{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T21:20:35Z","timestamp":1758403235984,"version":"3.37.3"},"reference-count":65,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,3,14]],"date-time":"2023-03-14T00:00:00Z","timestamp":1678752000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,3,14]],"date-time":"2023-03-14T00:00:00Z","timestamp":1678752000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100014718","name":"Innovative Research Group Project of the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61672470"],"award-info":[{"award-number":["61672470"]}],"id":[{"id":"10.13039\/100014718","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Major Public Welfare Projects in Henan Province, China","award":["201300210200"],"award-info":[{"award-number":["201300210200"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Complex Intell. Syst."],"published-print":{"date-parts":[[2024,2]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Entity resolution, accurately identifying various representations of the same real-world entities, is a crucial part of data integration systems. While existing learning-based models can achieve good performance, the models are extremely dependent on the quantity and quality of training data. In this paper, the MixER model is proposed to alleviate these problems. The MixER utilizes our newly designed data augmentation method called EMix. The EMix can map discrete entity records to continuous latent space variables (e.g., probability distributions) and then linearly interpolate entity records in latent space to generate many augmented training samples. The matching model is further optimized based on the augmented data to strengthen its generalization capability. The MixER model achieves significant strengths in the data sensitivity experiments when training data is below 50. In robustness experiments, the MixER model presents an absolute performance advantage when the label noise exceeds 20%. In addition, ablation experiments demonstrate that the newly developed EMix can effectively improve the generalization ability of the matching model. The overall experimental results prove that the MixER model exhibited excellent data sensitivity and robustness over the current state-of-the-art methods.<\/jats:p>","DOI":"10.1007\/s40747-023-01018-2","type":"journal-article","created":{"date-parts":[[2023,3,14]],"date-time":"2023-03-14T07:04:07Z","timestamp":1678777447000},"page":"3-22","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["MixER: linear interpolation of latent space for entity resolution"],"prefix":"10.1007","volume":"10","author":[{"given":"Huaiguang","family":"Wu","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2645-0398","authenticated-orcid":false,"given":"Shuaichao","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,3,14]]},"reference":[{"key":"1018_CR1","unstructured":"Amodei D, Ananthanarayanan S, Anubhai R, et\u00a0al (2016) Deep speech 2: End-to-end speech recognition in english and mandarin. In: Proceedings of the International Conference on Machine Learning, PMLR, pp 173\u2013182"},{"key":"1018_CR2","doi-asserted-by":"crossref","unstructured":"Ananthakrishna R, Chaudhuri S, Ganti V (2002) Eliminating fuzzy duplicates in data warehouses. In: Proceedings of the 28th International Conference on Very Large Databases. Elsevier, pp 586\u2013597","DOI":"10.1016\/B978-155860869-6\/50058-5"},{"issue":"21","key":"1018_CR3","doi-asserted-by":"publisher","first-page":"2705","DOI":"10.3390\/math9212705","volume":"9","author":"N Bacanin","year":"2021","unstructured":"Bacanin N, Stoean R, Zivkovic M et al (2021) Performance of a novel chaotic firefly algorithm with enhanced exploration for tackling global optimization problems: Application for dropout regularization. Mathematics 9(21):2705","journal-title":"Mathematics"},{"issue":"3","key":"1018_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3442200","volume":"15","author":"N Barlaug","year":"2021","unstructured":"Barlaug N, Gulla JA (2021) Neural networks for entity matching: a survey. ACM Trans Knowl Discov Data 15(3):1\u201337","journal-title":"ACM Trans Knowl Discov Data"},{"key":"1018_CR5","doi-asserted-by":"crossref","unstructured":"Bilenko M, Mooney RJ (2003) Adaptive duplicate detection using learnable string similarity measures. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, pp 39\u201348","DOI":"10.1145\/956750.956759"},{"key":"1018_CR6","doi-asserted-by":"crossref","unstructured":"Bogatu A, Paton NW, Douthwaite M, et\u00a0al (2021) Cost\u2013effective variational active entity resolution. In: Proceedings of the 2021 IEEE 37th International Conference on Data Engineering. IEEE, pp 1272\u20131283","DOI":"10.1109\/ICDE51399.2021.00114"},{"key":"1018_CR7","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla NV, Bowyer KW, Hall LO et al (2002) Smote: synthetic minority over-sampling technique. J Artif Intell Res 16:321\u2013357","journal-title":"J Artif Intell Res"},{"key":"1018_CR8","doi-asserted-by":"crossref","unstructured":"Chen J, Yang Z, Yang D (2020) Mixtext: Linguistically-informed interpolation of hidden space for semi-supervised text classification. arXiv","DOI":"10.18653\/v1\/2020.acl-main.194"},{"key":"1018_CR9","doi-asserted-by":"crossref","unstructured":"Cohen WW, Richman J (2002a) Learning to match and cluster large high-dimensional data sets for data integration. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, pp 475\u2013480","DOI":"10.1145\/775047.775116"},{"key":"1018_CR10","doi-asserted-by":"crossref","unstructured":"Cohen WW, Richman J (2002b) Learning to match and cluster large high-dimensional data sets for data integration. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, pp 475\u2013480","DOI":"10.1145\/775047.775116"},{"issue":"1","key":"1018_CR11","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1109\/MSP.2017.2765202","volume":"35","author":"A Creswell","year":"2018","unstructured":"Creswell A, White T, Dumoulin V et al (2018) Generative adversarial networks: an overview. IEEE Signal Process Mag 35(1):53\u201365","journal-title":"IEEE Signal Process Mag"},{"key":"1018_CR12","unstructured":"Devlin J, Chang MW, Lee K, et\u00a0al (2018) Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv"},{"issue":"3","key":"1018_CR13","doi-asserted-by":"publisher","first-page":"878","DOI":"10.1214\/aoms\/1177698881","volume":"38","author":"K Doksum","year":"1967","unstructured":"Doksum K (1967) Robust procedures for some linear models with one observation per cell. Ann Math Stat 38(3):878\u2013883","journal-title":"Ann Math Stat"},{"issue":"11","key":"1018_CR14","doi-asserted-by":"publisher","first-page":"1454","DOI":"10.14778\/3236187.3236198","volume":"11","author":"M Ebraheem","year":"2018","unstructured":"Ebraheem M, Thirumuruganathan S, Joty S et al (2018) Distributed representations of tuples for entity resolution. Proc VLDB Endow 11(11):1454\u20131467","journal-title":"Proc VLDB Endow"},{"key":"1018_CR15","doi-asserted-by":"crossref","unstructured":"Elmagarmid A, Ilyas IF, Ouzzani M, et\u00a0al (2014) Nadeef\/er: generic and interactive entity resolution. In: Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data. Association for Computing Machinery, pp 1071\u20131074","DOI":"10.1145\/2588555.2594511"},{"issue":"1","key":"1018_CR16","doi-asserted-by":"publisher","first-page":"407","DOI":"10.14778\/1687627.1687674","volume":"2","author":"W Fan","year":"2009","unstructured":"Fan W, Jia X, Li J et al (2009) Reasoning about record matching rules. Proc VLDB Endow 2(1):407\u2013418","journal-title":"Proc VLDB Endow"},{"issue":"328","key":"1018_CR17","doi-asserted-by":"publisher","first-page":"1183","DOI":"10.1080\/01621459.1969.10501049","volume":"64","author":"IP Fellegi","year":"1969","unstructured":"Fellegi IP, Sunter AB (1969) A theory for record linkage. J Am Stat Assoc 64(328):1183\u20131210","journal-title":"J Am Stat Assoc"},{"issue":"328","key":"1018_CR18","doi-asserted-by":"publisher","first-page":"1183","DOI":"10.1080\/01621459.1969.10501049","volume":"64","author":"IP Fellegi","year":"1969","unstructured":"Fellegi IP, Sunter AB (1969) A theory for record linkage. J Am Stat Assoc 64(328):1183\u20131210","journal-title":"J Am Stat Assoc"},{"issue":"200","key":"1018_CR19","doi-asserted-by":"publisher","first-page":"675","DOI":"10.1080\/01621459.1937.10503522","volume":"32","author":"M Friedman","year":"1937","unstructured":"Friedman