{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:07:13Z","timestamp":1750219633257,"version":"3.41.0"},"reference-count":54,"publisher":"Springer Science and Business Media LLC","issue":"18","license":[{"start":{"date-parts":[[2025,3,21]],"date-time":"2025-03-21T00:00:00Z","timestamp":1742515200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,3,21]],"date-time":"2025-03-21T00:00:00Z","timestamp":1742515200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR202103040840"],"award-info":[{"award-number":["ZR202103040840"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2025,6]]},"DOI":"10.1007\/s00521-025-11159-9","type":"journal-article","created":{"date-parts":[[2025,3,22]],"date-time":"2025-03-22T16:57:24Z","timestamp":1742662644000},"page":"11909-11922","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Improved KW entropy: a complexity measurement technique for time series and its application in feature extraction of quay crane gearbox"],"prefix":"10.1007","volume":"37","author":[{"given":"Chunxia","family":"Gu","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0008-8906-4030","authenticated-orcid":false,"given":"Juan","family":"Bi","sequence":"additional","affiliation":[]},{"given":"Bing","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,3,21]]},"reference":[{"key":"11159_CR1","first-page":"1","volume":"3","author":"C Tan","year":"2021","unstructured":"Tan C, He J (2021) Integrated proactive and reactive strategies for sustainable berth allocation and quay crane assignment under uncertainty. Ann Oper Res 3:1\u201332","journal-title":"Ann Oper Res"},{"issue":"22","key":"11159_CR2","first-page":"198","volume":"38","author":"MH Hou","year":"2019","unstructured":"Hou MH, Hu X, Wang B et al (2019) Degradation feature extraction for the ship-to-shore crane turning point based on Weibull distribution. J Vib Shock 38(22):198\u2013203","journal-title":"J Vib Shock"},{"issue":"1","key":"11159_CR3","doi-asserted-by":"crossref","first-page":"015014","DOI":"10.1088\/1361-6501\/ac3470","volume":"33","author":"N Lu","year":"2022","unstructured":"Lu N, Zhou TX, Wei JF et al (2022) Application of a whale optimized variation mode decomposition method based on envelope sample entropy in the fault diagnosis of rotating machinery. Meas Sci Technol 33(1):015014","journal-title":"Meas Sci Technol"},{"issue":"6","key":"11159_CR4","first-page":"1","volume":"19","author":"Y Li","year":"2019","unstructured":"Li Y, Zhou X (2019) Rolling bearing fault diagnosis based on HVD algorithm and sample entropy. J Comput Methods Sci Eng 19(6):1\u201310","journal-title":"J Comput Methods Sci Eng"},{"issue":"12","key":"11159_CR5","doi-asserted-by":"crossref","first-page":"470","DOI":"10.1016\/j.isatra.2020.12.054","volume":"114","author":"Z Wang","year":"2021","unstructured":"Wang Z, Yao L, Chen G et al (2021) Modified multi-scale weighted permutation entropy and optimized support vector machine method for rolling bearing fault diagnosis with complex signals. ISA Trans 114(12):470\u2013484","journal-title":"ISA Trans"},{"issue":"3","key":"11159_CR6","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1002\/we.2570","volume":"24","author":"V Sharma","year":"2020","unstructured":"Sharma V (2020) Gear fault detection based on instantaneous frequency estimation using variegation mode decomposition and permutation entropy under real speed scenarios. Wind Energy 24(3):246\u2013259","journal-title":"Wind Energy"},{"key":"11159_CR7","doi-asserted-by":"crossref","first-page":"103167","DOI":"10.1016\/j.dsp.2021.103167","volume":"117","author":"W Ying","year":"2021","unstructured":"Ying