{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T07:25:38Z","timestamp":1740122738419,"version":"3.37.3"},"reference-count":53,"publisher":"Springer Science and Business Media LLC","issue":"22","license":[{"start":{"date-parts":[[2023,9,18]],"date-time":"2023-09-18T00:00:00Z","timestamp":1694995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,9,18]],"date-time":"2023-09-18T00:00:00Z","timestamp":1694995200000},"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":["Appl Intell"],"published-print":{"date-parts":[[2023,11]]},"DOI":"10.1007\/s10489-023-04965-y","type":"journal-article","created":{"date-parts":[[2023,9,18]],"date-time":"2023-09-18T06:02:29Z","timestamp":1695016949000},"page":"27808-27825","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Discover unknown fault categories through active query evidence model"],"prefix":"10.1007","volume":"53","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5809-5327","authenticated-orcid":false,"given":"Min","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoyu","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ting","family":"Wen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nengji","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,9,18]]},"reference":[{"key":"4965_CR1","doi-asserted-by":"crossref","unstructured":"Li, S, Cui, Z, Ye, X, Feng, J, Yang, Y, He, Z (2020). Chip-Based Microwave-Photonic Radar for High-Resolution Imaging. Laser & Photonics Reviews, 14(10). http:\/\/ides.nuaa.edu.cn","DOI":"10.1002\/lpor.201900239"},{"key":"4965_CR2","doi-asserted-by":"publisher","unstructured":"Asad B, Vaimann T, Belahcen A, Kallaste A, Rass\u00f5lkin A, Iqbal MN (2020) The cluster computation-based hybrid fem-analytical model of induction motor for fault diagnostics. Applied Sciences 10(21). https:\/\/doi.org\/10.3390\/app10217572. https:\/\/www.mdpi.com\/2076-3417\/10\/21\/7572","DOI":"10.3390\/app10217572"},{"key":"4965_CR3","doi-asserted-by":"crossref","unstructured":"Bendale A, Boult TE (2016) Towards open set deep networks. In: Proc IEEE Conf Comput Vis Pattern Recognit (CVPR), pp 1563\u20131572. IEEE Computer Society","DOI":"10.1109\/CVPR.2016.173"},{"key":"4965_CR4","doi-asserted-by":"publisher","unstructured":"Bosman AS, Engelbrecht A, Helbig M (2020) Visualising basins of attraction for the cross-entropy and the squared error neural network loss functions. Neurocomputing 400:113\u2013136. https:\/\/doi.org\/10.1016\/j.neucom.2020.02.113. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0925231220303593","DOI":"10.1016\/j.neucom.2020.02.113"},{"issue":"3","key":"4965_CR5","doi-asserted-by":"publisher","first-page":"1981","DOI":"10.1007\/s00521-021-06534-1","volume":"34","author":"G Boztas","year":"2022","unstructured":"Boztas G, Tuncer T (2022) A fault classification method using dynamic centered one-dimensional local angular binary pattern for a pmsm and drive system. Neural Comput Appl 34(3):1981\u20131992. https:\/\/doi.org\/10.1007\/s00521-021-06534-1","journal-title":"Neural Comput Appl"},{"key":"4965_CR6","unstructured":"Chang N, Yu ZD, Wang YX, Anandkumar A, Fidler S, Alvarez JM (2021) Image-level or object-level? A tale of two resampling strategies for long-tailed detection. In: Proc Int Conf Mach Learn (ICML), pp 1463\u20131472"},{"issue":"8","key":"4965_CR7","doi-asserted-by":"publisher","first-page":"1734","DOI":"10.1109\/LCOMM.2020.2991449","volume":"24","author":"M Chen","year":"2020","unstructured":"Chen M, Zhu K, Wang R, Niyato D (2020) Active learning-based fault diagnosis in self-organizing cellular networks. IEEE Commun Lett 24(8):1734\u20131737. https:\/\/doi.org\/10.1109\/LCOMM.2020.2991449","journal-title":"IEEE Commun Lett"},{"key":"4965_CR8","doi-asserted-by":"publisher","first-page":"28122","DOI":"10.1109\/ACCESS.2021.3058387","volume":"9","author":"DD Cheng","year":"2021","unstructured":"Cheng DD, Liu LJ, Yu Z (2021) Cnn-based intelligent fault-tolerant control design for turbofan engines with actuator faults. IEEE Access 9:28122\u201328139","journal-title":"IEEE