{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T10:17:07Z","timestamp":1788171427356,"version":"build-2803163510"},"reference-count":55,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T00:00:00Z","timestamp":1783555200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T00:00:00Z","timestamp":1783555200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Guang dong Provincial Key Laboratory of Novel Security Intelligence Technologies, China","award":["Grant NO. 2022B1212010005"],"award-info":[{"award-number":["Grant NO. 2022B1212010005"]}]},{"name":"Natural Science Foun dation of Guangdong Province, China","award":["Grant NO. 2024A1515010242"],"award-info":[{"award-number":["Grant NO. 2024A1515010242"]}]},{"name":"Shenzhen Research Council","award":["Grant NO. GXWD202208111702530022"],"award-info":[{"award-number":["Grant NO. GXWD202208111702530022"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2026,8]]},"DOI":"10.1007\/s13042-026-03207-0","type":"journal-article","created":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T07:36:06Z","timestamp":1783582566000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Utility-driven mining of contiguous sequences"],"prefix":"10.1007","volume":"17","author":[{"given":"Chunkai","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Quanjian","family":"Dai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hanyu","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ryan Han-Yuan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wensheng","family":"Gan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Philip S.","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,9]]},"reference":[{"key":"3207_CR1","doi-asserted-by":"crossref","unstructured":"Agrawal R, Srikant R (1995) Mining sequential patterns. In: Proceedings of the 11th international conference on data engineering. IEEE, pp 3\u201314","DOI":"10.1109\/ICDE.1995.380415"},{"key":"3207_CR2","doi-asserted-by":"crossref","unstructured":"Srikant R, Agrawal R (1996) Mining sequential patterns: generalizations and performance improvements. In: Proceedings of the 5th international conference on extending database technology. Springer, pp 1\u201317","DOI":"10.1007\/BFb0014140"},{"key":"3207_CR3","doi-asserted-by":"crossref","unstructured":"Wang Q, Sheng V, Wu X (2017) Keyphrase extraction with sequential pattern mining. In: Proceedings of the AAAI conference on artificial intelligence. p 31","DOI":"10.1609\/aaai.v31i1.11075"},{"issue":"1","key":"3207_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12911-021-01389-x","volume":"21","author":"X Yu","year":"2021","unstructured":"Yu X, Ma N, Yang T, Zhang Y, Miao Q, Tao J, Li H, Li Y, Yang Y (2021) A multi-level hypoglycemia early alarm system based on sequence pattern mining. BMC Med Inform Decis Mak 21(1):1\u201311","journal-title":"BMC Med Inform Decis Mak"},{"issue":"5","key":"3207_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3399671","volume":"14","author":"T Wang","year":"2020","unstructured":"Wang T, Duan L, Dong G, Bao Z (2020) Efficient mining of outlying sequence patterns for analyzing outlierness of sequence data. ACM Trans Knowl Discov Data 14(5):1\u201326","journal-title":"ACM Trans Knowl Discov Data"},{"key":"3207_CR6","doi-asserted-by":"crossref","unstructured":"Le HH, Horino Y, Yamazaki T, Araki K, Yokota H (2021) Sequential pattern mining of large combinable items with values for a set-of-items recommendation. In: IEEE 34th international symposium on computer-based medical systems. IEEE, pp 56\u201361","DOI":"10.1109\/CBMS52027.2021.00017"},{"issue":"24","key":"3207_CR7","doi-asserted-by":"publisher","first-page":"35135","DOI":"10.1007\/s11042-019-08096-w","volume":"78","author":"C Bin","year":"2019","unstructured":"Bin C, Gu T, Sun Y, Chang L (2019) A personalized POI route recommendation system based on heterogeneous tourism data and sequential pattern mining. Multimed Tools Appl 78(24):35135\u201335156","journal-title":"Multimed Tools Appl"},{"issue":"4","key":"3207_CR8","doi-asserted-by":"publisher","first-page":"1306","DOI":"10.1109\/TKDE.2019.2942594","volume":"33","author":"W Gan","year":"2021","unstructured":"Gan W, Lin J-W, Fournier-Viger P, Chao H-C, Tseng