{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T06:16:34Z","timestamp":1783318594313,"version":"3.54.6"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T00:00:00Z","timestamp":1777420800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T00:00:00Z","timestamp":1777420800000},"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":"crossref","award":["92367111"],"award-info":[{"award-number":["92367111"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["22473007"],"award-info":[{"award-number":["22473007"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1007\/s13042-026-03120-6","type":"journal-article","created":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T06:39:03Z","timestamp":1777444743000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Hypergraph Few-shot Learning and Its Application to Bearing Fault Detection"],"prefix":"10.1007","volume":"17","author":[{"given":"Zhuolun","family":"Tan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lina","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huaqing","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongwei","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,4,29]]},"reference":[{"issue":"4","key":"3120_CR1","first-page":"878","volume":"18","author":"J Wang","year":"2024","unstructured":"Wang J, Xu Z, Liu W (2024) A review of research on intelligent monitoring and fault diagnosis mechanisms for rolling bearing health. Computer Science and Exploration 18(4):878\u2013898","journal-title":"Computer Science and Exploration"},{"issue":"1","key":"3120_CR2","doi-asserted-by":"publisher","first-page":"55","DOI":"10.16578\/j.issn.1004.2539.2010.01.011","volume":"34","author":"J Xu","year":"2010","unstructured":"Xu J, Tao X, Zhang H (2010) Bearing fault detection method based on signal singularity analysis. Mechanical Transmission 34(1):55\u201359. https:\/\/doi.org\/10.16578\/j.issn.1004.2539.2010.01.011","journal-title":"Mechanical Transmission"},{"key":"3120_CR3","volume-title":"The Fourier Transform and Its Applications","author":"RN Bracewell","year":"1986","unstructured":"Bracewell RN (1986) The Fourier Transform and Its Applications. McGraw-Hill, New York, NY, USA"},{"issue":"4","key":"3120_CR4","doi-asserted-by":"publisher","first-page":"966","DOI":"10.1016\/j.ymssp.2004.09.001","volume":"20","author":"X Fan","year":"2006","unstructured":"Fan X, Zuo MJ (2006) Gearbox fault detection using Hilbert and wavelet packet transform. Mech Syst Signal Process 20(4):966\u2013982. https:\/\/doi.org\/10.1016\/j.ymssp.2004.09.001","journal-title":"Mech Syst Signal Process"},{"issue":"1","key":"3120_CR5","doi-asserted-by":"publisher","first-page":"10","DOI":"10.13195\/j.kzyjc.2011.01.002","volume":"26","author":"H Li","year":"2011","unstructured":"Li H, Xiao D (2011) Overview of data-driven fault diagnosis methods. Control and Decision 26(1):10\u201315. https:\/\/doi.org\/10.13195\/j.kzyjc.2011.01.002","journal-title":"Control and Decision"},{"issue":"16","key":"3120_CR6","doi-asserted-by":"publisher","first-page":"3999","DOI":"10.1109\/TSP.2013.2265222","volume":"61","author":"J Gilles","year":"2013","unstructured":"Gilles J (2013) Empirical wavelet transform. IEEE Trans Signal Process 61(16):3999\u20134010. https:\/\/doi.org\/10.1109\/TSP.2013.2265222","journal-title":"IEEE Trans Signal Process"},{"issue":"17","key":"3120_CR7","doi-asserted-by":"publisher","first-page":"5618","DOI":"10.1109\/JSEN.2017.2726018","volume":"17","author":"BR Nayana","year":"2017","unstructured":"Nayana BR, Geethanjali P (2017) Analysis of statistical time-domain features effectiveness in identification of bearing faults from vibration signal. IEEE Sens J 17(17):5618\u20135625. https:\/\/doi.org\/10.1109\/JSEN.2017.2726018","journal-title":"IEEE Sens J"},{"key":"3120_CR8","first-page":"34","volume":"12","author":"G Cheng","year":"2005","unstructured":"Cheng G (2005) Application of time domain indicators in rolling bearing fault diagnosis. China Equipment Engineering 12:34\u201335","journal-title":"China Equipment