{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T20:58:32Z","timestamp":1774990712883,"version":"3.50.1"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2023,5,3]],"date-time":"2023-05-03T00:00:00Z","timestamp":1683072000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,5,3]],"date-time":"2023-05-03T00:00:00Z","timestamp":1683072000000},"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":["Vis Comput"],"published-print":{"date-parts":[[2024,3]]},"DOI":"10.1007\/s00371-023-02865-3","type":"journal-article","created":{"date-parts":[[2023,5,3]],"date-time":"2023-05-03T16:02:14Z","timestamp":1683129734000},"page":"1515-1535","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["An accurate violence detection framework using unsupervised spatial\u2013temporal action translation network"],"prefix":"10.1007","volume":"40","author":[{"given":"Tahereh Zarrat","family":"Ehsan","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9846-314X","authenticated-orcid":false,"given":"Manoochehr","family":"Nahvi","sequence":"additional","affiliation":[]},{"given":"Seyed Mehdi","family":"Mohtavipour","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,5,3]]},"reference":[{"issue":"9","key":"2865_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.15585\/mmwr.ss.6809a1","volume":"68","author":"A Ertl","year":"2019","unstructured":"Ertl, A., Sheats, K.J., Petrosky, E., Betz, C.J., Yuan, K., Fowler, K.A.: Surveillance for violent deaths\u2014national violent death reporting system, 32 states, 2016. MMWR Surveill. Summ. 68(9), 1 (2019)","journal-title":"MMWR Surveill. Summ."},{"issue":"8","key":"2865_CR2","first-page":"1","volume":"38","author":"K Bayoudh","year":"2021","unstructured":"Bayoudh, K., Knani, R., Hamdaoui, F., Mtibaa, A.: A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets. Vis. Comput. 38(8), 1\u201332 (2021)","journal-title":"Vis. Comput."},{"key":"2865_CR3","doi-asserted-by":"publisher","first-page":"200901","DOI":"10.1016\/j.fsidi.2019.200901","volume":"32","author":"I Jegham","year":"2020","unstructured":"Jegham, I., Khalifa, A.B., Alouani, I., Mahjoub, M.A.: Vision-based human action recognition: an overview and real world challenges. Forensic Sci. Int. Digit. Investig. 32, 200901 (2020)","journal-title":"Forensic Sci. Int. Digit. Investig."},{"issue":"4","key":"2865_CR4","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1007\/s00371-018-1478-x","volume":"35","author":"W Zhou","year":"2019","unstructured":"Zhou, W., Ma, C., Yao, T., Chang, P., Zhang, Q., Kuijper, A.: Histograms of Gaussian normal distribution for 3D feature matching in cluttered scenes. Vis. Comput. 35(4), 489\u2013505 (2019)","journal-title":"Vis. Comput."},{"issue":"3","key":"2865_CR5","doi-asserted-by":"publisher","first-page":"2259","DOI":"10.1007\/s10462-020-09904-8","volume":"54","author":"P Pareek","year":"2021","unstructured":"Pareek, P., Thakkar, A.: A survey on video-based human action recognition: recent updates, datasets, challenges, and applications. Artif. Intell. Rev. 54(3), 2259\u20132322 (2021)","journal-title":"Artif. Intell. Rev."},{"key":"2865_CR6","unstructured":"Soomro, K., Zamir, A.R., Shah, M.: UCF101: a dataset of 101 human actions classes from videos in the wild. arXiv preprint arXiv:1212.0402 (2012)"},{"key":"2865_CR7","doi-asserted-by":"crossref","unstructured":"Kuehne, H., Jhuang, H., Garrote, E., Poggio, T., Serre, T.: HMDB: a large video database for human motion recognition. In: International Conference on Computer Vision,\u00a0pp. 2556\u20132563 (2011)","DOI":"10.1109\/ICCV.2011.6126543"},{"issue":"7","key":"2865_CR8","doi-asserted-by":"publisher","first-page":"8497","DOI":"10.1007\/s11042-018-6923-3","volume":"78","author":"J Yu","year":"2019","unstructured":"Yu, J., Song, W., Zhou, G., Hou, J.J.: Violent scene detection algorithm based on kernel extreme learning machine and three-dimensional histograms of gradient orientation. Multimed. Tools Appl. 78(7), 8497\u20138512 (2019)","journal-title":"Multimed. Tools Appl."