VS, Yu PS (2021) A survey of utility-oriented pattern mining. IEEE Trans Know Data Eng 33(4):1306\u20131327","journal-title":"IEEE Trans Know Data Eng"},{"issue":"5","key":"3207_CR9","doi-asserted-by":"publisher","first-page":"676","DOI":"10.4218\/etrij.10.1510.0066","volume":"32","author":"CF Ahmed","year":"2010","unstructured":"Ahmed CF, Tanbeer SK, Jeong B-S (2010) A novel approach for mining high-utility sequential patterns in sequence databases. ETRI J 32(5):676\u2013686","journal-title":"ETRI J"},{"key":"3207_CR10","doi-asserted-by":"crossref","unstructured":"Yin J, Zheng Z, Cao L (2012) USpan: an efficient algorithm for mining high utility sequential patterns. In: Proceedings of the 18th ACM SIGKDD international conference on knowledge discovery and data mining. pp 660\u2013668","DOI":"10.1145\/2339530.2339636"},{"key":"3207_CR11","doi-asserted-by":"crossref","unstructured":"Zihayat M, Hut ZZ, An A, Hut Y (2016) Distributed and parallel high utility sequential pattern mining. In: Proceedings of IEEE international conference on big data, IEEE\u00a0pp. 853\u2013862","DOI":"10.1109\/BigData.2016.7840678"},{"issue":"6","key":"3207_CR12","doi-asserted-by":"publisher","first-page":"866","DOI":"10.1109\/69.553155","volume":"8","author":"M-S Chen","year":"1996","unstructured":"Chen M-S, Han J, Yu PS (1996) Data mining: an overview from a database perspective. IEEE Trans Knowl Data Eng 8(6):866\u2013883","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"2","key":"3207_CR13","doi-asserted-by":"publisher","first-page":"597","DOI":"10.1007\/s10115-015-0914-8","volume":"49","author":"J-Z Wang","year":"2016","unstructured":"Wang J-Z, Huang J-L, Chen Y-C (2016) On efficiently mining high utility sequential patterns. Knowl Inf Syst 49(2):597\u2013627","journal-title":"Knowl Inf Syst"},{"key":"3207_CR14","doi-asserted-by":"publisher","first-page":"222","DOI":"10.1016\/j.ins.2019.10.033","volume":"513","author":"W Gan","year":"2020","unstructured":"Gan W, Lin J-W, Zhang J, Chao H-C, Fujita H, Yu PS (2020) ProUM: projection-based utility mining on sequence data. Inf Sci 513:222\u2013240","journal-title":"Inf Sci"},{"issue":"2","key":"3207_CR15","doi-asserted-by":"publisher","first-page":"487","DOI":"10.1109\/TCYB.2020.2970176","volume":"51","author":"W Gan","year":"2021","unstructured":"Gan W, Lin JC-W, Zhang J, Fournier-Viger P, Chao H-C, Yu PS (2021) Fast utility mining on sequence data. IEEE Trans Cybern 51(2):487\u2013500","journal-title":"IEEE Trans Cybern"},{"key":"3207_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2023.118945","volume":"637","author":"X Zhang","year":"2023","unstructured":"Zhang X, Lai F, Chen G, Gan W (2023) Mining high-utility sequences with positive and negative values. Inf Sci 637:118945. https:\/\/doi.org\/10.1016\/j.ins.2023.118945","journal-title":"Inf Sci"},{"key":"3207_CR17","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1016\/j.knosys.2017.12.003","volume":"143","author":"W Gan","year":"2018","unstructured":"Gan W, Lin JC-W, Fournier-Viger P, Chao H-C, Fujita H (2018) Extracting non-redundant correlated purchase behaviors by utility measure. Knowl-Based Syst 143:30\u201341","journal-title":"Knowl-Based Syst"},{"issue":"1","key":"3207_CR18","doi-asserted-by":"publisher","first-page":"3","DOI":"10.4103\/0256-4602.74506","volume":"28","author":"CF Ahmed","year":"2011","unstructured":"Ahmed CF, Tanbeer SK, Jeong B-S (2011) A framework for mining high utility web access sequences. IETE Tech Rev 28(1):3\u201316","journal-title":"IETE Tech Rev"},{"issue":"2","key":"3207_CR19","doi-asserted-by":"publisher","first-page":"363","DOI":"10.1007\/s10115-012-0483-z","volume":"37","author":"B-E Shie","year":"2013","unstructured":"Shie B-E, Hsiao H-F, Tseng VS (2013) Efficient algorithms for discovering high utility user behavior patterns in mobile commerce environments. Knowl Inf Syst 37(2):363\u2013387","journal-title":"Knowl Inf Syst"},{"key":"3207_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2019.105076","volume":"189","author":"L Bermingham","year":"2020","unstructured":"Bermingham L, Lee I (2020) Mining distinct and contiguous sequential patterns from large vehicle trajectories. Knowl-Based Syst 