{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T04:13:28Z","timestamp":1778645608706,"version":"3.51.4"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T00:00:00Z","timestamp":1726185600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T00:00:00Z","timestamp":1726185600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62177022"],"award-info":[{"award-number":["62177022"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61901165"],"award-info":[{"award-number":["61901165"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Circuits Syst Signal Process"],"published-print":{"date-parts":[[2025,1]]},"DOI":"10.1007\/s00034-024-02850-8","type":"journal-article","created":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T05:01:56Z","timestamp":1726203716000},"page":"480-512","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Squeeze-and-Excitation Self-Attention Mechanism Enhanced Digital Audio Source Recognition Based on Transfer Learning"],"prefix":"10.1007","volume":"44","author":[{"given":"Chunyan","family":"Zeng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuhao","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6960-509X","authenticated-orcid":false,"given":"Zhifeng","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kun","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangkui","family":"Wan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Min","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,9,13]]},"reference":[{"issue":"5","key":"2850_CR1","first-page":"2813","volume":"16","author":"ND Ahakarchy","year":"2024","unstructured":"N.D. Ahakarchy, Z.N. Abdullah, Z.M. Alameen, Z.A. Harjan, Audio verification in forensic investigation using light deep neural network. Int. J. Inf. Technol. 16(5), 2813\u20132821 (2024)","journal-title":"Int. J. Inf. Technol."},{"issue":"2","key":"2850_CR2","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1109\/MSP.2006.1598091","volume":"23","author":"BS Atal","year":"2006","unstructured":"B.S. Atal, The history of linear prediction. IEEE Signal Process. Mag. 23(2), 154\u2013161 (2006)","journal-title":"IEEE Signal Process. Mag."},{"key":"2850_CR3","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1016\/j.neunet.2021.03.004","volume":"140","author":"Z Bai","year":"2021","unstructured":"Z. Bai, X. Zhong, Speaker recognition based on deep learning: an overview. Neural Netw. 140, 65\u201399 (2021)","journal-title":"Neural Netw."},{"issue":"7","key":"2850_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/LSENS.2019.2923590","volume":"3","author":"G Baldini","year":"2019","unstructured":"G. Baldini, I. Amerini, C. Gentile, Microphone identification using convolutional neural networks. IEEE Sens. Lett. 3(7), 1\u20134 (2019)","journal-title":"IEEE Sens. Lett."},{"key":"2850_CR5","doi-asserted-by":"crossref","unstructured":"R. Buchholz, C. Kraetzer, J. Dittmann, Microphone classification using Fourier coefficients, in Proceedings of Information Hiding, 11th International Workshop, pp. 235\u2013246 (2009)","DOI":"10.1007\/978-3-642-04431-1_17"},{"issue":"3","key":"2850_CR6","doi-asserted-by":"publisher","first-page":"2114","DOI":"10.3758\/s13428-023-02139-9","volume":"56","author":"F Busquet","year":"2024","unstructured":"F. Busquet, F. Efthymiou, C. Hildebrand, Voice analytics in the wild: validity and predictive accuracy of common audio-recording devices. Behav. Res. Methods 56(3), 2114\u20132134 (2024)","journal-title":"Behav. Res. Methods"},{"key":"2850_CR7","doi-asserted-by":"crossref","unstructured":"W.M. Campbell, Generalized linear discriminant sequence kernels for speaker recognition, in Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, 1, pp. 161\u2013164 (2002)","DOI":"10.1109\/ICASSP.2002.5743679"},{"key":"2850_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11042-023-14942-9","volume":"82","author":"R Chakroun","year":"2023","unstructured":"R. Chakroun, M. Frikha, A deep learning approach for text-independent speaker recognition with short utterances. Multimed. Tools Appl. 82, 1\u201323 (2023)","journal-title":"Multimed. Tools Appl."