W, Zheng J, Pan H et al (2021) Permutation entropy-based improved uniform phase empirical mode decomposition for mechanical fault diagnosis. Digit Signal Process 117:103167","journal-title":"Digit Signal Process"},{"key":"11159_CR8","first-page":"1","volume":"586","author":"AS Minhas","year":"2020","unstructured":"Minhas AS, Singh S, Sharma N et al (2020) Improvement in classification accuracy and computational speed in bearing fault diagnosis using multiscale fuzzy entropy. J Braz Soc Mech Sci Eng 586:1\u201321","journal-title":"J Braz Soc Mech Sci Eng"},{"key":"11159_CR9","first-page":"1","volume":"99","author":"C Liang","year":"2021","unstructured":"Liang C, Chen C (2021) Generalized composite multiscale diversity entropy and its application for fault diagnosis of rolling bearing in automotive production line. IEEE Access 99:1\u20131","journal-title":"IEEE Access"},{"issue":"2","key":"11159_CR10","doi-asserted-by":"crossref","first-page":"1447","DOI":"10.1007\/s11071-021-06728-1","volume":"108","author":"X Wang","year":"2022","unstructured":"Wang X, Si S, Li Y (2022) Hierarchical diversity entropy for the early fault diagnosis of rolling bearing. Nonlinear Dyn 108(2):1447\u20131462","journal-title":"Nonlinear Dyn"},{"key":"11159_CR11","doi-asserted-by":"crossref","first-page":"107574","DOI":"10.1016\/j.measurement.2020.107574","volume":"156","author":"Z Wang","year":"2020","unstructured":"Wang Z, Yao L, Cai Y (2020) Rolling bearing fault diagnosis using generalized refined composite multiscale sample entropy and optimized support vector machine. Measurement 156:107574","journal-title":"Measurement"},{"issue":"1","key":"11159_CR12","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1007\/s12206-018-1211-8","volume":"33","author":"Y Li","year":"2019","unstructured":"Li Y, Miao B, Zhang W et al (2019) Refined composite multiscale fuzzy entropy: Localized defect detection of rolling element bearing. J Mech Sci Technol 33(1):109\u2013120","journal-title":"J Mech Sci Technol"},{"issue":"05","key":"11159_CR13","first-page":"83","volume":"37","author":"M Wang","year":"2021","unstructured":"Wang M, Liu Y (2021) Fault Diagnosis method of rolling bearing based on time-shifted multi-scale dispersion entropy and SVM. Mach Des Res 37(05):83\u201387","journal-title":"Mach Des Res"},{"issue":"04","key":"11159_CR14","first-page":"92","volume":"37","author":"RT Peng","year":"2021","unstructured":"Peng RT, Luo XQ, Luo Y et al (2021) Rolling bearing fault diagnosis based on information entropy and alpha stable distribution. Mach Des Res 37(04):92\u201398","journal-title":"Mach Des Res"},{"issue":"04","key":"11159_CR15","first-page":"23","volume":"40","author":"M Chen","year":"2021","unstructured":"Chen M, Yu DJ, Gao YY (2021) Fault diagnosis of rolling bearings based on graph spectrum amplitude entropy of visibility graph. J Vib Shock 40(04):23\u201329","journal-title":"J Vib Shock"},{"issue":"03","key":"11159_CR16","first-page":"654","volume":"34","author":"XL Du","year":"2021","unstructured":"Du XL, Chen ZG, Wang YX et al (2021) Application of morphological empirical wavelet transform and IFractalNet in bearing fault identification. J Vib Eng 34(03):654\u2013662","journal-title":"J Vib Eng"},{"issue":"02","key":"11159_CR17","first-page":"208","volume":"36","author":"MS Xi","year":"2020","unstructured":"Xi MS, Xu X, Pan HX (2020) Early fault diagnosis of supply and delivery system based on k value optimization for vmd and matrix fractal. Mach Des Res 36(02):208\u2013211","journal-title":"Mach Des Res"},{"key":"11159_CR18","unstructured":"Jay L, Hai Q, Gang Y, et al., (2020) Bearing dataset from IMS of university of Cincinnati and NASA ames prognostics data repository."