189:105076","journal-title":"Knowl-Based Syst"},{"key":"3207_CR21","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-021-02193-w","author":"MS Nawaz","year":"2021","unstructured":"Nawaz MS, Fournier-Viger P, Shojaee A, Fujita H (2021) Using artificial intelligence techniques for COVID-19 genome analysis. Appl Intell. https:\/\/doi.org\/10.1007\/s10489-021-02193-w","journal-title":"Appl Intell"},{"key":"3207_CR22","doi-asserted-by":"crossref","unstructured":"Chen J, Cook T (2007) Mining contiguous sequential patterns from web logs. In: Proceedings of the 16th international conference on world wide web. pp 1177\u20131178","DOI":"10.1145\/1242572.1242753"},{"key":"3207_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.knosys.2015.06.014","volume":"89","author":"J Zhang","year":"2015","unstructured":"Zhang J, Wang Y, Yang D (2015) CCSpan: Mining closed contiguous sequential patterns. Knowl-Based Syst 89:1\u201313","journal-title":"Knowl-Based Syst"},{"issue":"1","key":"3207_CR24","first-page":"35","volume":"6","author":"S Jawahar","year":"2018","unstructured":"Jawahar S, Sumathi P (2018) An efficient contiguous pattern mining technique to predict mutations in breast cancer for dna data sequences. Int J Bioinform Biol Sci 6(1):35\u201341","journal-title":"Int J Bioinform Biol Sci"},{"issue":"7","key":"3207_CR25","doi-asserted-by":"publisher","first-page":"1282","DOI":"10.1080\/13658816.2017.1393542","volume":"32","author":"C Yang","year":"2018","unstructured":"Yang C, Gid\u00f3falvi G (2018) Mining and visual exploration of closed contiguous sequential patterns in trajectories. Int J Geogr Inf Sci 32(7):1282\u20131304","journal-title":"Int J Geogr Inf Sci"},{"key":"3207_CR26","doi-asserted-by":"crossref","unstructured":"Zhou L, Liu Y, Wang J, Shi Y (2007) Utility-based web path traversal pattern mining. In: Proceedings of the 7th IEEE international conference on data mining workshops. IEEE, pp 373\u2013380","DOI":"10.1109\/ICDMW.2007.72"},{"key":"3207_CR27","unstructured":"Ahmed CF, Tanbeer SK, Jeong B-S, Lee Y-K (2009) Efficient mining of utility-based web path traversal patterns. In: Proceedings of the 11th international conference on advanced communication technology, vol 3. IEEE, pp 2215\u20132218"},{"issue":"3","key":"3207_CR28","first-page":"959","volume":"12","author":"G Huang","year":"2016","unstructured":"Huang G, Gao R, Wang J, Yan J, Ren J (2016) An efficient algorithm for mining high utility contiguous patterns from software executing traces. Int J Innov Comput Inf Control 12(3):959\u2013971","journal-title":"Int J Innov Comput Inf Control"},{"key":"3207_CR29","doi-asserted-by":"crossref","unstructured":"Yap G-E, Li X-L, Yu PS (2012) Effective next-items recommendation via personalized sequential pattern mining. In: International conference on database systems for advanced applications. Springer, pp 48\u201364","DOI":"10.1007\/978-3-642-29035-0_4"},{"key":"3207_CR30","doi-asserted-by":"crossref","unstructured":"Rage UK (2025) Mining symbolic sequences. Hands-on pattern mining: theory and examples with PAMI, Sklearn, Keras, and TensorFlow. pp 127\u2013133","DOI":"10.1007\/978-981-96-6791-8_13"},{"key":"3207_CR31","unstructured":"Agrawal R, Srikant R (1994) Fast algorithms for mining association rules. In: Proceedings of the 20th international conference on very large data bases. Morgan Kaufmann, pp 487\u2013499"},{"issue":"1","key":"3207_CR32","first-page":"54","volume":"1","author":"P Fournier-Viger","year":"2017","unstructured":"Fournier-Viger P, Lin JC-W, Kiran RU, Koh YS, Thomas R (2017) A survey of sequential pattern mining. Data Sci Pattern Recognit 1(1):54\u201377","journal-title":"Data Sci Pattern Recognit"},{"issue":"1\u20132","key":"3207_CR33","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1023\/A:1007652502315","volume":"42","author":"MJ Zaki","year":"2001","unstructured":"Zaki MJ (2001) SPADE: an efficient algorithm for mining frequent sequences. Mach Learn 42(1\u20132):31\u201360","journal-title":"Mach Learn"},{"key":"3207_CR34","doi-asserted-by":"crossref","unstructured":"Ayres J, Flannick J, Gehrke J, Yiu T (2002) Sequential pattern mining using a bitmap representation. In: Proceedings of the eighth ACM SIGKDD International conference