},{"key":"2850_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.111077","volume":"281","author":"Z Chen","year":"2023","unstructured":"Z. Chen, M. Lin, Z. Wang, Q. Zheng, C. Liu, Spatio-temporal representation learning enhanced speech emotion recognition with multi-head attention mechanisms. Knowl. Based Syst. 281, 111077 (2023)","journal-title":"Knowl. Based Syst."},{"key":"2850_CR10","doi-asserted-by":"publisher","first-page":"788","DOI":"10.1109\/TASL.2010.2064307","volume":"19","author":"N Dehak","year":"2011","unstructured":"N. Dehak, P.J. Kenny, R. Dehak, P. Dumouchel, P. Ouellet, Front-end factor analysis for speaker verification. IEEE Trans. Audio Speech Lang. Process. 19, 788\u2013798 (2011)","journal-title":"IEEE Trans. Audio Speech Lang. Process."},{"key":"2850_CR11","doi-asserted-by":"publisher","first-page":"2597","DOI":"10.1109\/TASLP.2022.3195113","volume":"30","author":"M Geng","year":"2022","unstructured":"M. Geng, X. Xie, Z. Ye, T. Wang, G. Li, S. Hu, X. Liu, H. Meng, Speaker adaptation using spectro-temporal deep features for dysarthric and elderly speech recognition. IEEE\/ACM Trans. Audio Speech Lang. Process. 30, 2597\u20132611 (2022)","journal-title":"IEEE\/ACM Trans. Audio Speech Lang. Process."},{"key":"2850_CR12","doi-asserted-by":"crossref","unstructured":"C. Hanil\u00e7i, F. Ertas, Optimizing acoustic features for source cell-phone recognition using speech signals, in Proceedings of the First ACM Workshop on Information Hiding and Multimedia Security, pp. 141\u2013148 (2013)","DOI":"10.1145\/2482513.2482520"},{"issue":"2","key":"2850_CR13","doi-asserted-by":"publisher","first-page":"625","DOI":"10.1109\/TIFS.2011.2178403","volume":"7","author":"C Hanil\u00e7i","year":"2012","unstructured":"C. Hanil\u00e7i, F. Ertas, T. Ertas, \u00d6. Eskidere, Recognition of brand and models of cell-phones from recorded speech signals. IEEE Trans. Inf. Forensics Secur. 7(2), 625\u2013634 (2012)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"2850_CR14","doi-asserted-by":"publisher","first-page":"1309","DOI":"10.1007\/s10916-010-9591-z","volume":"36","author":"M Hariharan","year":"2012","unstructured":"M. Hariharan, L.S. Chee, S. Yaacob, Analysis of infant cry through weighted linear prediction cepstral coefficients and probabilistic neural network. J. Med. Syst. 36, 1309\u20131315 (2012)","journal-title":"J. Med. Syst."},{"key":"2850_CR15","doi-asserted-by":"crossref","unstructured":"K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"issue":"4","key":"2850_CR16","doi-asserted-by":"publisher","first-page":"1738","DOI":"10.1121\/1.399423","volume":"87","author":"H Hermansky","year":"1990","unstructured":"H. Hermansky, Perceptual linear predictive (plp) analysis of speech. J. Acoust. Soc. Am. 87(4), 1738\u20131752 (1990)","journal-title":"J. Acoust. Soc. Am."},{"key":"2850_CR17","doi-asserted-by":"crossref","unstructured":"J. Hu, L. Shen, G. Sun, Squeeze-and-excitation networks, in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7132\u20137141 (2018)","DOI":"10.1109\/CVPR.2018.00745"},{"issue":"1","key":"2850_CR18","first-page":"186","volume":"15","author":"YA Ibrahim","year":"2017","unstructured":"Y.A. Ibrahim, J.C. Odiketa, T.S. Ibiyemi, Preprocessing technique in automatic speech recognition for human computer interaction: an overview. Ann. Comput. Sci. Ser. 15(1), 186\u2013191 (2017)","journal-title":"Ann. Comput. Sci. Ser."},{"key":"2850_CR19","doi-asserted-by":"publisher","first-page":"79236","DOI":"10.1109\/ACCESS.2021.3084299","volume":"9","author":"MM Kabir","year":"2021","unstructured":"M.M. Kabir, M.F. Mridha, J. Shin, I. Jahan, A.Q. Ohi, A survey of speaker recognition: fundamental theories, recognition methods and opportunities. IEEE Access. 