},{"key":"11159_CR19","unstructured":"Nectoux P, Gouriveaur R, Medjaher K, et al. (2012) Pronostia: an experimental platform for bearings accelerated degradation tests. IEEE International Conference on Prognostics and Health Management. Denver,CO."},{"issue":"2","key":"11159_CR20","doi-asserted-by":"crossref","first-page":"312","DOI":"10.20855\/ijav.2019.24.21461","volume":"24","author":"DJ Sun","year":"2019","unstructured":"Sun DJ, Wang B, Hu X, Wang W (2019) A fault diagnosis method based on improved pattern spectrum and foa-svm. Int J Acoust Vib 24(2):312\u2013319","journal-title":"Int J Acoust Vib"},{"issue":"10","key":"11159_CR21","first-page":"1272","volume":"55","author":"W Wang","year":"2021","unstructured":"Wang W, Wang B, Hu X et al (2021) Online degradation assessment of shore bridge hoisting gearbox based on improved symbolic sequence entropy and logistic regression model. J Shanghai Jiaotong Univ (Chin Ed) 55(10):1272\u20131280","journal-title":"J Shanghai Jiaotong Univ (Chin Ed)"},{"issue":"1","key":"11159_CR22","first-page":"211","volume":"34","author":"W Wang","year":"2021","unstructured":"Wang W, Wang B, Hu X et al (2021) A kind of degradation feature extraction using improved symbolic sequence entropy and sliding window singular value. J Vib Eng 34(1):211\u2013218","journal-title":"J Vib Eng"},{"key":"11159_CR23","doi-asserted-by":"crossref","first-page":"121405","DOI":"10.1016\/j.physa.2019.121405","volume":"528","author":"W Yao","year":"2018","unstructured":"Yao W, Wu M, Wang J (2018) Effects of controlling parameter on symbolic nonlinear complexity detection. Phys A: Stat Mech Appl 528:121405","journal-title":"Phys A: Stat Mech Appl"},{"issue":"9","key":"11159_CR24","doi-asserted-by":"crossref","first-page":"105835","DOI":"10.1016\/j.cnsns.2021.105835","volume":"99","author":"D Gu","year":"2021","unstructured":"Gu D, Mi Y, Lin A (2021) Application of Time-delay multiscale symbolic phase compensated transfer entropy in analyzing cyclic alternating pattern (CAP) in sleep-related pathological data. Commun Nonlinear Sci Numer Simul 99(9):105835","journal-title":"Commun Nonlinear Sci Numer Simul"},{"issue":"2","key":"11159_CR25","doi-asserted-by":"crossref","first-page":"1121","DOI":"10.1109\/TII.2021.3082517","volume":"18","author":"Y Li","year":"2022","unstructured":"Li Y, Wang S, Li N et al (2022) Multiscale symbolic diversity entropy: a novel measurement approach for time-series analysis and its application in fault diagnosis of planetary gearboxes. IEEE Trans Industr Inf 18(2):1121\u20131131","journal-title":"IEEE Trans Industr Inf"},{"key":"11159_CR26","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1016\/j.neucom.2018.07.021","volume":"315","author":"Y Li","year":"2018","unstructured":"Li Y, Liang X, Wei Y et al (2018) A method based on refined composite multi-scale symbolic dynamic entropy and ISVM-BT for rotating machinery fault diagnosis. Neurocomputing 315:246\u2013260","journal-title":"Neurocomputing"},{"issue":"4","key":"11159_CR27","doi-asserted-by":"crossref","first-page":"1950026","DOI":"10.1142\/S0219477519500263","volume":"18","author":"N Zhang","year":"2019","unstructured":"Zhang N, Sun Y, Zhang Y et al (2019) Distinguishing stock indices and detecting economic crises based on weighted symbolic permutation entropy. Fluct Noise Lett 18(4):1950026","journal-title":"Fluct Noise Lett"},{"key":"11159_CR28","doi-asserted-by":"crossref","unstructured":"Wang S, Li Y (2020) A novel nonlinear analysis