on knowledge discovery and data mining. ACM, pp 429\u2013435","DOI":"10.1145\/775047.775109"},{"key":"3207_CR35","doi-asserted-by":"crossref","unstructured":"Fournier-Viger P, Gomariz A, Campos M, Thomas R (2014) Fast vertical mining of sequential patterns using co-occurrence information. In: Proceedings of the 18th pacific-asia conference on knowledge discovery and data mining. Springer, pp 40\u201352","DOI":"10.1007\/978-3-319-06608-0_4"},{"issue":"11","key":"3207_CR36","doi-asserted-by":"publisher","first-page":"1424","DOI":"10.1109\/TKDE.2004.77","volume":"16","author":"J Pei","year":"2004","unstructured":"Pei J, Han J, Mortazavi-Asl B, Wang J, Pinto H, Chen Q, Dayal U, Hsu M-C (2004) Mining sequential patterns by pattern-growth: the PrefixSpan approach. IEEE Trans Knowl Data Eng 16(11):1424\u20131440","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"3","key":"3207_CR37","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3314107","volume":"13","author":"W Gan","year":"2019","unstructured":"Gan W, Lin JC-W, Fournier-Viger P, Chao H-C, Yu PS (2019) A survey of parallel sequential pattern mining. ACM Trans Knowl Discov Data 13(3):1\u201334","journal-title":"ACM Trans Knowl Discov Data"},{"issue":"10","key":"3207_CR38","doi-asserted-by":"publisher","first-page":"2645","DOI":"10.1109\/TKDE.2015.2420557","volume":"27","author":"OK Alkan","year":"2015","unstructured":"Alkan OK, Karagoz P (2015) CRoM and HuspExt: Improving efficiency of high utility sequential pattern extraction. IEEE Trans Knowl Data Eng 27(10):2645\u20132657","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"3207_CR39","doi-asserted-by":"publisher","first-page":"40714","DOI":"10.1109\/ACCESS.2020.2976662","volume":"8","author":"JC-W Lin","year":"2020","unstructured":"Lin JC-W, Li Y, Fournier-Viger P, Djenouri Y, Zhang J (2020) Efficient chain structure for high-utility sequential pattern mining. IEEE Access 8:40714\u201340722","journal-title":"IEEE Access"},{"key":"3207_CR40","doi-asserted-by":"crossref","unstructured":"Truong-Chi T, Fournier-Viger P (2019) A survey of high utility sequential pattern mining. High-utility pattern mining: theory. Algorithms and applications. Springer, Cham, Switzerland, pp 97\u2013129","DOI":"10.1007\/978-3-030-04921-8_4"},{"key":"3207_CR41","doi-asserted-by":"publisher","DOI":"10.1145\/3628450","author":"C Zhang","year":"2024","unstructured":"Zhang C, Lyu M, Gan W, Yu PS (2024) Totally-ordered sequential rules for utility maximization. ACM Trans Knowl Discov Data. https:\/\/doi.org\/10.1145\/3628450","journal-title":"ACM Trans Knowl Discov Data"},{"key":"3207_CR42","doi-asserted-by":"publisher","first-page":"342","DOI":"10.1016\/j.ins.2021.04.035","volume":"570","author":"C Zhang","year":"2021","unstructured":"Zhang C, Du Z, Gan W, Yu PS (2021) TKUS: Mining top-$$k$$ high utility sequential patterns. Inf Sci 570:342\u2013359","journal-title":"Inf Sci"},{"issue":"1","key":"3207_CR43","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1007\/s11280-020-00839-2","volume":"24","author":"M Ceci","year":"2021","unstructured":"Ceci M, Lanotte PF (2021) Closed sequential pattern mining for sitemap generation. World Wide Web 24(1):175\u2013203","journal-title":"World Wide Web"},{"key":"3207_CR44","doi-asserted-by":"crossref","unstructured":"Pan J, Wang P, Wang W, Shi B, Yang G (2005) Efficient algorithms for mining maximal frequent concatenate sequences in biological datasets. In: Proceedings of the fifth international conference on computer and information technology, IEEE\u00a0pp. 98\u2013104","DOI":"10.1109\/CIT.2005.106"},{"key":"3207_CR45","doi-asserted-by":"crossref","unstructured":"Kang TH, Yoo JS, Kim HY (2007) Mining frequent contiguous sequence patterns in biological sequences. In: Proceedings of the IEEE 7th international symposium on bioinformatics and bioengineering. IEEE, pp 723\u2013728","DOI":"10.1109\/BIBE.2007.4375640"},{"key":"3207_CR46","doi-asserted-by":"crossref","unstructured":"Chen J, Shankar S, Kelly A, Gningue S, Rajaravivarma R (2009) A two stage approach for contiguous sequential pattern mining. In: Proceedings of the IEEE international conference on information reuse and integration. IEEE, pp 