Engineering"},{"issue":"4","key":"3120_CR9","doi-asserted-by":"publisher","first-page":"3740","DOI":"10.1109\/TPEL.2018.2865512","volume":"34","author":"N Wassinger","year":"2018","unstructured":"Wassinger N, Penovi E et al (2018) Open-circuit fault identification method for interleaved converters based on time-domain analysis of the state observer residual. IEEE Trans Power Electron 34(4):3740\u20133749. https:\/\/doi.org\/10.1109\/TPEL.2018.2865512","journal-title":"IEEE Trans Power Electron"},{"key":"3120_CR10","doi-asserted-by":"publisher","first-page":"110556","DOI":"10.1016\/j.ress.2024.110556","volume":"253","author":"K You","year":"2025","unstructured":"You K et al (2025) A sound-vibration physical-information fusion constraint-guided deep learning method for rolling bearing fault diagnosis. Reliab Eng Syst Saf 253:110556. https:\/\/doi.org\/10.1016\/j.ress.2024.110556","journal-title":"Reliab Eng Syst Saf"},{"key":"3120_CR11","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6501\/acfc1a","author":"K You","year":"2024","unstructured":"You K, Qiu G, Gu Y (2024) Remaining useful life prediction of lithium-ion batteries using EM-PF-SSA-SVR with gamma stochastic process. Meas Sci Technol. https:\/\/doi.org\/10.1088\/1361-6501\/acfc1a","journal-title":"Meas Sci Technol"},{"key":"3120_CR12","doi-asserted-by":"publisher","unstructured":"Jin G (2020) Research on end-to-end rolling bearing fault diagnosis algorithm based on deep learning for complex operating conditions, Ph.D. dissertation, Univ. Sci. Technol. China, Hefei, China. https:\/\/doi.org\/10.27517\/d.cnki.gzkju.2020.001723.","DOI":"10.27517\/d.cnki.gzkju.2020.001723."},{"key":"3120_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2021\/6638783","volume":"6638783","author":"S Han","year":"2021","unstructured":"Han S, Oh S, Jeong J (2021) Bearing fault diagnosis based on multi-scale convolutional neural network using data augmentation. Journal of Sensors 6638783:1\u201314. https:\/\/doi.org\/10.1155\/2021\/6638783","journal-title":"Journal of Sensors"},{"key":"3120_CR14","doi-asserted-by":"publisher","first-page":"106427","DOI":"10.1016\/j.cie.2020.106427","volume":"143","author":"C Che","year":"2020","unstructured":"Che C, Wang H, Ni X et al (2020) Domain adaptive deep belief network for rolling bearing fault diagnosis. Comput Ind Eng 143:106427. https:\/\/doi.org\/10.1016\/j.cie.2020.106427","journal-title":"Comput Ind Eng"},{"key":"3120_CR15","first-page":"38","volume":"9","author":"N Wu","year":"2021","unstructured":"Wu N, Wang Z (2021) Bearing fault diagnosis based on the combination of one-dimensional CNN and Bi-LSTM. Modular Machine Tool and Automatic Manufacturing Technique 9:38\u201341","journal-title":"Modular Machine Tool and Automatic Manufacturing Technique"},{"key":"3120_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2020.107233","volume":"150","author":"W Mao","year":"2021","unstructured":"Mao W, Feng W, Liu Y (2021) A new deep auto-encoder method with fusing discriminant information for bearing fault diagnosis. Mech Syst Signal Process 150:107233. https:\/\/doi.org\/10.1016\/j.ymssp.2020.107233","journal-title":"Mech Syst Signal Process"},{"key":"3120_CR17","first-page":"68","volume":"11","author":"Q Liu","year":"2022","unstructured":"Liu Q, Wei P (2022) Fault diagnosis for rolling bearings based on conditional residual generative adversarial networks of self-attention mechanism. Bearing 11:68\u201375","journal-title":"Bearing"},{"key":"3120_CR18","doi-asserted-by":"publisher","unstructured":"Mao W, Shi H, Zhang Y et al (2023) Research on unsupervised tensor-based deep transfer learning for online early fault detection of bearing, Control and Decision, early access. [Online]. Available: https:\/\/doi.org\/10.13195\/j.kzyjc.2022.1101","DOI":"10.13195\/j.kzyjc.2022.1101"},{"issue":"10","key":"3120_CR19","first-page":"1164","volume":"32","author":"Q He","year":"2021","unstructured":"He Q, Tang X, Li C et al (2021) Bearing fault diagnosis with few-shot data under unbalanced load. China Mechanical Engineering 32(10):1164\u20131171","journal-title":"China