},{"issue":"10","key":"2865_CR9","doi-asserted-by":"publisher","first-page":"e0203668","DOI":"10.1371\/journal.pone.0203668","volume":"13","author":"P Zhou","year":"2018","unstructured":"Zhou, P., Ding, Q., Luo, H., Hou, X.: Violence detection in surveillance video using low-level features. PLoS ONE 13(10), e0203668 (2018)","journal-title":"PLoS ONE"},{"key":"2865_CR10","doi-asserted-by":"publisher","first-page":"2057","DOI":"10.1007\/s00371-021-02266-4","volume":"38","author":"SM Mohtavipour","year":"2021","unstructured":"Mohtavipour, S.M., Saeidi, M., Arabsorkhi, A.: A multi-stream CNN for deep violence detection in video sequences using handcrafted features. Vis. Comput. 38, 2057\u20132072 (2021)","journal-title":"Vis. Comput."},{"key":"2865_CR11","first-page":"1","volume":"38","author":"MU Farooq","year":"2021","unstructured":"Farooq, M.U., Saad, M.N.M., Khan, S.D.: Motion-shape-based deep learning approach for divergence behavior detection in high-density crowd. Vis. Comput. 38, 1\u201325 (2021)","journal-title":"Vis. Comput."},{"key":"2865_CR12","doi-asserted-by":"publisher","first-page":"621","DOI":"10.1007\/s00371-019-01644-3","volume":"36","author":"Y Qin","year":"2020","unstructured":"Qin, Y., Mo, L., Li, C., Luo, J.: Skeleton-based action recognition by part-aware graph convolutional networks. Vis. Comput. 36, 621\u2013631 (2020)","journal-title":"Vis. Comput."},{"key":"2865_CR13","first-page":"1","volume":"38","author":"D Li","year":"2021","unstructured":"Li, D., Jahan, H., Huang, X., Feng, Z.: Human action recognition method based on historical point cloud trajectory characteristics. Vis. Comput. 38, 1\u20139 (2021)","journal-title":"Vis. Comput."},{"issue":"8","key":"2865_CR14","doi-asserted-by":"publisher","first-page":"1535","DOI":"10.1007\/s00371-019-01754-y","volume":"36","author":"J Fern\u00e1ndez-Ram\u00edrez","year":"2020","unstructured":"Fern\u00e1ndez-Ram\u00edrez, J., \u00c1lvarez-Meza, A., Pereira, E.M., Orozco-Guti\u00e9rrez, A., Castellanos-Dominguez, G.: Video-based social behavior recognition based on kernel relevance analysis. Vis. Comput. 36(8), 1535\u20131547 (2020)","journal-title":"Vis. Comput."},{"key":"2865_CR15","doi-asserted-by":"crossref","unstructured":"Hassner, T., Itcher, Y., Kliper-Gross, O.: Violent flows: real-time detection of violent crowd behaviour. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops,\u00a0pp. 1\u20136 (2012)","DOI":"10.1109\/CVPRW.2012.6239348"},{"key":"2865_CR16","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/j.imavis.2016.01.006","volume":"48","author":"Y Gao","year":"2016","unstructured":"Gao, Y., Liu, H., Sun, X., Wang, C., Liu, Y.: Violence detection using oriented violent flows. Image Vis. Comput. 48, 37\u201341 (2016)","journal-title":"Image Vis. Comput."},{"key":"2865_CR17","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1016\/j.patrec.2017.04.015","volume":"92","author":"AB Mabrouk","year":"2017","unstructured":"Mabrouk, A.B., Zagrouba, E.: Spatio-temporal feature using optical flow based distribution for violence detection. Pattern Recognit. Lett. 92, 62\u201367 (2017)","journal-title":"Pattern Recognit. Lett."},{"key":"2865_CR18","doi-asserted-by":"crossref","unstructured":"Nievas, E.B., Suarez, O.D., Garc\u00eda, G.B., Sukthankar, R.: Violence detection in video using computer vision techniques. In: International Conference on Computer Analysis of Images and Patterns, pp. 332\u2013339 (2011)","DOI":"10.1007\/978-3-642-23678-5_39"},{"key":"2865_CR19","doi-asserted-by":"crossref","unstructured":"Ehsan, T.Z., Nahvi, M.: Violence detection in indoor surveillance cameras using motion trajectory and differential histogram of optical flow. In: 8th International Conference on Computer and Knowledge Engineering (ICCKE), pp. 