9, 79236\u201379263 (2021)","journal-title":"IEEE Access."},{"issue":"1","key":"2850_CR20","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1016\/j.specom.2009.08.009","volume":"52","author":"T Kinnunen","year":"2010","unstructured":"T. Kinnunen, H. Li, An overview of text-independent speaker recognition: from features to supervectors. Speech Commun. 52(1), 12\u201340 (2010)","journal-title":"Speech Commun."},{"issue":"2","key":"2850_CR21","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1049\/iet-bmt.2013.0056","volume":"3","author":"C Kotropoulos","year":"2014","unstructured":"C. Kotropoulos, Source phone identification using sketches of features. IET Biom. 3(2), 75\u201383 (2014)","journal-title":"IET Biom."},{"key":"2850_CR22","doi-asserted-by":"crossref","unstructured":"C. Kotropoulos, S. Samaras, Mobile phone identification using recorded speech signals, in Proceedings of 19th International Conference on Digital Signal Processing, pp. 586\u2013591 (2014)","DOI":"10.1109\/ICDSP.2014.6900732"},{"key":"2850_CR23","doi-asserted-by":"crossref","unstructured":"Y. Lei, N. Scheffer, L. Ferrer, M. McLaren, A novel scheme for speaker recognition using a phonetically-aware deep neural network, in Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1695\u20131699 (2014)","DOI":"10.1109\/ICASSP.2014.6853887"},{"key":"2850_CR24","unstructured":"B. Logan, Mel frequency cepstral coefficients for music modeling, in Proceedings of Ismir, 1, pp. 11 (2000)"},{"key":"2850_CR25","doi-asserted-by":"publisher","first-page":"2179","DOI":"10.1109\/TIFS.2018.2812185","volume":"13","author":"D Luo","year":"2018","unstructured":"D. Luo, P. Korus, J. Huang, Band energy difference for source attribution in audio forensics. IEEE Trans. Inf. Forensics Secur. 13, 2179\u20132189 (2018)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"2850_CR26","doi-asserted-by":"publisher","first-page":"89619","DOI":"10.1109\/ACCESS.2021.3090109","volume":"9","author":"AQ Ohi","year":"2021","unstructured":"A.Q. Ohi, M.F. Mridha, M.A. Hamid, M.M. Monowar, Deep speaker recognition: process, progress, and challenges. IEEE Access. 9, 89619\u201389643 (2021)","journal-title":"IEEE Access."},{"issue":"10","key":"2850_CR27","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","volume":"22","author":"SJ Pan","year":"2010","unstructured":"S.J. Pan, Q. Yang, A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345\u20131359 (2010)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"2850_CR28","doi-asserted-by":"crossref","unstructured":"Y. Panagakis, C. Kotropoulos, Automatic telephone handset identification by sparse representation of random spectral features, in Proceedings of the on Multimedia and Security, pp. 91\u201396 (2012)","DOI":"10.1145\/2361407.2361422"},{"key":"2850_CR29","doi-asserted-by":"crossref","unstructured":"Y. Panagakis, C. Kotropoulos, Telephone handset identification by feature selection and sparse representations, in Proceedings of IEEE International Workshop on Information Forensics and Security (WIFS), pp. 73\u201378 (2012)","DOI":"10.1109\/WIFS.2012.6412628"},{"issue":"5","key":"2850_CR30","doi-asserted-by":"publisher","first-page":"1012","DOI":"10.1109\/TASL.2013.2243436","volume":"21","author":"W Rao","year":"2013","unstructured":"W. Rao, M.W. Mak, Boosting the performance of i-vector based speaker verification via utterance partitioning. IEEE Trans. Audio Speech Lang. Process. 21(5), 1012\u20131022 (2013)","journal-title":"IEEE Trans. Audio Speech Lang. Process."},{"key":"2850_CR31","doi-asserted-by":"crossref","unstructured":"D. Snyder, D. Garcia-Romero, G. Sell, D. Povey, S. Khudanpur, X-vectors: Robust dnn embeddings for speaker recognition, in Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5329\u20135333 (2018)","DOI":"10.1109\/ICASSP.2018.8461375"},{"issue":"5","key":"2850_CR32","first-page":"1278","volume":"3","author":"T Suchitha","year":"2015","unstructured":"T. Suchitha, A. Bindu, Feature extraction using mfcc and classification using gmm. Int. J. Sci. Res. Dev. 3(5), 1278\u20131283 (2015)","journal-title":"Int. J. Sci. Res. Dev."