{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,27]],"date-time":"2025-09-27T17:00:27Z","timestamp":1758992427694,"version":"3.37.3"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2023,8,10]],"date-time":"2023-08-10T00:00:00Z","timestamp":1691625600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,8,10]],"date-time":"2023-08-10T00:00:00Z","timestamp":1691625600000},"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":"publisher","award":["61976089"],"award-info":[{"award-number":["61976089"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2024,3]]},"DOI":"10.1007\/s13042-023-01931-5","type":"journal-article","created":{"date-parts":[[2023,8,10]],"date-time":"2023-08-10T03:24:01Z","timestamp":1691637841000},"page":"739-756","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Fast attribute reduction by neighbor inconsistent pair selection for dynamic decision tables"],"prefix":"10.1007","volume":"15","author":[{"given":"Chucai","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hong","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhengxiang","family":"Lu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1459-0833","authenticated-orcid":false,"given":"Jianhua","family":"Dai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,8,10]]},"reference":[{"issue":"5","key":"1931_CR1","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1007\/BF01001956","volume":"11","author":"Z Pawlak","year":"1982","unstructured":"Pawlak Z (1982) Rough sets. Int J Comput Inform Sci 11(5):341\u2013356","journal-title":"Int J Comput Inform Sci"},{"key":"1931_CR2","volume":"196","author":"ZW Li","year":"2020","unstructured":"Li ZW, Dai JH, Chen JL, Fujita H (2020) Measures of uncertainty based on gaussian kernel for a fully fuzzy information system. Knowl-Based Syst 196:105791","journal-title":"Knowl-Based Syst"},{"issue":"1","key":"1931_CR3","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1109\/TFUZZ.2020.2989098","volume":"29","author":"L Sun","year":"2021","unstructured":"Sun L, Wang LY, Ding WP, Qian YH, Xu JC (2021) Feature selection using fuzzy neighborhood entropy-based uncertainty measures for fuzzy neighborhood multigranulation rough sets. IEEE Trans Fuzzy Syst 29(1):19\u201333","journal-title":"IEEE Trans Fuzzy Syst"},{"key":"1931_CR4","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.asoc.2019.02.018","volume":"78","author":"B Zhao","year":"2019","unstructured":"Zhao B, Ren Y, Gao DK (2019) Prediction of service life of large centrifugal compressor remanufactured impeller based on clustering rough set and fuzzy bandelet neural network. Appl Soft Comput 78:132\u2013140","journal-title":"Appl Soft Comput"},{"issue":"6","key":"1931_CR5","doi-asserted-by":"crossref","first-page":"1460","DOI":"10.1109\/TFUZZ.2017.2717803","volume":"25","author":"R Wang","year":"2017","unstructured":"Wang R, Wang XZ, Kwong S, Xu C (2017) Incorporating diversity and informativeness in multiple-instance active learning. IEEE Trans Fuzzy Syst 25(6):1460\u20131475","journal-title":"IEEE Trans Fuzzy Syst"},{"issue":"4","key":"1931_CR6","doi-asserted-by":"crossref","first-page":"1215","DOI":"10.1007\/s00521-017-3154-x","volume":"31","author":"N Deepa","year":"2019","unstructured":"Deepa N, Ganesan K (2019) Decision-making tool for crop selection for agriculture development. Neural Comput Appl 31(4):1215\u20131225","journal-title":"Neural Comput Appl"},{"issue":"5","key":"1931_CR7","doi-asserted-by":"crossref","first-page":"965","DOI":"10.1109\/TFUZZ.2020.2965896","volume":"28","author":"S Roy","year":"2020","unstructured":"Roy S, Maji P (2020) Medical image segmentation by partitioning spatially constrained fuzzy approximation spaces. IEEE Trans Fuzzy Syst 28(5):965\u2013977","journal-title":"IEEE Trans Fuzzy Syst"},{"key":"1931_CR8","doi-asserted-by":"crossref","first-page":"764","DOI":"10.1016\/j.asoc.2017.06.032","volume":"67","author":"R Cheruku","year":"2018","unstructured":"Cheruku R, Edla DR, Kuppili V, Dharavath