153\u2013158 (2018)","DOI":"10.1109\/ICCKE.2018.8566460"},{"issue":"4","key":"2865_CR20","doi-asserted-by":"publisher","first-page":"e0120448","DOI":"10.1371\/journal.pone.0120448","volume":"10","author":"I Serrano Gracia","year":"2015","unstructured":"Serrano Gracia, I., Deniz Suarez, O., Bueno Garcia, G., Kim, T.K.: Fast fight detection. PloS One 10(4), e0120448 (2015)","journal-title":"PloS One"},{"issue":"4","key":"2865_CR21","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1080\/08839514.2020.1723876","volume":"34","author":"S Accattoli","year":"2020","unstructured":"Accattoli, S., Sernani, P., Falcionelli, N., Mekuria, D.N., Dragoni, A.F.: Violence detection in videos by combining 3D convolutional neural networks and support vector machines. Appl. Artif. Intell. 34(4), 329\u2013344 (2020)","journal-title":"Appl. Artif. Intell."},{"key":"2865_CR22","doi-asserted-by":"crossref","unstructured":"Tran, D., Bourdev, L., Fergus, R., Torresani, L., Paluri, M.: Learning spatiotemporal features with 3d convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4489\u20134497 (2015)","DOI":"10.1109\/ICCV.2015.510"},{"key":"2865_CR23","doi-asserted-by":"crossref","unstructured":"Li, C., Zhu, L., Zhu, D., Chen, J., Pan, Z., Li, X., Wang, B.: End-to-end multiplayer violence detection based on deep 3D CNN. In: Proceedings of the VII International Conference on Network, Communication and Computing,\u00a0pp. 227\u2013230 (2018)","DOI":"10.1145\/3301326.3301367"},{"issue":"6","key":"2865_CR24","doi-asserted-by":"publisher","first-page":"1415","DOI":"10.1007\/s00371-020-01878-6","volume":"37","author":"M Asad","year":"2021","unstructured":"Asad, M., Yang, J., He, J., Shamsolmoali, P., He, X.: Multi-frame feature-fusion-based model for violence detection. Vis. Comput. 37(6), 1415\u20131431 (2021)","journal-title":"Vis. Comput."},{"key":"2865_CR25","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1007\/s11760-020-01740-1","volume":"15","author":"K Deepak","year":"2021","unstructured":"Deepak, K., Chandrakala, S., Mohan, C.K.: Residual spatiotemporal autoencoder for unsupervised video anomaly detection. SIViP 15, 215\u2013222 (2021)","journal-title":"SIViP"},{"key":"2865_CR26","doi-asserted-by":"crossref","unstructured":"Dong, Z., Qin, J., Wang, Y.: Multi-stream deep networks for person to person violence detection in videos. In: Chinese Conference on Pattern Recognition, pp. 517\u2013531 (2016)","DOI":"10.1007\/978-981-10-3002-4_43"},{"key":"2865_CR27","doi-asserted-by":"crossref","unstructured":"Ehsan, T.Z., Mohtavipour, S.M.: Vi-Net: a deep violent flow network for violence detection in video sequences. In: 11th International Conference on Information and Knowledge Technology (IKT),\u00a0pp. 88\u201392 (2020)","DOI":"10.1109\/IKT51791.2020.9345617"},{"issue":"10","key":"2865_CR28","doi-asserted-by":"publisher","first-page":"4787","DOI":"10.1109\/TIP.2018.2845742","volume":"27","author":"I Serrano","year":"2018","unstructured":"Serrano, I., Deniz, O., Espinosa-Aranda, J.L., Bueno, G.: Fight recognition in video using hough forests and 2D convolutional neural network. IEEE Trans. Image Process. 27(10), 4787\u20134797 (2018)","journal-title":"IEEE Trans. Image Process."},{"issue":"1","key":"2865_CR29","doi-asserted-by":"publisher","first-page":"49","DOI":"10.3233\/IDT-190360","volume":"13","author":"GT Foo","year":"2019","unstructured":"Foo, G.T., Goh, K.M.: Violence action recognition using region proposal in region convolution neural network. Intell. Decis. Technol. 13(1), 49\u201365 (2019)","journal-title":"Intell. Decis. Technol."