},{"key":"2850_CR33","doi-asserted-by":"crossref","unstructured":"E. Variani, X. Lei, E. McDermott, I.L. Moreno, J. Gonzalez-Dominguez, Deep neural networks for small footprint text-dependent speaker verification, in Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4052\u20134056 (2014)","DOI":"10.1109\/ICASSP.2014.6854363"},{"key":"2850_CR34","unstructured":"A. Veit, M.J. Wilber, S. Belongie, Residual networks behave like ensembles of relatively shallow networks, in Advances in Neural Information Processing Systems, vol. 29 (2016)"},{"issue":"69","key":"2850_CR35","first-page":"1","volume":"2022","author":"Z Wang","year":"2022","unstructured":"Z. Wang, Y. Yang, C. Zeng, S. Kong, S. Feng, N. Zhao, Shallow and deep feature fusion for digital audio tampering detection. EURASIP J. Adv. Signal Process. 2022(69), 1\u201320 (2022)","journal-title":"EURASIP J. Adv. Signal Process."},{"issue":"14","key":"2850_CR36","doi-asserted-by":"publisher","first-page":"11272","DOI":"10.3390\/su151411272","volume":"15","author":"Z Wang","year":"2023","unstructured":"Z. Wang, J. Zhan, G. Zhang, D. Ouyang, H. Guo, An end-to-end transfer learning framework of source recording device identification for audio sustainable security. Sustainability 15(14), 11272 (2023)","journal-title":"Sustainability"},{"key":"2850_CR37","volume":"80","author":"C Zeng","year":"2024","unstructured":"C. Zeng, S. Feng, Z. Wang, X. Wan, Y. Chen, N. Zhao, Spatio-temporal representation learning enhanced source cell-phone recognition from speech recordings. J. Inf. Secur. Appl. 80, 103672 (2024)","journal-title":"J. Inf. Secur. Appl."},{"key":"2850_CR38","volume":"48","author":"C Zeng","year":"2024","unstructured":"C. Zeng, S. Feng, Z. Wang, Y. Zhao, K. Li, X. Wan, Audio source recording device recognition based on representation learning of sequential gaussian mean matrix. Forensic Sci. Int. Digit. Investig. 48, 301676 (2024)","journal-title":"Forensic Sci. Int. Digit. Investig."},{"issue":"4","key":"2850_CR39","doi-asserted-by":"publisher","first-page":"626","DOI":"10.3390\/e25040626","volume":"25","author":"C Zeng","year":"2023","unstructured":"C. Zeng, S. Feng, D. Zhu, Z. Wang, Source acquisition device identification from recorded audio based on spatiotemporal representation learning with multi-attention mechanisms. Entropy 25(4), 626 (2023)","journal-title":"Entropy"},{"key":"2850_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.specom.2024.103046","volume":"158","author":"C Zeng","year":"2024","unstructured":"C. Zeng, S. Kong, Z. Wang, S. Feng, N. Zhao, J. Wang, Deletion and insertion tampering detection for speech authentication based on fluctuating super vector of electrical network frequency. Speech Commun. 158, 103046 (2024)","journal-title":"Speech Commun."},{"issue":"5","key":"2850_CR41","doi-asserted-by":"publisher","first-page":"253","DOI":"10.3390\/info14050253","volume":"14","author":"C Zeng","year":"2023","unstructured":"C. Zeng, S. Kong, Z. Wang, K. Li, Y. Zhao, Digital audio tampering detection based on deep temporal-spatial features of electrical network frequency. Information 14(5), 253 (2023)","journal-title":"Information"},{"key":"2850_CR42","first-page":"1","volume":"2024","author":"C Zeng","year":"2024","unstructured":"C. Zeng, S. Kong, Z. Wang, K. Li, Y. Zhao, X. Wan, Y. Chen, Digital audio tampering detection based on spatio-temporal representation learning of electrical network frequency. Multimed. Tools Appl. 2024, 1\u201323 (2024)","journal-title":"Multimed. Tools Appl."},{"key":"2850_CR43","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2024.111938","volume":"297","author":"C Zeng","year":"2024","unstructured":"C. Zeng, K. Li, Z. Wang, Enfformer: long-short term representation of electric network frequency for digital audio tampering detection. Knowl. Based Syst. 297, 111938 (2024)","journal-title":"Knowl. Based Syst."