Access"},{"key":"4965_CR9","doi-asserted-by":"crossref","unstructured":"Chu P, Bian X, Liu SP, Ling HB (2020) Feature space augmentation for long-tailed data. In: Proc Eur Conf Comput Vis (ECCV), pp 694\u2013710","DOI":"10.1007\/978-3-030-58526-6_41"},{"issue":"16","key":"4965_CR10","doi-asserted-by":"publisher","first-page":"3456","DOI":"10.1177\/1077546314524260","volume":"21","author":"LF De Almeida","year":"2015","unstructured":"De Almeida LF, Bizarria JW, Bizarria FC, Mathias MH (2015) Condition-based monitoring system for rolling element bearing using a generic multi-layer perceptron. J Vib Control 21(16):3456\u20133464","journal-title":"J Vib Control"},{"issue":"2","key":"4965_CR11","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1214\/aoms\/1177698950","volume":"38","author":"AP Dempster","year":"1967","unstructured":"Dempster AP (1967) Upper and lower probabilities induced by a multivalued mapping. Annals Math Stat 38(2):325\u2013339","journal-title":"Annals Math Stat"},{"issue":"1\u20132","key":"4965_CR12","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/j.ymssp.2013.09.003","volume":"43","author":"WL Du","year":"2014","unstructured":"Du WL, Tao JF, Li YM, Liu CL (2014) Wavelet leaders multifractal features based fault diagnosis of rotating mechanism. Mech Syst Signal Proc 43(1\u20132):57\u201375","journal-title":"Mech Syst Signal Proc"},{"key":"4965_CR13","unstructured":"Gal Y, Ghahramani Z (2016) Bayesian convolutional neural networks with bernoulli approximate variational inference. Proc Int Conf Learn Represent (ICLR)"},{"issue":"01","key":"4965_CR14","doi-asserted-by":"publisher","first-page":"1850013","DOI":"10.1142\/S0218126618500135","volume":"27","author":"ZY Han","year":"2018","unstructured":"Han ZY, Wang J (2018) A fault diagnosis method based on active example selection. J Circuits Syst Comput 27(01):1850013","journal-title":"J Circuits Syst Comput"},{"key":"4965_CR15","doi-asserted-by":"crossref","unstructured":"He KM, Zhang XY, Ren SQ, Sun J (2016) Deep residual learning for image recognition. In: Proc. IEEE Conf Comput Vis Pattern Recognit (CVPR), pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"issue":"4","key":"4965_CR16","doi-asserted-by":"publisher","first-page":"1493","DOI":"10.1109\/TIM.2019.2915404","volume":"69","author":"Y He","year":"2019","unstructured":"He Y, Song KC, Meng QG, Yan YH (2019) An end-to-end steel surface defect detection approach via fusing multiple hierarchical features. IEEE Trans Instrum Meas 69(4):1493\u20131504","journal-title":"IEEE Trans Instrum Meas"},{"issue":"5","key":"4965_CR17","doi-asserted-by":"publisher","first-page":"867","DOI":"10.1016\/j.cja.2017.11.016","volume":"31","author":"W Hong","year":"2018","unstructured":"Hong W, Cai WJ, Wang S, Mileta MT (2018) Mechanical wear debris feature, detection, and diagnosis: A review. Chin J Aeronaut 31(5):867\u2013882","journal-title":"Chin J Aeronaut"},{"key":"4965_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2021.104365","volume":"104","author":"CX Jian","year":"2021","unstructured":"Jian CX, Yang KJ, Ao YH (2021) Industrial fault diagnosis based on active learning and semi-supervised learning using small training set. Eng Appl Artif Intell 104:104365. https:\/\/doi.org\/10.1016\/j.engappai.2021.104365","journal-title":"Eng Appl Artif Intell"},{"key":"4965_CR19","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-42337-1","volume-title":"Subjective Logic: A formalism for reasoning under uncertainty","author":"A J\u00f8sang","year":"2016","unstructured":"J\u00f8sang A (2016) Subjective Logic: A formalism for reasoning under uncertainty. Intell. Found. Theory Algorithms. Springer, Artif. https:\/\/doi.org\/10.1007\/978-3-319-42337-1"},{"key":"4965_CR20","doi-asserted-by":"publisher","unstructured":"Jun LW, Hui L, Sai G, Tao C (2020) Process fault diagnosis with model- and knowledge-based approaches: Advances and opportunities. Control Eng Pract 105:104637. https:\/\/doi.org\/10.1016\/j.conengprac.2020.104637. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0967066120302070","DOI":"10.1016\/j.conengprac.2020.104637"},{"issue":"5","key":"4965_CR21","doi-asserted-by":"publisher","first-page":"2045","DOI":"10.1109\/TCST.2020.2997648","volume":"28","author":"D Jung","year":"2020","unstructured":"Jung D (2020) Data-driven open-set fault classification of residual data using bayesian filtering. IEEE Trans Control Syst Technol 28(5):2045\u20132052","journal-title":"IEEE Trans Control Syst Technol"},{"key":"4965_CR22","unstructured":"Kingma DP, Salimans T, Welling M (2015) Variational dropout and the local reparameterization trick. Proc Neural Inf Process Syst (NIPS) 28"},{"key":"4965_CR23","doi-asserted-by":"publisher","unstructured":"Lao ZP, He DQ, Wei ZX, Hui S, Jin ZZ, Jian M, Hui RC (2023) Intelligent fault diagnosis for rail transit switch machine based on adaptive feature selection and improved lightgbm. Eng Failure Anal 148:107219. https:\/\/doi.org\/10.1016\/j.engfailanal.2023.107219. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1350630723001735","DOI":"10.1016\/j.engfailanal.2023.107219"},{"key":"4965_CR24","unstructured":"Lee K, Lee H, Lee K, Shin, J (2018) Training confidence-calibrated classifiers for detecting out-of-distribution samples. In: Proc Int Conf Learn Represent (ICLR)"},{"key":"4965_CR25","doi-asserted-by":"publisher","unstructured":"Liu XM, Ming XN (2019) A nonlinear grey forecasting model with double shape parameters and its application. Appl Math Comput 360:203\u2013212. https:\/\/doi.org\/10.1016\/j.amc.2019.05.012. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0096300319304059","DOI":"10.1016\/j.amc.2019.05.012"},{"key":"4965_CR26","doi-asserted-by":"publisher","first-page":"108806","DOI":"10.1016\/j.petrol.2021.108806","volume":"203","author":"XX Lv","year":"2021","unstructured":"Lv XX, Wang HX, Zhang X, Liu YX, Jiang D, Wei B (2021) An evolutional svm method based on incremental algorithm and simulated indicator diagrams for fault diagnosis in sucker rod pumping systems. J Pet Sci Eng 203:108806. https:\/\/doi.org\/10.1016\/j.petrol.2021.108806","journal-title":"J Pet Sci Eng"},{"key":"4965_CR27","unstructured":"Molchanov D, Ashukha A, Vetrov D (2017) Variational dropout sparsifies deep neural networks. In: Proc Int Conf Mach Learn (ICML), pp 2498\u20132507"},{"key":"4965_CR28","doi-asserted-by":"crossref","unstructured":"Mundt M, Pliushch I, Majumder S, Ramesh V (2019) Open set recognition through deep neural network uncertainty: does out-of-distribution detection require generative classifiers?. In: Proc IEEE\/CVF Int Conf Comput Vis (ICCV), pp 753\u2013757","DOI":"10.1109\/ICCVW.2019.00098"},{"key":"4965_CR29","doi-asserted-by":"crossref","unstructured":"Nielsen, F (2022) The kullback\u2013leibler divergence between lattice gaussian distributions. Journal of the Indian Institute of Science pp 1\u201312","DOI":"10.1007\/s41745-021-00279-5"},{"key":"4965_CR30","doi-asserted-by":"publisher","first-page":"115299","DOI":"10.1109\/ACCESS.2020.3004489","volume":"8","author":"N Riaz","year":"2020","unstructured":"Riaz N, Shah SIA, Rehman F, Gilani SO, Udin E (2020) A novel 2-d current signal-based residual learning with optimized softmax to identify faults in ball screw actuators. IEEE Access 8:115299\u2013115313. https:\/\/doi.org\/10.1109\/ACCESS.2020.3004489","journal-title":"IEEE Access"},{"key":"4965_CR31","doi-asserted-by":"crossref","unstructured":"Saenko K, Kulis B, Fritz M, Darrell T (2010) Adapting visual category models to new domains. In: ECCV, pp. 213\u2013226. Springer","DOI":"10.1007\/978-3-642-15561-1_16"},{"key":"4965_CR32","unstructured":"Sensoy M, Kaplan L, Kandemir M (2018) Evidential deep learning to quantify classification uncertainty. pp 3183\u20133193"},{"key":"4965_CR33","doi-asserted-by":"publisher","DOI":"10.1515\/9780691214696","volume-title":"A mathematical theory of evidence","author":"G Shafer","year":"1976","unstructured":"Shafer G (1976) A mathematical theory of evidence. Princeton University