M (1937) The use of ranks to avoid the assumption of normality implicit in the analysis of variance. J Am Stat Assoc 32(200):675\u2013701","journal-title":"J Am Stat Assoc"},{"issue":"17","key":"1018_CR20","doi-asserted-by":"publisher","first-page":"4387","DOI":"10.1109\/TSP.2013.2269047","volume":"61","author":"G Gallego","year":"2013","unstructured":"Gallego G, Cuevas C, Mohedano R et al (2013) On the mahalanobis distance classification criterion for multidimensional normal distributions. IEEE Trans Signal Process 61(17):4387\u20134396","journal-title":"IEEE Trans Signal Process"},{"key":"1018_CR21","doi-asserted-by":"crossref","unstructured":"Garcia-Molina H (2004) Entity resolution: Overview and challenges. In: Proceedings of the International Conference on Conceptual Modeling. Springer, pp 1\u20132","DOI":"10.1007\/978-3-540-30464-7_1"},{"key":"1018_CR22","doi-asserted-by":"crossref","unstructured":"Graves A, Mohamed Ar, Hinton G (2013) Speech recognition with deep recurrent neural networks. In: Proceedings of the 2013 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, pp 6645\u20136649","DOI":"10.1109\/ICASSP.2013.6638947"},{"key":"1018_CR23","doi-asserted-by":"crossref","unstructured":"Guha S, Koudas N, Marathe A, et\u00a0al (2004) Merging the results of approximate match operations. In: Proceedings of the Thirtieth International Conference on Very Large Data Bases. VLDB Endowment, pp 636\u2013647","DOI":"10.1016\/B978-012088469-8.50057-7"},{"issue":"2","key":"1018_CR24","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1145\/568271.223807","volume":"24","author":"MA Hern\u00e1ndez","year":"1995","unstructured":"Hern\u00e1ndez MA, Stolfo SJ (1995) The merge\/purge problem for large databases. ACM Sigmod Record 24(2):127\u2013138","journal-title":"ACM Sigmod Record"},{"key":"1018_CR25","doi-asserted-by":"crossref","unstructured":"Hodges J, Lehmann EL (2012) Rank methods for combination of independent experiments in analysis of variance. In: Proceedings of the Selected Works of EL Lehmann. Springer, p 403\u2013418","DOI":"10.1007\/978-1-4614-1412-4_35"},{"key":"1018_CR26","doi-asserted-by":"crossref","unstructured":"Hou B, Chen Q, Shen J, et\u00a0al (2019) Gradual machine learning for entity resolution. In: Proceedings of the World Wide Web Conference. Association for Computing Machinery, pp 3526\u20133530","DOI":"10.1145\/3308558.3314121"},{"issue":"3","key":"1018_CR27","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1109\/2.485891","volume":"29","author":"AK Jain","year":"1996","unstructured":"Jain AK, Mao J, Mohiuddin KM (1996) Artificial neural networks: a tutorial. Computer 29(3):31\u201344","journal-title":"Computer"},{"issue":"406","key":"1018_CR28","doi-asserted-by":"publisher","first-page":"414","DOI":"10.1080\/01621459.1989.10478785","volume":"84","author":"MA Jaro","year":"1989","unstructured":"Jaro MA (1989) Advances in record-linkage methodology as applied to matching the 1985 census of Tampa, Florida. J Am Stat Assoc 84(406):414\u2013420","journal-title":"J Am Stat Assoc"},{"key":"1018_CR29","unstructured":"John GH, Langley P (2013) Estimating continuous distributions in bayesian classifiers. arXiv"},{"key":"1018_CR30","doi-asserted-by":"crossref","unstructured":"Kasai J, Qian K, Gurajada S, et\u00a0al (2019) Low-resource deep entity resolution with transfer and active learning. arXiv","DOI":"10.18653\/v1\/P19-1586"},{"issue":"3","key":"1018_CR31","doi-asserted-by":"publisher","first-page":"637","DOI":"10.1162\/089976601300014493","volume":"13","author":"SS Keerthi","year":"2001","unstructured":"Keerthi SS, Shevade SK, Bhattacharyya C et al (2001) Improvements to platt\u2019s smo algorithm for svm classifier design. Neural comput 13(3):637\u2013649","journal-title":"Neural comput"},{"key":"1018_CR32","unstructured":"Kingma DP, Welling M (2013) Auto-encoding variational bayes. arXiv"},{"issue":"13","key":"1018_CR33","doi-asserted-by":"publisher","first-page":"1581","DOI":"10.14778\/3007263.3007314","volume":"9","author":"P Konda","year":"2016","unstructured":"Konda P, Das S, Doan A et al (2016) Magellan: toward building entity matching management systems over data science stacks. Proc VLDB Endow 