tool: multi-scale symbolic sample entropy and its application in condition monitoring of rotary machinery. In: 2020 Asia-Pacific international symposium on advanced reliability and maintenance modeling (APARM). Vancouver, BC, Canada.","DOI":"10.1109\/APARM49247.2020.9209495"},{"issue":"1","key":"11159_CR29","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1177\/1475921720923973","volume":"20","author":"C Yang","year":"2020","unstructured":"Yang C, Jia M (2020) Health condition identification for rolling bearing based on hierarchical multi-scale symbolic dynamic entropy and least squares support tensor machine\u2013based binary tree. Struct Health Monit 20(1):151\u2013172","journal-title":"Struct Health Monit"},{"key":"11159_CR30","doi-asserted-by":"crossref","first-page":"108052","DOI":"10.1016\/j.ymssp.2021.108052","volume":"162","author":"Y Li","year":"2022","unstructured":"Li Y, Wang S, Yang Y et al (2022) Multiscale symbolic fuzzy entropy: an entropy denoising method for weak feature extraction of rotating machinery. Mech Syst Signal Process 162:108052","journal-title":"Mech Syst Signal Process"},{"issue":"5","key":"11159_CR31","doi-asserted-by":"crossref","first-page":"1993","DOI":"10.1007\/s12206-021-0417-3","volume":"35","author":"M Han","year":"2021","unstructured":"Han M, Wu Y, Wang Y et al (2021) Roller bearing fault diagnosis based on LMD and multi-scale symbolic dynamic information entropy. J Mech Sci Technol 35(5):1993\u20132005","journal-title":"J Mech Sci Technol"},{"issue":"12","key":"11159_CR32","doi-asserted-by":"crossref","first-page":"1138","DOI":"10.3390\/e21121138","volume":"21","author":"C Dou","year":"2019","unstructured":"Dou C, Lin J (2019) Adaptive multi-scale symbolic-dynamics entropy for condition monitoring of rotating machinery. Entropy 21(12):1138","journal-title":"Entropy"},{"issue":"01","key":"11159_CR33","first-page":"140","volume":"45","author":"BW Yang","year":"2018","unstructured":"Yang BW (2018) Low-rate-denial-of-service attack detection by symbolic dynamics method. J Xidian Univ 45(01):140\u2013144","journal-title":"J Xidian Univ"},{"issue":"3\/4","key":"11159_CR34","first-page":"1769","volume":"129","author":"LT Glissoi","year":"2023","unstructured":"Glissoi LT, Roberto AP et al (2023) Machine condition monitoring in FDM based on electret microphone, SVM, and neural networks. Int J Advan Manuf Technol 129(3\/4):1769\u20131786","journal-title":"Int J Advan Manuf Technol"},{"issue":"1","key":"11159_CR35","doi-asserted-by":"crossref","first-page":"9","DOI":"10.30880\/ijie.2023.15.01.002","volume":"15","author":"MM Suhaimi","year":"2023","unstructured":"Suhaimi MM, Ghazali AS, Jazlan ASSN (2023) Explication of extrinsic forearm muscles on the classification of thumb position using high-density surface electromyogram. Int J Integr Eng 15(1):9\u201321","journal-title":"Int J Integr Eng"},{"issue":"13\/16","key":"11159_CR36","doi-asserted-by":"crossref","first-page":"2057","DOI":"10.1080\/10255842.2023.2165068","volume":"26","author":"K Anam","year":"2023","unstructured":"Anam K, Swasono DI, Hanggara MFS (2023) Random forest-based simultaneous and proportional myoelectric control system for finger movements. Comput Methods Biomech Biomed Engin 26(13\/16):2057\u20132069","journal-title":"Comput Methods Biomech Biomed Engin"},{"key":"11159_CR37","doi-asserted-by":"crossref","first-page":"108964","DOI":"10.1016\/j.ymssp.2022.108964","volume":"172","author":"Z Wang","year":"2022","unstructured":"Wang Z, Yang J, Guo Y (2022) Unknown fault feature extraction of rolling bearings under variable speed conditions based on