},{"issue":"5\u20136","key":"2865_CR30","doi-asserted-by":"publisher","first-page":"796","DOI":"10.1177\/0020294020902788","volume":"53","author":"H Li","year":"2020","unstructured":"Li, H., Wang, J., Han, J., Zhang, J., Yang, Y., Zhao, Y.: A novel multi-stream method for violent interaction detection using deep learning. Meas. Control 53(5\u20136), 796\u2013806 (2020)","journal-title":"Meas. Control"},{"key":"2865_CR31","doi-asserted-by":"crossref","unstructured":"Ehsan, T.Z., Nahvi, M., Mohtavipour, S.M.: DABA-Net: deep acceleration-based autoencoder network for violence detection in surveillance cameras. In: International Conference on Machine Vision and Image Processing (MVIP), pp. 1\u20136 (2022)","DOI":"10.1109\/MVIP53647.2022.9738791"},{"key":"2865_CR32","first-page":"1","volume":"82","author":"TZ Ehsan","year":"2022","unstructured":"Ehsan, T.Z, Nahvi, M., Mohtavipour, S.M.: Learning deep latent space for unsupervised violence detection. Multimed. Tools Appl. 82, 1\u201320 (2022)","journal-title":"Multimed. Tools Appl."},{"issue":"9","key":"2865_CR33","doi-asserted-by":"publisher","first-page":"1627","DOI":"10.1109\/TPAMI.2009.167","volume":"32","author":"PF Felzenszwalb","year":"2009","unstructured":"Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1627\u20131645 (2009)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2865_CR34","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580\u2013587 (2014)","DOI":"10.1109\/CVPR.2014.81"},{"key":"2865_CR35","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,\u00a0pp. 779\u2013788 (2016)","DOI":"10.1109\/CVPR.2016.91"},{"key":"2865_CR36","unstructured":"Lucas, B.D., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: Proceedings of DARPA Image Understanding Workshop, pp. 121\u2013130 (1981)"},{"key":"2865_CR37","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1016\/0004-3702(81)90024-2","volume":"17","author":"BK Horn","year":"1981","unstructured":"Horn, B.K., Schunck, B.G.: Determining optical flow. Artif. Intell. 17, 185\u2013203 (1981)","journal-title":"Artif. Intell."},{"key":"2865_CR38","doi-asserted-by":"crossref","unstructured":"Farneb\u00e4ck, G.: Two-frame motion estimation based on polynomial expansion. In: Scandinavian Conference on Image Analysis, pp. 363\u2013370 (2003)","DOI":"10.1007\/3-540-45103-X_50"},{"key":"2865_CR39","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.-Y., Zhou, T., Efros, A.A..: Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1125\u20131134 (2017)","DOI":"10.1109\/CVPR.2017.632"},{"key":"2865_CR40","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 234\u2013241 (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"2865_CR41","doi-asserted-by":"publisher","first-page":"160580","DOI":"10.1109\/ACCESS.2021.3131315","volume":"9","author":"P Sernani","year":"2021","unstructured":"Sernani, P., Falcionelli, N., Tomassini, S., Contardo, P., Dragoni, A.F.: Deep learning for automatic violence detection: tests on the AIRTLab dataset. IEEE Access 9, 160580\u2013160595 (2021)","journal-title":"IEEE Access"},{"issue":"11","key":"2865_CR42","doi-asserted-by":"publisher","first-page":"2472","DOI":"10.3390\/s19112472","volume":"19","author":"FUM Ullah","year":"2019","unstructured":"Ullah, F.U.M., Ullah, A., Muhammad, K., Haq, I.U., Baik, S.W.: Violence detection using spatiotemporal features with 3D convolutional neural network. Sensors 19(11), 2472 (2019)","journal-title":"Sensors"},{"issue":"13","key":"2865_CR43","doi-asserted-by":"publisher","first-page":"1601","DOI":"10.3390\/electronics10131601","volume":"10","author":"FJ Rend\u00f3n-Segador","year":"2021","unstructured":"Rend\u00f3n-Segador, F.J., \u00c1lvarez-Garc\u00eda, J.A., Enr\u00edquez, F., Deniz, O.: Violencenet: dense multi-head self-attention with bidirectional convolutional lstm for detecting violence. Electronics 10(13), 1601 (2021)","journal-title":"Electronics"},{"key":"2865_CR44","doi-asserted-by":"crossref","unstructured":"Soliman, M.M., Kamal, M.H., Nashed, M.A.E.M., Mostafa, Y.M., Chawky, B.S., Khattab, D.: Violence recognition from videos using deep learning techniques. In: 9th International Conference on Intelligent Computing and Information Systems (ICICIS), pp. 80\u201385 (2019)","DOI":"10.1109\/ICICIS46948.2019.9014714"},{"key":"2865_CR45","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1016\/j.patrec.2017.08.021","volume":"107","author":"T Zhang","year":"2018","unstructured":"Zhang, T., Jia, W., Gong, C., Sun, J., Song, X.: Semi-supervised dictionary learning via local sparse constraints for violence detection. Pattern Recognit. Lett. 107, 98\u2013104 (2018)","journal-title":"Pattern Recognit. Lett."