382\u2013387","DOI":"10.1109\/IRI.2009.5211583"},{"key":"3207_CR47","doi-asserted-by":"crossref","unstructured":"Abboud Y, Boyer A, Brun A (2017) CCPM: a scalable and noise-resistant closed contiguous sequential patterns mining algorithm. In: Proceedings of the 13th international conference on machine learning and data mining in pattern recognition. Springer, pp 147\u2013162","DOI":"10.1007\/978-3-319-62416-7_11"},{"key":"3207_CR48","doi-asserted-by":"publisher","first-page":"36057","DOI":"10.1109\/ACCESS.2019.2905353","volume":"7","author":"Y-H Goo","year":"2019","unstructured":"Goo Y-H, Shim K-S, Lee M-S, Kim M-S (2019) Protocol specification extraction based on contiguous sequential pattern algorithm. IEEE Access 7:36057\u201336074","journal-title":"IEEE Access"},{"issue":"5","key":"3207_CR49","doi-asserted-by":"publisher","first-page":"855","DOI":"10.1109\/TCBB.2015.2495132","volume":"13","author":"J Zhang","year":"2015","unstructured":"Zhang J, Wang Y, Zhang C, Shi Y (2015) Mining contiguous sequential generators in biological sequences. IEEE\/ACM Trans Comput Biol Bioinf 13(5):855\u2013867","journal-title":"IEEE\/ACM Trans Comput Biol Bioinf"},{"key":"3207_CR50","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2023.119791","volume":"653","author":"K Hu","year":"2024","unstructured":"Hu K, Gan W, Huang S, Peng H, Fournier-Viger P (2024) Targeted mining of contiguous sequential patterns. Inf Sci 653:119791. https:\/\/doi.org\/10.1016\/j.ins.2023.119791","journal-title":"Inf Sci"},{"key":"3207_CR51","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.111314","volume":"284","author":"Z Chen","year":"2024","unstructured":"Chen Z, Gan W, Huang G, Zheng Y, Yu PS (2024) Towards utility-driven contiguous sequential patterns in uncertain multi-sequences. Knowl-Based Syst 284:111314","journal-title":"Knowl-Based Syst"},{"key":"3207_CR52","unstructured":"Agrawal R, Srikant R (1994) Quest synthetic data generator. IBM Research Report . http:\/\/www.Almaden.ibm.com\/cs\/quest\/syndata.html"},{"issue":"2","key":"3207_CR53","first-page":"1","volume":"16","author":"C Zhang","year":"2021","unstructured":"Zhang C, Du Z, Yang Y, Gan W, Yu PS (2021) On-shelf utility mining of sequence data. ACM Trans Knowl Discov Data 16(2):1\u201331","journal-title":"ACM Trans Knowl Discov Data"},{"issue":"1","key":"3207_CR54","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1007\/s10044-017-0624-9","volume":"21","author":"S Alias","year":"2018","unstructured":"Alias S, Mohammad SK, Hoon GK, Ping TT (2018) A text representation model using sequential pattern-growth method. Pattern Anal Appl 21(1):233\u2013247","journal-title":"Pattern Anal Appl"},{"key":"3207_CR55","doi-asserted-by":"crossref","unstructured":"Stamoulakatou E, Gulino A, Pinoli P (2018) DLA: a distributed, location-based and apriori-based algorithm for biological sequence pattern mining. In: IEEE international conference on big data. IEEE, pp 1121\u20131126","DOI":"10.1109\/BigData.2018.8622007"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-026-03207-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-026-03207-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-026-03207-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T09:38:40Z","timestamp":1788169120000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-026-03207-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,9]]},"references-count":55,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2026,8]]}},"alternative-id":["3207"],"URL":"https:\/\/doi.org\/10.1007\/s13042-026-03207-0","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,9]]},"assertion":[{"value":"27 August 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 June 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 July 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no conflict of interest.","order":1,"name":"Ethics","label":"Conflict of interest","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not Applicable.","order":2,"name":"Ethics","label":"Research involving human and\/or animal participants","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not Applicable.","order":3,"name":"Ethics","label":"Informed consent","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"380"}}