9(13):1581\u20131584","journal-title":"Proc VLDB Endow"},{"issue":"6","key":"1018_CR34","first-page":"84","volume":"25","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 25(6):84\u201390","journal-title":"Adv Neural Inf Process Syst"},{"issue":"11","key":"1018_CR35","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun Y, Bottou L, Bengio Y et al (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278\u20132324","journal-title":"Proc IEEE"},{"issue":"7553","key":"1018_CR36","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436\u2013444","journal-title":"Nature"},{"key":"1018_CR37","unstructured":"Li Y, Liu DB, Zhang WM (2005) Schema matching using neural network. In: Proceedings of the 2005 IEEE\/WIC\/ACM International Conference on Web Intelligence. IEEE, pp 743\u2013746"},{"issue":"1","key":"1018_CR38","doi-asserted-by":"publisher","first-page":"50","DOI":"10.14778\/3421424.3421431","volume":"14","author":"Y Li","year":"2020","unstructured":"Li Y, Li J, Suhara Y et al (2020) Deep entity matching with pre-trained language models. Proc VLDB Endow 14(1):50\u201360","journal-title":"Proc VLDB Endow"},{"issue":"1\u20132","key":"1018_CR39","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/0020-0255(95)00185-9","volume":"89","author":"EP Lim","year":"1996","unstructured":"Lim EP, Srivastava J, Prabhakar S et al (1996) Entity identification in database integration. Inform Sci 89(1\u20132):1\u201338","journal-title":"Inform Sci"},{"key":"1018_CR40","unstructured":"Liu Y, Ott M, Goyal N, et\u00a0al (2019) Roberta: A robustly optimized bert pretraining approach. arXiv"},{"key":"1018_CR41","volume-title":"Master data management","author":"D Loshin","year":"2010","unstructured":"Loshin D (2010) Master data management. Morgan Kaufmann, Massachusetts"},{"issue":"7","key":"1018_CR42","doi-asserted-by":"publisher","first-page":"2533","DOI":"10.1007\/s00521-018-3937-8","volume":"32","author":"S Malakar","year":"2020","unstructured":"Malakar S, Ghosh M, Bhowmik S et al (2020) A ga based hierarchical feature selection approach for handwritten word recognition. Neural Comput Appl 32(7):2533\u20132552","journal-title":"Neural Comput Appl"},{"key":"1018_CR43","unstructured":"Mallasto A, Feragen A (2017) Learning from uncertain curves: The 2-wasserstein metric for gaussian processes. Advances in Neural Information Processing Systems 30"},{"key":"1018_CR44","doi-asserted-by":"crossref","unstructured":"Marcus A, Wu E, Karger D, et\u00a0al (2011) Human-powered sorts and joins. arXiv","DOI":"10.14778\/2047485.2047487"},{"key":"1018_CR45","doi-asserted-by":"crossref","unstructured":"Maskat R, Paton NW, Embury SM (2016) Pay-as-you-go configuration of entity resolution. In: Proceedings of the Transactions on Large-Scale Data-and Knowledge-Centered Systems XXIX. Springer Berlin Heidelberg, pp 40\u201365","DOI":"10.1007\/978-3-662-54037-4_2"},{"key":"1018_CR46","unstructured":"Mescheder L, Nowozin S, Geiger A (2017) Adversarial variational bayes: Unifying variational autoencoders and generative adversarial networks. In: Proceedings of the International Conference on Machine Learning, PMLR, pp 2391\u20132400"},{"key":"1018_CR47","doi-asserted-by":"crossref","unstructured":"Miao Z, Li Y, Wang X, et\u00a0al (2020) Snippext: Semi-supervised opinion mining with augmented data. In: Proceedings of the Web Conference 2020. Association for Computing Machinery, pp 617\u2013628","DOI":"10.1145\/3366423.3380144"},{"key":"1018_CR48","doi-asserted-by":"crossref","unstructured":"Mudgal S, Li H, Rekatsinas T, et\u00a0al (2018) Deep learning for entity matching: A design space exploration. In: Proceedings of the 2018 International Conference on Management of Data. Association for Computing Machinery, pp 19\u201334","DOI":"10.1145\/3183713.3196926"},{"key":"1018_CR49","unstructured":"O\u2019Shea K, Nash R (2015) An introduction to convolutional neural networks. arXiv"},{"key":"1018_CR50","unstructured":"Pixton B, Giraud-Carrier C (2006) Using structured neural networks for record linkage. In: Proceedings of the Sixth Annual Workshop on Technology for Family History and Genealogical Research"},{"key":"1018_CR51","unstructured":"Rasmussen C (1999) The infinite gaussian mixture model. In: Proceedings of the Advances in Neural Information Processing Systems, vol\u00a012. MIT Press"},{"key":"1018_CR52","unstructured":"Sander ME, Ablin P, Blondel M, et\u00a0al (2021) Momentum residual neural networks. In: Proceedings of the International Conference on Machine Learning. PMLR, pp 9276\u20139287"},{"key":"1018_CR53","unstructured":"Sanh V, Debut L, Chaumond J, et\u00a0al (2019) Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter. arXiv"},{"key":"1018_CR54","doi-asserted-by":"crossref","unstructured":"Sarawagi S, Bhamidipaty A (2002) Interactive deduplication using active learning. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, pp 269\u2013278","DOI":"10.1145\/775047.775087"},{"key":"1018_CR55","unstructured":"SAS (1996) Banking Analytics. http:\/\/www.sas.com\/industry\/fsi\/fraud\/"},{"key":"1018_CR56","unstructured":"Scannapieco M (2006) Data quality, In Concepts Methodologies and Techniques. Data-Centric Systems and Applications, Springer"},{"key":"1018_CR57","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv"},{"key":"1018_CR58","unstructured":"Verma V, Lamb A, Beckham C, et\u00a0al (2019) Manifold mixup: Better representations by interpolating hidden states. In: Proceedings of the International Conference on Machine Learning, PMLR, pp 6438\u20136447"},{"key":"1018_CR59","doi-asserted-by":"crossref","unstructured":"Wang J, Kraska T, Franklin MJ, et\u00a0al (2012) Crowder: Crowdsourcing entity resolution. arXiv","DOI":"10.14778\/2350229.2350263"},{"key":"1018_CR60","doi-asserted-by":"crossref","unstructured":"Wei J, Zou K (2019) Eda: Easy data augmentation techniques for boosting performance on text classification tasks. arXiv","DOI":"10.18653\/v1\/D19-1670"},{"key":"1018_CR61","doi-asserted-by":"crossref","unstructured":"Wu R, Chaba S, Sawlani S, et\u00a0al (2020) Zeroer: Entity resolution using zero labeled examples. In: Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data. Association for Computing Machinery, pp 1149\u20131164","DOI":"10.1145\/3318464.3389743"},{"key":"1018_CR62","first-page":"1","volume":"71","author":"H Xing","year":"2022","unstructured":"Xing H, Xiao Z, Qu R et al (2022) An efficient federated distillation learning system for multitask time series classification. IEEE Trans Instrum Meas 71:1\u201312","journal-title":"IEEE Trans Instrum Meas"},{"key":"1018_CR63","doi-asserted-by":"crossref","unstructured":"Yang Y, Hu Y, Zhang X et al (2021) Two-stage selective ensemble of cnn via deep tree training for medical image classification. IEEE Trans Cybern 52(9):9194\u20139207","DOI":"10.1109\/TCYB.2021.3061147"},{"key":"1018_CR64","doi-asserted-by":"crossref","unstructured":"Yun S, Han D, Oh SJ, et\u00a0al (2019) Cutmix: Regularization strategy to train strong classifiers with localizable features. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. IEEE, pp 6023\u20136032","DOI":"10.1109\/ICCV.2019.00612"},{"key":"1018_CR65","doi-asserted-by":"crossref","unstructured":"Zhang H, Cisse M, Dauphin YN, et\u00a0al (2017) Mixup: Beyond empirical risk minimization. arXiv","DOI":"10.1007\/978-1-4899-7687-1_79"}],"container-title":["Complex &amp; Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-023-01018-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40747-023-01018-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-023-01018-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,10]],"date-time":"2024-02-10T22:08:18Z","timestamp":1707602898000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40747-023-01018-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,14]]},"references-count":65,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,2]]}},"alternative-id":["1018"],"URL":"https:\/\/doi.org\/10.1007\/s40747-023-01018-2","relation":{},"ISSN":["2199-4536","2198-6053"],"issn-type":[{"type":"print","value":"2199-4536"},{"type":"electronic","value":"2198-6053"}],"subject":[],"published":{"date-parts":[[2023,3,14]]},"assertion":[{"value":"12 October 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 February 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 March 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 declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}