R (2018) RST-BatMiner: a fuzzy rule miner integrating rough set feature selection and bat optimization for detection of diabetes disease. Appl Soft Comput 67:764\u2013780","journal-title":"Appl Soft Comput"},{"issue":"2","key":"1931_CR9","doi-asserted-by":"crossref","first-page":"515","DOI":"10.1142\/S0219622017500092","volume":"16","author":"CH Su","year":"2017","unstructured":"Su CH (2017) A novel hybrid learning achievement prediction model: A case study in gamification education applications (APPs). Int J Inform Technol Decis Mak 16(2):515\u2013543","journal-title":"Int J Inform Technol Decis Mak"},{"key":"1931_CR10","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1016\/j.eswa.2017.07.021","volume":"89","author":"F Zhou","year":"2017","unstructured":"Zhou F, Jiao JR, Yang XJ, Lei BY (2017) Augmenting feature model through customer preference mining by hybrid sentiment analysis. Expert Syst Appl 89:306\u2013317","journal-title":"Expert Syst Appl"},{"key":"1931_CR11","doi-asserted-by":"crossref","first-page":"748","DOI":"10.1016\/j.asoc.2016.09.050","volume":"52","author":"YC Ko","year":"2017","unstructured":"Ko YC, Fujita H, Li TR (2017) An evidential analysis of Altman Z-score for financial predictions: Case study on solar energy companies. Appl Soft Comput 52:748\u2013759","journal-title":"Appl Soft Comput"},{"key":"1931_CR12","doi-asserted-by":"crossref","first-page":"923","DOI":"10.1016\/j.asoc.2017.09.029","volume":"62","author":"L Lei","year":"2018","unstructured":"Lei L (2018) Wavelet neural network prediction method of stock price trend based on rough set attribute reduction. Appl Soft Comput 62:923\u2013932","journal-title":"Appl Soft Comput"},{"issue":"5","key":"1931_CR13","doi-asserted-by":"crossref","first-page":"806","DOI":"10.1109\/TFUZZ.2019.2939957","volume":"28","author":"XM Liu","year":"2020","unstructured":"Liu XM, Shen C, Wang W, Guan XH (2020) CoEvil: a coevolutionary model for crime inference based on fuzzy rough feature selection. IEEE Trans Fuzzy Syst 28(5):806\u2013817","journal-title":"IEEE Trans Fuzzy Syst"},{"key":"1931_CR14","doi-asserted-by":"crossref","first-page":"1141","DOI":"10.1007\/s13042-020-01089-4","volume":"11","author":"CC Zhang","year":"2020","unstructured":"Zhang CC, Dai JH, Chen JL (2020) Knowledge granularity based incremental attribute reduction for incomplete decision systems. Int J Mach Learn Cybern 11:1141\u20131157","journal-title":"Int J Mach Learn Cybern"},{"key":"1931_CR15","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2023.3263684","author":"WH Xu","year":"2023","unstructured":"Xu WH, Huang M, Jiang ZY, Qian YH (2023) Graph-based unsupervised feature selection for interval-valued information system. IEEE Trans Neural Netw Learn Syst. https:\/\/doi.org\/10.1109\/TNNLS.2023.3263684","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"1931_CR16","first-page":"1","volume":"2015","author":"YB Su","year":"2015","unstructured":"Su YB, Guo J, Li ZJ (2015) A simple fitness function for minimum attribute reduction. Comput Intell Neurosci 2015:1\u20136","journal-title":"Comput Intell Neurosci"},{"key":"1931_CR17","first-page":"15","volume":"397","author":"J Fan","year":"2017","unstructured":"Fan J, Jiang YL, Liu Y (2017) Quick attribute reduction with generalized indiscernibility models. Inf Sci 397:15\u201336","journal-title":"Inf Sci"},{"issue":"2","key":"1931_CR18","doi-asserted-by":"crossref","first-page":"317","DOI":"10.3233\/IDA-160807","volume":"20","author":"MS Lazo-Cortes","year":"2016","unstructured":"Lazo-Cortes MS, Martinez-Trinidad JF, Carrasco-Ochoa JA, Sanchez Diaz G (2016) A new algorithm for computing reducts based on the binary discernibility matrix. Intell Data Anal 20(2):317\u2013337","journal-title":"Intell Data Anal"},{"key":"1931_CR19","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.ins.2017.06.013","volume":"415","author":"J Konecny","year":"2017","unstructured":"Konecny J (2017) On attribute reduction in concept lattices: methods