Mechanical Engineering"},{"key":"3120_CR20","doi-asserted-by":"publisher","DOI":"10.1177\/09544062221087654","author":"H Wang","year":"2022","unstructured":"Wang H, Tong X, Wang P, Xu Z, Song L (2022) Few-shot transfer learning method based on meta-learning and graph convolution network for machinery fault diagnosis. Mechanical Engineering Science. https:\/\/doi.org\/10.1177\/09544062221087654","journal-title":"Mechanical Engineering Science"},{"issue":"2","key":"3120_CR21","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1109\/LCOMM.2021.3128339","volume":"26","author":"Y Wang","year":"2022","unstructured":"Wang Y, Gui G, Lin Y (2022) Specific emitter identification with limited samples: A model-agnostic meta-learning approach. IEEE Commun Lett 26(2):345\u2013349. https:\/\/doi.org\/10.1109\/LCOMM.2021.3128339","journal-title":"IEEE Commun Lett"},{"issue":"6","key":"3120_CR22","doi-asserted-by":"publisher","first-page":"3894","DOI":"10.1109\/TII.2021.3116143","volume":"18","author":"Y Hu","year":"2021","unstructured":"Hu Y, Liu R, Li X et al (2021) Task-sequencing meta learning for intelligent few-shot fault diagnosis with limited data. IEEE Trans Industr Inf 18(6):3894\u20133904. https:\/\/doi.org\/10.1109\/TII.2021.3116143","journal-title":"IEEE Trans Industr Inf"},{"key":"3120_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2023.109522","volume":"239","author":"S Yan","year":"2023","unstructured":"Yan S, Zhong X, Shao H, Ming Y, Liu C, Liu B (2023) Digital twin-assisted imbalanced fault diagnosis framework using subdomain adaptive mechanism and margin-aware regularization. Reliab Eng Syst Saf 239:109522. https:\/\/doi.org\/10.1016\/j.ress.2023.109522","journal-title":"Reliab Eng Syst Saf"},{"key":"3120_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2025.128592","volume":"292","author":"K You","year":"2025","unstructured":"You K, Liu C, Lin Y, Qiu G, Gu Y (2025) DTMPI-DIVR: A digital twins for multi-margin physical information via dynamic interaction of virtual and real sound-vibration signals for bearing fault diagnosis without real fault samples. Expert Syst Appl 292:128592","journal-title":"Expert Syst Appl"},{"issue":"8","key":"3120_CR25","first-page":"2161","volume":"41","author":"L Tian","year":"2021","unstructured":"Tian L, Zhang J, Zhang J et al (2021) Overview of knowledge graph: Representation, construction, reasoning, and knowledge hypergraph theory. Computer Applications 41(8):2161\u20132186","journal-title":"Computer Applications"},{"issue":"2","key":"3120_CR26","doi-asserted-by":"publisher","first-page":"498","DOI":"10.13328\/j.cnki.jos.006353","volume":"33","author":"B Hu","year":"2022","unstructured":"Hu B, Wang X, Wang X et al (2022) Overview of hypergraph learning: Algorithm classification and application analysis. Journal of Software 33(2):498\u2013523. https:\/\/doi.org\/10.13328\/j.cnki.jos.006353","journal-title":"Journal of Software"},{"issue":"2","key":"3120_CR27","doi-asserted-by":"publisher","first-page":"93","DOI":"10.13952\/j.cnki.jofmdr.2017.0115","volume":"33","author":"J Yuan","year":"2017","unstructured":"Yuan J, Han T, Tang J et al (2017) Intelligent fault diagnosis method for rolling bearings based on wavelet time-frequency graph and CNN. Mechanical Design and Research 33(2):93\u201397. https:\/\/doi.org\/10.13952\/j.cnki.jofmdr.2017.0115","journal-title":"Mechanical Design and Research"},{"issue":"9","key":"3120_CR28","doi-asserted-by":"publisher","first-page":"4290","DOI":"10.1109\/TIP.2012.2199502","volume":"21","author":"Y Gao","year":"2012","unstructured":"Gao Y, Wang M, Tao D et al (2012) 3D object retrieval and recognition with hypergraph analysis. IEEE Trans Image Process 21(9):4290\u20134303. https:\/\/doi.org\/10.1109\/TIP.2012.2199502","journal-title":"IEEE Trans Image Process"},{"issue":"12","key":"3120_CR29","doi-asserted-by":"publisher","first-page":"95","DOI":"10.3901\/JME.2000.12.095","volume":"36","author":"J Lin","year":"2000","unstructured":"Lin J, Qu L (2000) Signal detection technology and fault diagnosis