statistical complexity measures. Mech Syst Signal Process 172:108964","journal-title":"Mech Syst Signal Process"},{"key":"11159_CR38","doi-asserted-by":"crossref","unstructured":"Fang T, Zhang C (2022) Fault feature extraction of rolling-element bearing based on neighborhood rough set. 2022 Global Reliability and Prognostics and Health Management (PHM-Yantai), (2022)1\u20134.","DOI":"10.1109\/PHM-Yantai55411.2022.9942156"},{"issue":"8","key":"11159_CR39","doi-asserted-by":"crossref","first-page":"935","DOI":"10.1007\/s11265-023-01845-z","volume":"95","author":"B Qi","year":"2023","unstructured":"Qi B, Li Y, Yao W, Li Z (2023) Application of EMD combined with deep learning and knowledge graph in bearing fault. J Signal Process Syst 95(8):935\u2013954","journal-title":"J Signal Process Syst"},{"issue":"1","key":"11159_CR40","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1007\/s11668-022-01567-7","volume":"23","author":"Z Jin","year":"2023","unstructured":"Jin Z, Chen D, He D et al (2023) Bearing fault diagnosis based on VMD and improved CNN. J Fail Anal Prev 23(1):165\u2013175","journal-title":"J Fail Anal Prev"},{"issue":"11","key":"11159_CR41","doi-asserted-by":"crossref","first-page":"5759","DOI":"10.1007\/s12206-023-1015-3","volume":"37","author":"Z Ziyou","year":"2023","unstructured":"Ziyou Z, Wenhua C, Ce Y (2023) Adaptive range selection for parameter optimization of VMD algorithm in rolling bearing fault diagnosis under strong background noise. J Mech Sci Technol 37(11):5759\u20135773","journal-title":"J Mech Sci Technol"},{"key":"11159_CR42","doi-asserted-by":"crossref","first-page":"115328","DOI":"10.1016\/j.measurement.2024.115328","volume":"238","author":"X Dai","year":"2024","unstructured":"Dai X, Yi K, Wang F et al (2024) Bearing fault diagnosis based on POA-VMD with GADF-Swin Transformer transfer learning network. Measurement 238:115328","journal-title":"Measurement"},{"issue":"5","key":"11159_CR43","doi-asserted-by":"crossref","first-page":"108216","DOI":"10.1016\/j.ymssp.2021.108216","volume":"164","author":"Q Ni","year":"2022","unstructured":"Ni Q, Ji JC, Feng K et al (2022) A fault information-guided variational mode decomposition (FIVMD) method for rolling element bearings diagnosis. Mech Syst Signal Process 164(5):108216","journal-title":"Mech Syst Signal Process"},{"key":"11159_CR44","doi-asserted-by":"crossref","first-page":"862","DOI":"10.21595\/jve.2022.22354","volume":"24","author":"T Zhang","year":"2022","unstructured":"Zhang T, Chen Y, Chen Y (2022) Hierarchical dispersion entropy and its application in fault diagnosis of rolling bearing. J Vibroengineering 24:862","journal-title":"J Vibroengineering"},{"key":"11159_CR45","doi-asserted-by":"crossref","first-page":"110843","DOI":"10.1016\/j.measurement.2022.110843","volume":"191","author":"W Jiang","year":"2022","unstructured":"Jiang W, Xu Y, Chen Z et al (2022) Fault diagnosis for rolling bearing using a hybrid hierarchical method based on scale-variable dispersion entropy and parametric t-SNE algorithm. Measurement 191:110843","journal-title":"Measurement"},{"issue":"6","key":"11159_CR46","doi-asserted-by":"crossref","first-page":"4209","DOI":"10.1007\/s11071-023-09152-9","volume":"112","author":"W Bing","year":"2024","unstructured":"Bing W, Wentao Q, Wei XW (2024) A rolling bearing fault diagnosis technique based on fined-grained multi-scale symbolic entropy and whale optimization algorithm-MSVM. Nonlinear Dyn 112(6):4209\u20134225","journal-title":"Nonlinear Dyn"},{"key":"11159_CR47","doi-asserted-by":"crossref","unstructured":"Ma P, Chen KCJ (2023) NN-based bearing fault