based on discernibility matrix are outperformed by basic clarification and reduction. Inf Sci 415:199\u2013212","journal-title":"Inf Sci"},{"key":"1931_CR20","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1016\/j.ins.2018.08.004","volume":"467","author":"J Konecny","year":"2018","unstructured":"Konecny J, Krajca P (2018) On attribute reduction in concept lattices: experimental evaluation shows discernibility matrix based methods inefficient. Inf Sci 467:431\u2013445","journal-title":"Inf Sci"},{"issue":"11","key":"1931_CR21","doi-asserted-by":"crossref","first-page":"1929","DOI":"10.1007\/s13042-017-0712-6","volume":"9","author":"CZ Wang","year":"2018","unstructured":"Wang CZ, He Q, Shao MW, Hu QH (2018) Feature selection based on maximal neighborhood discernibility. Int J Mach Learn Cybern 9(11):1929\u20131940","journal-title":"Int J Mach Learn Cybern"},{"issue":"11","key":"1931_CR22","first-page":"72","volume":"240","author":"JH Dai","year":"2013","unstructured":"Dai JH, Tian HW (2013) Entropy measures and granularity measures for set-valued information systems. Inf Sci 240(11):72\u201382","journal-title":"Inf Sci"},{"issue":"1","key":"1931_CR23","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1016\/j.asoc.2012.07.029","volume":"13","author":"JH Dai","year":"2013","unstructured":"Dai JH, Xu Q (2013) Attribute selection based on information gain ratio in fuzzy rough set theory with application to tumor classification. Appl Soft Comput 13(1):211\u2013221","journal-title":"Appl Soft Comput"},{"issue":"1","key":"1931_CR24","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1109\/TETCI.2022.3171784","volume":"7","author":"WH Xu","year":"2023","unstructured":"Xu WH, Yuan KH, Li WT, Ding WP (2023) An emerging fuzzy feature selection method using composite entropy-based uncertainty measure and data distribution. IEEE Trans Emerg Top Comput Intell 7(1):76\u201388","journal-title":"IEEE Trans Emerg Top Comput Intell"},{"issue":"2","key":"1931_CR25","doi-asserted-by":"crossref","first-page":"937","DOI":"10.1109\/TFUZZ.2017.2698420","volume":"26","author":"JH Dai","year":"2018","unstructured":"Dai JH, Hu QH, Hu H, Huang DB (2018) Neighbor inconsistent pair selection for attribute reduction by rough set approach. IEEE Trans Fuzzy Syst 26(2):937\u2013950","journal-title":"IEEE Trans Fuzzy Syst"},{"issue":"7","key":"1931_CR26","doi-asserted-by":"crossref","first-page":"1893","DOI":"10.1007\/s13042-018-0838-1","volume":"10","author":"A Bhattacharya","year":"2019","unstructured":"Bhattacharya A, Goswami RT, Mukherjee K (2019) A feature selection technique based on rough set and improvised PSO algorithm (PSORS-FS) for permission based detection of android malwares. Int J Mach Learn Cybern 10(7):1893\u20131907","journal-title":"Int J Mach Learn Cybern"},{"key":"1931_CR27","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1016\/j.knosys.2018.11.034","volume":"165","author":"KY Liu","year":"2019","unstructured":"Liu KY, Yang XB, Yu HL, Mi JS, Wang PX, Chen XJ (2019) Rough set based semi-supervised feature selection via ensemble selector. Knowl-Based Syst 165:282\u2013296","journal-title":"Knowl-Based Syst"},{"key":"1931_CR28","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/j.knosys.2018.10.038","volume":"164","author":"CZ Wang","year":"2019","unstructured":"Wang CZ, Huang Y, Shao MW, Fan XD (2019) Fuzzy rough set-based attribute reduction using distance measures. Knowl-Based Syst 164:205\u2013212","journal-title":"Knowl-Based Syst"},{"issue":"4","key":"1931_CR29","doi-asserted-by":"crossref","first-page":"2174","DOI":"10.1109\/TFUZZ.2017.2768044","volume":"26","author":"JH Dai","year":"2018","unstructured":"Dai JH, Hu H, Wu WZ, Qian YH, Huang DB (2018) Maximal-discernibility-pairs-based approach to attribute reduction in fuzzy rough sets. IEEE Trans Fuzzy Syst 26(4):2174\u20132187","journal-title":"IEEE Trans Fuzzy Syst"},{"issue":"5","key":"1931_CR30","doi-asserted-by":"crossref","first-page":"925","DOI":"10.1007\/s13042-017-0771-8","volume":"10","author":"FC