},{"issue":"1","key":"2850_CR44","doi-asserted-by":"publisher","first-page":"1","DOI":"10.4018\/IJDCF.302894","volume":"14","author":"C Zeng","year":"2022","unstructured":"C. Zeng, Y. Yang, Z. Wang, S. Kong, S. Feng, Audio tampering forensics based on representation learning of enf phase sequence. Int. J. Digit. Crime Forensics 14(1), 1\u201319 (2022)","journal-title":"Int. J. Digit. Crime Forensics"},{"issue":"1","key":"2850_CR45","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13634-021-00763-1","volume":"2021","author":"C Zeng","year":"2021","unstructured":"C. Zeng, D. Zhu, Z. Wang, M. Wu, W. Xiong, N. Zhao, Spatial and temporal learning representation for end-to-end recording device identification. EURASIP J. Adv. Signal Process. 2021(1), 1\u201319 (2021)","journal-title":"EURASIP J. Adv. Signal Process."},{"issue":"4","key":"2850_CR46","doi-asserted-by":"publisher","first-page":"413","DOI":"10.1108\/IJWIS-06-2020-0038","volume":"16","author":"C Zeng","year":"2020","unstructured":"C. Zeng, D. Zhu, Z. Wang, Z. Wang, N. Zhao, L. He, An end-to-end deep source recording device identification system for web media forensics. Int. J. Web Inf. Syst. 16(4), 413\u2013425 (2020)","journal-title":"Int. J. Web Inf. Syst."},{"key":"2850_CR47","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.123004","volume":"244","author":"Q Zheng","year":"2024","unstructured":"Q. Zheng, Z. Chen, Z. Wang, H. Liu, M. Lin, Meconformer: highly representative embedding extractor for speaker verification via incorporating selective convolution into deep speaker encoder. Expert Syst. Appl. 244, 123004 (2024)","journal-title":"Expert Syst. Appl."},{"key":"2850_CR48","doi-asserted-by":"crossref","unstructured":"L. Zou, Q. He, X. Feng, Cell phone verification from speech recordings using sparse representation, in Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1787\u20131791 (2015)","DOI":"10.1109\/ICASSP.2015.7178278"},{"key":"2850_CR49","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1016\/j.dsp.2016.10.017","volume":"62","author":"L Zou","year":"2017","unstructured":"L. Zou, Q. He, J. Wu, Source cell phone verification from speech recordings using sparse representation. Digit. Signal Process. 62, 125\u2013136 (2017)","journal-title":"Digit. Signal Process."},{"key":"2850_CR50","doi-asserted-by":"crossref","unstructured":"L. Zou, Q. He, J. Yang, Y. Li, Source cell phone matching from speech recordings by sparse representation and kiss metric, in Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2079\u20132083 (2016)","DOI":"10.1109\/ICASSP.2016.7472043"}],"container-title":["Circuits, Systems, and Signal Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00034-024-02850-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00034-024-02850-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00034-024-02850-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,20]],"date-time":"2025-01-20T13:33:38Z","timestamp":1737380018000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00034-024-02850-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,13]]},"references-count":50,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["2850"],"URL":"https:\/\/doi.org\/10.1007\/s00034-024-02850-8","relation":{},"ISSN":["0278-081X","1531-5878"],"issn-type":[{"value":"0278-081X","type":"print"},{"value":"1531-5878","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,13]]},"assertion":[{"value":"4 July 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 August 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 August 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 September 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":"No potential conflict of interest was reported by the authors.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}