based on continuous wavelet transform. Journal of Mechanical Engineering 36(12):95\u2013100","journal-title":"Journal of Mechanical Engineering"},{"key":"3120_CR30","unstructured":"Chen W, Liu Y, Kira Z et al (2019) A closer look at few-shot classification, in Proc. Int. Conf. Learn. Represent. (ICLR), New Orleans, LA, USA. [Online]. Available: arXiv:1904.04232"},{"key":"3120_CR31","doi-asserted-by":"publisher","unstructured":"You K, Gu Y, Liu Y, Wang Y (2025) A novel physical constraint-guided quadratic neural networks for interpretable bearing fault diagnosis under zero-fault sample, Nondestructive Testing and Evaluation, pp. 1\u201331, https:\/\/doi.org\/10.1080\/10589759.2025.2534429.","DOI":"10.1080\/10589759.2025.2534429."},{"key":"3120_CR32","doi-asserted-by":"publisher","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition, in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Las Vegas, NV, USA, pp. 770\u2013778, https:\/\/doi.org\/10.1109\/CVPR.2016.90.","DOI":"10.1109\/CVPR.2016.90."},{"key":"3120_CR33","first-page":"4077","volume":"30","author":"J Snell","year":"2017","unstructured":"Snell J, Swersky K, Zemel RS (2017) Prototypical networks for few-shot learning. Adv Neural Inf Process Syst (NeurIPS) 30:4077\u20134087","journal-title":"Adv Neural Inf Process Syst (NeurIPS)"},{"key":"3120_CR34","doi-asserted-by":"publisher","unstructured":"Sung F, Yang Y, Zhang L et al (2018) Learning to compare: Relation network for few-shot learning, in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Salt Lake City, UT, USA, pp. 1199\u20131208, https:\/\/doi.org\/10.1109\/CVPR.2018.00131.","DOI":"10.1109\/CVPR.2018.00131."},{"issue":"3","key":"3120_CR35","doi-asserted-by":"publisher","first-page":"6","DOI":"10.3969\/j.issn.1000-8055.2006.03.025","volume":"21","author":"J Cheng","year":"2006","unstructured":"Cheng J, Yu D, Yang Y (2006) Fault diagnosis method for rolling bearings based on EMD and SVM. Journal of Aerodynamics 21(3):6. https:\/\/doi.org\/10.3969\/j.issn.1000-8055.2006.03.025","journal-title":"Journal of Aerodynamics"},{"key":"3120_CR36","doi-asserted-by":"publisher","unstructured":"Li W, Wang L, Xu J et al (2019) Revisiting local descriptor based image-to-class measure for few-shot learning, in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Long Beach, CA, USA, pp. 7263\u20137272, https:\/\/doi.org\/10.1109\/CVPR.2019.00744.","DOI":"10.1109\/CVPR.2019.00744."},{"key":"3120_CR37","unstructured":"Vinyals O et al (2016) Matching networks for one shot learning, in Adv. Neural Inf. Process. Syst. (NeurIPS)"},{"key":"3120_CR38","unstructured":"Simon C, Koniusz P, Nock R, Harandi M (2020) A dual-space framework for sub-structure-aware few-shot learning, in Proc. Eur. Conf. Comput. Vis. (ECCV)"},{"issue":"4","key":"3120_CR39","doi-asserted-by":"publisher","first-page":"6656","DOI":"10.1609\/aaai.v34i04.6134","volume":"34","author":"H Yao","year":"2020","unstructured":"Yao H, Zhang S, Guan H et al (2020) Graph few-shot learning via knowledge transfer. Proc AAAI Conf Artif Intell 34(4):6656\u20136663. https:\/\/doi.org\/10.1609\/aaai.v34i04.6134","journal-title":"Proc AAAI Conf Artif Intell"},{"issue":"7","key":"3120_CR40","doi-asserted-by":"publisher","first-page":"3509","DOI":"10.1109\/TNNLS.2022.3148123","volume":"34","author":"Y Zhang","year":"2023","unstructured":"Zhang Y et al (2023) Meta-learning with adaptive hyperparameters for few-shot classification. IEEE Transactions on Neural Networks and Learning Systems 34(7):3509\u20133520. https:\/\/doi.org\/10.1109\/TNNLS.2022.3148123","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"3120_CR41","doi-asserted-by":"publisher","DOI":"10.19713\/j.cnki.43-1423\/u.T2023110","author":"F Xie","year":"2024","unstructured":"Xie F, Wang L, Song M et al (2024) Fault diagnosis of multi-source variable working condition rolling bearings based on GCN. Journal of Railway Science and Engineering. https:\/\/doi.org\/10.19713\/j.cnki.43-1423\/u.T2023110","journal-title":"Journal of Railway Science and