Li","year":"2019","unstructured":"Li FC, Jin CX, Yang JN (2019) Roughness measure based on description ability for attribute reduction in information system. Int J Mach Learn Cybern 10(5):925\u2013934","journal-title":"Int J Mach Learn Cybern"},{"key":"1931_CR31","volume":"229","author":"Y Chen","year":"2021","unstructured":"Chen Y, Wang PX, Yang XB, Mi JS, Liu D (2021) Granular ball guided selector for attribute reduction. Knowl-Based Syst 229:107326","journal-title":"Knowl-Based Syst"},{"key":"1931_CR32","doi-asserted-by":"crossref","first-page":"1089","DOI":"10.1016\/j.ins.2022.06.022","volume":"607","author":"DD Guo","year":"2022","unstructured":"Guo DD, Jiang CM, Sheng RX, Liu SS (2022) A novel outcome evaluation model of three-way decision: a change viewpoint. Inf Sci 607:1089\u20131110","journal-title":"Inf Sci"},{"key":"1931_CR33","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.ins.2021.10.058","volume":"584","author":"KH Yuan","year":"2022","unstructured":"Yuan KH, Xu WH, Li WT, Ding WP (2022) An incremental learning mechanism for object classification based on progressive fuzzy three-way concept. Inf Sci 584:127\u2013147","journal-title":"Inf Sci"},{"key":"1931_CR34","doi-asserted-by":"publisher","DOI":"10.1109\/TFUZZ.2022.3216110","author":"WH Xu","year":"2022","unstructured":"Xu WH, Guo DD, Qian YH, Ding WP (2022) Two-way concept-cognitive learning method: a fuzzy-based progressive learning. IEEE Trans Fuzzy Syst. https:\/\/doi.org\/10.1109\/TFUZZ.2022.3216110","journal-title":"IEEE Trans Fuzzy Syst"},{"issue":"2","key":"1931_CR35","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1109\/TKDE.2012.146","volume":"26","author":"JY Liang","year":"2014","unstructured":"Liang JY, Wang F, Dang CY, Qian YH (2014) A group incremental approach to feature selection applying rough set technique. IEEE Trans Knowl Data Eng 26(2):294\u2013308","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"4","key":"1931_CR36","doi-asserted-by":"crossref","first-page":"825","DOI":"10.1109\/TFUZZ.2016.2581186","volume":"25","author":"YY Yang","year":"2017","unstructured":"Yang YY, Chen DG, Wang H (2017) Active sample selection based incremental algorithm for attribute reduction with rough sets. IEEE Trans Fuzzy Syst 25(4):825\u2013838","journal-title":"IEEE Trans Fuzzy Syst"},{"issue":"3","key":"1931_CR37","doi-asserted-by":"crossref","first-page":"1257","DOI":"10.1109\/TFUZZ.2017.2718492","volume":"26","author":"YY Yang","year":"2018","unstructured":"Yang YY, Chen DG, Wang H (2018) Incremental perspective for feature selection based on fuzzy rough sets. IEEE Trans Fuzzy Syst 26(3):1257\u20131273","journal-title":"IEEE Trans Fuzzy Syst"},{"key":"1931_CR38","first-page":"1","volume":"294","author":"FM Ma","year":"2019","unstructured":"Ma FM, Ding MW, Zhang TF (2019) Compressed binary discernibility matrix based incremental attribute reduction algorithm for group dynamic data. Neurocomputing 294:1\u201317","journal-title":"Neurocomputing"},{"issue":"5","key":"1931_CR39","doi-asserted-by":"crossref","first-page":"901","DOI":"10.1109\/TFUZZ.2019.2959995","volume":"28","author":"X Zhang","year":"2020","unstructured":"Zhang X, Mei CL, Chen DG, Yang YY, Li JH (2020) Active incremental feature selection using a fuzzy-rough-set-based information entropy. IEEE Trans Fuzzy Syst 28(5):901\u2013915","journal-title":"IEEE Trans Fuzzy Syst"},{"key":"1931_CR40","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1016\/j.ins.2023.01.058","volume":"625","author":"XY Zhang","year":"2023","unstructured":"Zhang XY, Li JR (2023) Incremental feature selection approach to interval-valued fuzzy decision information systems based on $$\\lambda$$-fuzzy similarity selfinformation. Inf Sci 625:593\u2013619","journal-title":"Inf Sci"},{"key":"1931_CR41","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.ijar.2016.05.001","volume":"76","author":"YG Jing","year":"2016","unstructured":"Jing YG, Li