diagnosis using exponential power entropy and a decision threshold. In: 2023 IEEE International Conference on Omni-layer Intelligent Systems (COINS), 1\u20135.","DOI":"10.1109\/COINS57856.2023.10189273"},{"key":"11159_CR48","doi-asserted-by":"publisher","DOI":"10.1142\/S0218127423500542","author":"YH Tong","year":"2023","unstructured":"Tong YH, Ling G, Guan ZH et al (2023) Refined composite multiscale phase renyi dispersion entropy for complexity measure[J]. Int J Bifurcation Chaos Appl Sci Eng. https:\/\/doi.org\/10.1142\/S0218127423500542","journal-title":"Int J Bifurcation Chaos Appl Sci Eng"},{"issue":"1","key":"11159_CR49","first-page":"2022020","volume":"3","author":"Y Zhang","year":"2023","unstructured":"Zhang Y, Li Y, Kong L et al (2023) Rolling bearing condition monitoring method based on multi-feature information fusion[J]. J Advan Manuf Sci Technol 3(1):2022020","journal-title":"J Advan Manuf Sci Technol"},{"key":"11159_CR50","doi-asserted-by":"publisher","DOI":"10.3390\/app13010192","author":"X Yuan","year":"2022","unstructured":"Yuan X, Liu H, Zhang H (2022) Enhanced rolling bearing fault diagnosis combining novel fluctuation entropy guided-VMD with neighborhood statistical mode. Appl Sci. https:\/\/doi.org\/10.3390\/app13010192","journal-title":"Appl Sci"},{"key":"11159_CR51","doi-asserted-by":"crossref","first-page":"122108","DOI":"10.1016\/j.energy.2021.122108","volume":"239","author":"D He","year":"2022","unstructured":"He D, Liu C, Jin Z et al (2022) Fault diagnosis of flywheel bearing based on parameter optimization variational mode decomposition energy entropy and deep learning. Energy 239:122108","journal-title":"Energy"},{"issue":"2","key":"11159_CR52","doi-asserted-by":"crossref","first-page":"915","DOI":"10.1063\/1.1531823","volume":"74","author":"CS Daw","year":"2003","unstructured":"Daw CS, Finney C, Tracy ER (2003) A review of symbolic analysis of experimental data. Rev Sci Instrum 74(2):915\u2013930","journal-title":"Rev Sci Instrum"},{"issue":"3","key":"11159_CR53","first-page":"228","volume":"145","author":"Q Yu","year":"2021","unstructured":"Yu Q, Labi S, Fricker JD (2021) Does highway project bundling policy affect bidding competition? Insights from a mixed ordinal logistic model. Transp Res Part A Policy Pract 145(3):228\u2013242","journal-title":"Transp Res Part A Policy Pract"},{"key":"11159_CR54","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1038\/s41598-023-50575-6","volume":"14","author":"CX Gu","year":"2024","unstructured":"Gu CX, Bi J, Wang B (2024) A degradation feature extraction technique based on static divided symbol sequence entropy. Sci Rep 14:463","journal-title":"Sci Rep"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11159-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-025-11159-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11159-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T14:48:17Z","timestamp":1750171697000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-025-11159-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,21]]},"references-count":54,"journal-issue":{"issue":"18","published-print":{"date-parts":[[2025,6]]}},"alternative-id":["11159"],"URL":"https:\/\/doi.org\/10.1007\/s00521-025-11159-9","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2025,3,21]]},"assertion":[{"value":"17 October 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 September 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 March 2025","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 have no competing interests to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}