Press"},{"key":"4965_CR34","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1016\/j.ymssp.2015.04.021","volume":"64","author":"WA Smith","year":"2015","unstructured":"Smith WA, Randall RB (2015) Rolling element bearing diagnostics using the case western reserve university data: A benchmark study. Mech Syst Signal Proc 64:100\u2013131","journal-title":"Mech Syst Signal Proc"},{"key":"4965_CR35","doi-asserted-by":"publisher","first-page":"5117","DOI":"10.1609\/aaai.v33i01.33015117","volume":"33","author":"YP Tang","year":"2019","unstructured":"Tang YP, Huang SJ (2019) Self-paced active learning: Query the right thing at the right time. Proc. AAAI 33:5117\u20135124","journal-title":"Proc. AAAI"},{"key":"4965_CR36","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIM.2023.3238059","volume":"72","author":"JB Thomas","year":"2023","unstructured":"Thomas JB, Chaudhari SG, Shihabudheen KV, Verma NK (2023) Cnn-based transformer model for fault detection in power system networks. IEEE Trans Instrum Meas 72:1\u201310. https:\/\/doi.org\/10.1109\/TIM.2023.3238059","journal-title":"IEEE Trans Instrum Meas"},{"key":"4965_CR37","doi-asserted-by":"publisher","first-page":"194","DOI":"10.1016\/j.aei.2018.04.006","volume":"36","author":"Y Tian","year":"2018","unstructured":"Tian Y, Wang ZL, Zhang LP, Lu C, Ma J (2018) A subspace learning-based feature fusion and open-set fault diagnosis approach for machinery components. Adv Eng Inform 36:194\u2013206","journal-title":"Adv Eng Inform"},{"issue":"1","key":"4965_CR38","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1109\/TII.2015.2500098","volume":"12","author":"M Van","year":"2015","unstructured":"Van M, Kang HJ (2015) Bearing defect classification based on individual wavelet local fisher discriminant analysis with particle swarm optimization. IEEE Trans Ind Informat 12(1):124\u2013135","journal-title":"IEEE Trans Ind Informat"},{"key":"4965_CR39","doi-asserted-by":"publisher","first-page":"105140","DOI":"10.1016\/j.knosys.2019.105140","volume":"189","author":"M Wang","year":"2020","unstructured":"Wang M, Fu K, Min F, Jia XY (2020) Active learning through label error statistical methods. Knowl-Based Syst 189:105140","journal-title":"Knowl-Based Syst"},{"key":"4965_CR40","doi-asserted-by":"publisher","unstructured":"Wang M, Yang CY, Zhao F, Min F, Wang XZ (2022) Cost-sensitive active learning for incomplete data. IEEE Trans Syst Man Cybern -Syst, pp 1\u201312. https:\/\/doi.org\/10.1109\/TSMC.2022.3182122","DOI":"10.1109\/TSMC.2022.3182122"},{"key":"4965_CR41","doi-asserted-by":"publisher","unstructured":"Wang M, Zhou L, Li Q, Aa Zhang (2023) Open world long-tailed data classification through active distribution optimization. Exp Syst Appl 213:119054. https:\/\/doi.org\/10.1016\/j.eswa.2022.119054. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0957417422020723","DOI":"10.1016\/j.eswa.2022.119054"},{"key":"4965_CR42","doi-asserted-by":"publisher","unstructured":"Wang T, Zhang L, Wang X (2023) Fault detection for motor drive control system of industrial robots using cnn-lstm-based observers. CES Transactions on Electrical Machines and Systems pp. 1\u20139. https:\/\/doi.org\/10.30941\/CESTEMS.2023.00014","DOI":"10.30941\/CESTEMS.2023.00014"},{"key":"4965_CR43","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1016\/j.neucom.2018.05.024","volume":"310","author":"ZR Wang","year":"2018","unstructured":"Wang ZR, Wang J, Wang YR (2018) An intelligent diagnosis scheme based on generative\u00a0adversarial learning deep neural networks and its application to planetary gearbox fault pattern recognition. Neurocomputing 310:213\u2013222. https:\/\/doi.org\/10.1016\/j.neucom.2018.05.024","journal-title":"Neurocomputing"},{"key":"4965_CR44","doi-asserted-by":"publisher","unstructured":"Yan WH, Wang J, Lu S, Zhou M, Peng X (2023) A review of real-time fault diagnosis methods for industrial smart manufacturing. Processes 