TR, Huang JF, Zhang YY (2016) An incremental attribute reduction approach based on knowledge granularity under the attribute generalization. Int J Approx Reason 76:80\u201395","journal-title":"Int J Approx Reason"},{"issue":"1","key":"1931_CR42","doi-asserted-by":"crossref","first-page":"676","DOI":"10.1016\/j.asoc.2012.07.018","volume":"13","author":"F Wang","year":"2013","unstructured":"Wang F, Liang JY, Dang CY (2013) Attribute reduction for dynamic data sets. Appl Soft Comput 13(1):676\u2013689","journal-title":"Appl Soft Comput"},{"issue":"9","key":"1931_CR43","doi-asserted-by":"crossref","first-page":"900","DOI":"10.1002\/int.21876","volume":"32","author":"YG Jing","year":"2017","unstructured":"Jing YG, Li TR, Huang JF, Chen HM, Horng SJ (2017) A group incremental reduction algorithm with varying data values. Int J Intell Syst 32(9):900\u2013925","journal-title":"Int J Intell Syst"},{"key":"1931_CR44","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.knosys.2017.10.033","volume":"140","author":"W Wei","year":"2018","unstructured":"Wei W, Wu XY, Liang JY, Cui JB, Sun YJ (2018) Discernibility matrix based incremental attribute reduction for dynamic data. Knowl-Based Syst 140:142\u2013157","journal-title":"Knowl-Based Syst"},{"key":"1931_CR45","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1016\/j.ins.2018.07.001","volume":"465","author":"YG Jing","year":"2018","unstructured":"Jing YG, Li TR, Fujita H, Wang BL, Cheng N (2018) An incremental attribute reduction method for dynamic data mining. Inf Sci 465:202\u2013218","journal-title":"Inf Sci"},{"key":"1931_CR46","doi-asserted-by":"publisher","DOI":"10.1109\/TBDATA.2022.3215494","author":"WH Xu","year":"2022","unstructured":"Xu WH, Pan YZ, Chen XW, Ding WP, Qian YH (2022) A novel dynamic fusion approach using information entropy for interval-valued ordered datasets. IEEE Trans Big Data. https:\/\/doi.org\/10.1109\/TBDATA.2022.3215494","journal-title":"IEEE Trans Big Data"},{"issue":"3","key":"1931_CR47","first-page":"391","volume":"29","author":"ZY Xu","year":"2006","unstructured":"Xu ZY, Liu ZP, Yang BR, Song W (2006) Quick attribute reduction algorithm with complexity of max $$(O(|C||U|), O(|C|^2|U\/C|))$$. Chin J Comput 29(3):391\u2013399","journal-title":"Chin J Comput"},{"key":"1931_CR48","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1016\/j.datak.2015.06.009","volume":"100","author":"WH Shu","year":"2015","unstructured":"Shu WH, Qian WB (2015) An incremental approach to attribute reduction from dynamic incomplete decision systems in rough set theory. Data Knowl Engi 100:116\u2013132","journal-title":"Data Knowl Engi"},{"key":"1931_CR49","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.knosys.2016.04.007","volume":"104","author":"YG Jing","year":"2016","unstructured":"Jing YG, Li TR, Luo C, Horng SJ, Wang GY, Yu Z (2016) An incremental approach for attribute reduction based on knowledge granularity. Knowl-Based Syst 104:24\u201338","journal-title":"Knowl-Based Syst"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-023-01931-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-023-01931-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-023-01931-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,10]],"date-time":"2024-02-10T10:23:52Z","timestamp":1707560632000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-023-01931-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,10]]},"references-count":49,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,3]]}},"alternative-id":["1931"],"URL":"https:\/\/doi.org\/10.1007\/s13042-023-01931-5","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"type":"print","value":"1868-8071"},{"type":"electronic","value":"1868-808X"}],"subject":[],"published":{"date-parts":[[2023,8,10]]},"assertion":[{"value":"15 December 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 July 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 August 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}