},{"key":"2865_CR46","doi-asserted-by":"publisher","first-page":"108213","DOI":"10.1016\/j.patcog.2021.108213","volume":"122","author":"Y Chang","year":"2022","unstructured":"Chang, Y., Tu, Z., Xie, W., Luo, B., Zhang, S., Sui, H., Yuan, J.: Video anomaly detection with spatio-temporal dissociation. Pattern Recognit. 122, 108213 (2022)","journal-title":"Pattern Recognit."},{"key":"2865_CR47","doi-asserted-by":"publisher","first-page":"114916","DOI":"10.1016\/j.eswa.2021.114916","volume":"177","author":"H Buckchash","year":"2021","unstructured":"Buckchash, H., Raman, B.: Towards zero shot learning of geometry of motion streams and its application to anomaly recognition. Expert Syst. Appl. 177, 114916 (2021)","journal-title":"Expert Syst. Appl."},{"key":"2865_CR48","doi-asserted-by":"publisher","first-page":"108232","DOI":"10.1016\/j.patcog.2021.108232","volume":"121","author":"Y Hao","year":"2022","unstructured":"Hao, Y., Li, J., Wang, N., Wang, X., Gao, X.: Spatiotemporal consistency-enhanced network for video anomaly detection. Pattern Recognit. 121, 108232 (2022)","journal-title":"Pattern Recognit."},{"key":"2865_CR49","doi-asserted-by":"publisher","first-page":"270","DOI":"10.1016\/j.neucom.2019.11.087","volume":"383","author":"X Hu","year":"2020","unstructured":"Hu, X., Dai, J., Huang, Y.P., Yang, H.M., Zhang, L., Chen, W.M., Yang, G.K., Zhang, D.W.: A weakly supervised framework for abnormal behavior detection and localization. Neurocomputing 383, 270\u2013281 (2020)","journal-title":"Neurocomputing"},{"issue":"1","key":"2865_CR50","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1007\/s11760-020-01740-1","volume":"15","author":"K Deepak","year":"2021","unstructured":"Deepak, K., Chandrakala, S., Mohan, C.K.: Residual spatiotemporal autoencoder for unsupervised video anomaly detection. SIViP 15(1), 215\u2013222 (2021)","journal-title":"SIViP"},{"key":"2865_CR51","doi-asserted-by":"publisher","first-page":"33353","DOI":"10.1109\/ACCESS.2018.2848210","volume":"6","author":"J Sun","year":"2018","unstructured":"Sun, J., Wang, X., Xiong, N., Shao, J.: Learning sparse representation with variational auto-encoder for anomaly detection. IEEE Access 6, 33353\u201333361 (2018)","journal-title":"IEEE Access"},{"key":"2865_CR52","unstructured":"Samuel, D.J., Cuzzolin, F.: Svd-gan for real-time unsupervised video anomaly detection (2021)."}],"container-title":["The Visual Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-023-02865-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00371-023-02865-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-023-02865-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,19]],"date-time":"2024-10-19T18:38:45Z","timestamp":1729363125000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00371-023-02865-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,3]]},"references-count":52,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,3]]}},"alternative-id":["2865"],"URL":"https:\/\/doi.org\/10.1007\/s00371-023-02865-3","relation":{},"ISSN":["0178-2789","1432-2315"],"issn-type":[{"value":"0178-2789","type":"print"},{"value":"1432-2315","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,3]]},"assertion":[{"value":"6 April 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 May 2023","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Authors certified that they have no conflicts of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}