Engineering"},{"key":"3120_CR42","doi-asserted-by":"crossref","unstructured":"Tian Y, Wang Y, Krishnan D, Tenenbaum JB, Isola P (2020) Rethinking few-shot image classification: A good baseline, in Proc. IEEE\/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), 2020","DOI":"10.1007\/978-3-030-58568-6_16"},{"key":"3120_CR43","unstructured":"Hou R, Chang H, Ma B, Shan S, Chen X (2019) Cross-attention network for few-shot classification, in Adv. Neural Inf. Process. Syst. (NeurIPS)"},{"issue":"9","key":"3120_CR44","doi-asserted-by":"publisher","first-page":"10234","DOI":"10.1609\/aaai.v37i9.26245","volume":"37","author":"X Wang","year":"2023","unstructured":"Wang X et al (2023) Meta-UAFS: Uncertainty-aware few-shot learning via meta-learning. Proc AAAI Conf Artif Intell 37(9):10234\u201310242. https:\/\/doi.org\/10.1609\/aaai.v37i9.26245","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"3120_CR45","doi-asserted-by":"publisher","unstructured":"Wang Y, Xu C, Liu C, Zhang L, Fu Y (2020) Instance credibility inference for few-shot learning, in Proc. IEEE\/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), Seattle, WA, USA, pp. 12836\u201312845, https:\/\/doi.org\/10.1109\/CVPR42600.2020.01285.","DOI":"10.1109\/CVPR42600.2020.01285."},{"issue":"1","key":"3120_CR46","doi-asserted-by":"publisher","first-page":"31","DOI":"10.3390\/machines12010044","volume":"12","author":"SZ Hejazi","year":"2024","unstructured":"Hejazi SZ, Packianather M, Liu Y (2024) A novel customised load adaptive framework for induction motor fault classification utilising MFPT bearing dataset. Machines 12(1):31. https:\/\/doi.org\/10.3390\/machines12010044","journal-title":"Machines"},{"issue":"7","key":"3120_CR47","doi-asserted-by":"publisher","first-page":"1761","DOI":"10.1109\/TPAMI.2018.2858242","volume":"41","author":"R He","year":"2019","unstructured":"He R et al (2019) Wasserstein CNN: Learning invariant features for NIR-VIS face recognition. IEEE Trans Pattern Anal Mach Intell 41(7):1761\u20131773. https:\/\/doi.org\/10.1109\/TPAMI.2018.2858242","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"3120_CR48","volume":"2021","author":"Z Zhao","year":"2021","unstructured":"Zhao Z, Wu F et al (2021) A multi-kernel convolutional neural network for bearing fault diagnosis. Measurement 2021:109845","journal-title":"Measurement"},{"key":"3120_CR49","volume-title":"SincNet: Learning Sinc-based filters for speech processing, in Proc","author":"M Ravanelli","year":"2018","unstructured":"Ravanelli M, Bengio Y (2018) SincNet: Learning Sinc-based filters for speech processing, in Proc. IEEE Spoken Lang. Technol, Workshop (SLT)"},{"key":"3120_CR50","doi-asserted-by":"crossref","unstructured":"Li H, Xiong P, Fan H et al (2019) DFANet: Deep feature aggregation for real-time semantic segmentation, in Proc. IEEE\/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), arXiv:1904.02216","DOI":"10.1109\/CVPR.2019.00975"},{"key":"3120_CR51","doi-asserted-by":"crossref","unstructured":"Tian Z, Shen C, Chen H et al (2019) FCOS: Fully convolutional one-stage object detection, in Proc. IEEE\/CVF Int. Conf. Comput. Vis. (ICCV), pp. 9627\u20139636","DOI":"10.1109\/ICCV.2019.00972"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-026-03120-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-026-03120-6","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-026-03120-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T05:56:54Z","timestamp":1783317414000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-026-03120-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,29]]},"references-count":51,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2026,6]]}},"alternative-id":["3120"],"URL":"https:\/\/doi.org\/10.1007\/s13042-026-03120-6","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,29]]},"assertion":[{"value":"12 August 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 April 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 April 2026","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 no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"286"}}