation":{},"ISSN":["2661-8907"],"issn-type":[{"type":"electronic","value":"2661-8907"}],"subject":[],"published":{"date-parts":[[2022,11,17]]},"assertion":[{"value":"31 May 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 October 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 November 2022","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 declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"61"}}