11(2). https:\/\/doi.org\/10.3390\/pr11020369. https:\/\/www.mdpi.com\/2227-9717\/11\/2\/369","DOI":"10.3390\/pr11020369"},{"issue":"15","key":"4965_CR45","doi-asserted-by":"publisher","first-page":"8336","DOI":"10.1109\/JSEN.2020.2976523","volume":"20","author":"J Yang","year":"2020","unstructured":"Yang J, Yang Y, Xie G (2020) Diagnosis of incipient fault based on sliding-scale resampling strategy and improved deep autoencoder. IEEE Sensors J 20(15):8336\u20138348. https:\/\/doi.org\/10.1109\/JSEN.2020.2976523","journal-title":"IEEE Sensors J"},{"key":"4965_CR46","doi-asserted-by":"crossref","unstructured":"Yu XL, Zhao ZB, Zhang XW, Sun C, Zhang QY, Chen XF (2020) Coupling deep models and extreme value theory for open set fault diagnosis. In: Int Conf Sens Meas Data Anal, pp 118\u2013123","DOI":"10.1109\/ICSMD50554.2020.9261657"},{"key":"4965_CR47","doi-asserted-by":"publisher","first-page":"49999","DOI":"10.1109\/ACCESS.2020.2977421","volume":"8","author":"C Zhang","year":"2020","unstructured":"Zhang C, Guo QX, Li Y (2020) Fault detection in the tennessee eastman benchmark process using principal component difference based on k-nearest neighbors. IEEE Access 8:49999\u201350009","journal-title":"IEEE Access"},{"key":"4965_CR48","doi-asserted-by":"crossref","unstructured":"Zhang C, Xu LQ, Li XW, Wang HY (2018) A method of fault diagnosis for rotary equipment based on deep learning. In: Prognostics System Health Manag. Conf, pp 958\u2013962","DOI":"10.1109\/PHM-Chongqing.2018.00171"},{"key":"4965_CR49","doi-asserted-by":"publisher","first-page":"439","DOI":"10.1016\/j.cja.2019.07.011","volume":"33","author":"JQ Zhang","year":"2020","unstructured":"Zhang JQ, Sun Y, Guo L, Gao HL, Hong X, Song HL (2020) A new bearing fault diagnosis method based on modified convolutional neural networks. Chin J Aeronaut 33:439\u2013447","journal-title":"Chin J Aeronaut"},{"key":"4965_CR50","doi-asserted-by":"publisher","unstructured":"Zhang TC, Chen JL, Li FD, Zhang KY, Lv HX, He SL, Xu EY (2022) Intelligent fault diagnosis of machines with small & imbalanced data: A state-of-the-art review and possible extensions. ISA Trans 119:152\u2013171. https:\/\/doi.org\/10.1016\/j.isatra.2021.02.042","DOI":"10.1016\/j.isatra.2021.02.042"},{"key":"4965_CR51","doi-asserted-by":"publisher","unstructured":"Zhao X, Xiao J, Yu S, Li H, Zhang B (2023) Weight-guided class complementing for long-tailed image recognition. Pattern Recognit 138:109374. https:\/\/doi.org\/10.1016\/j.patcog.2023.109374. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0031320323000754","DOI":"10.1016\/j.patcog.2023.109374"},{"key":"4965_CR52","doi-asserted-by":"crossref","unstructured":"Zhou ZH (2022) Open-environment machine learning. Natl Sci Rev 9(8):nwac123","DOI":"10.1093\/nsr\/nwac123"},{"key":"4965_CR53","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1016\/j.neucom.2020.04.074","volume":"407","author":"L Zou","year":"2020","unstructured":"Zou L, Li Y, Xu FY (2020) An adversarial denoising convolutional neural network for fault diagnosis of rotating machinery under noisy environment and limited sample size case. Neurocomputing 407:105\u2013120","journal-title":"Neurocomputing"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-023-04965-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-023-04965-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-023-04965-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,25]],"date-time":"2023-10-25T23:21:44Z","timestamp":1698276104000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-023-04965-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,18]]},"references-count":53,"journal-issue":{"issue":"22","published-print":{"date-parts":[[2023,11]]}},"alternative-id":["4965"],"URL":"https:\/\/doi.org\/10.1007\/s10489-023-04965-y","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"type":"print","value":"0924-669X"},{"type":"electronic","value":"1573-7497"}],"subject":[],"published":{"date-parts":[[2023,9,18]]},"assertion":[{"value":"11 August 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 September 2023","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}