{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T12:28:35Z","timestamp":1780489715453,"version":"3.54.1"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2023,2,23]],"date-time":"2023-02-23T00:00:00Z","timestamp":1677110400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,2,23]],"date-time":"2023-02-23T00:00:00Z","timestamp":1677110400000},"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":["31772059"],"award-info":[{"award-number":["31772059"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Technology Development Plan of Jilin Province","award":["YDZJ202101ZYTS135"],"award-info":[{"award-number":["YDZJ202101ZYTS135"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2023,8]]},"DOI":"10.1007\/s13042-023-01797-7","type":"journal-article","created":{"date-parts":[[2023,2,23]],"date-time":"2023-02-23T16:03:05Z","timestamp":1677168185000},"page":"2761-2776","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Recognition of odor and pleasantness based on olfactory EEG combined with functional brain network model"],"prefix":"10.1007","volume":"14","author":[{"given":"Xiuxin","family":"Xia","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaotong","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenbo","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaofei","family":"Jia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bo","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0215-4399","authenticated-orcid":false,"given":"Yan","family":"Shi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hong","family":"Men","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,2,23]]},"reference":[{"key":"1797_CR1","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1016\/j.tifs.2008.11.004","volume":"20","author":"CF Ross","year":"2009","unstructured":"Ross CF (2009) Sensory science at the human\u2013machine interface. Trends Food Sci Technol 20:63\u201372. https:\/\/doi.org\/10.1016\/j.tifs.2008.11.004","journal-title":"Trends Food Sci Technol"},{"key":"1797_CR2","doi-asserted-by":"publisher","first-page":"481","DOI":"10.1039\/c6fo01099g","volume":"8","author":"E Scholten","year":"2017","unstructured":"Scholten E (2017) Composite foods: from structure to sensory perception. Food Funct 8:481\u2013497. https:\/\/doi.org\/10.1039\/c6fo01099g","journal-title":"Food Funct"},{"key":"1797_CR3","doi-asserted-by":"publisher","first-page":"3411","DOI":"10.1111\/1750-3841.14917","volume":"84","author":"H Yuan","year":"2019","unstructured":"Yuan H, Chen X, Shao Y, Cheng Y, Yang Y, Zhang M, Hua J, Li J, Deng Y, Wang J, Dong C, Jiang Y, Xie Z, Wu Z (2019) Quality evaluation of green and dark tea grade using electronic nose and multivariate statistical analysis, Food. Science 84:3411\u20133417. https:\/\/doi.org\/10.1111\/1750-3841.14917","journal-title":"Science"},{"key":"1797_CR4","doi-asserted-by":"publisher","unstructured":"Noble AC, Arnold RA, Masuda BM, Pecore SD, Schmidt JO, Stern PM (1984) Progress towards a standardized system of wine aroma terminology. Am J Enol Viticult 35:107\u2013109. https:\/\/doi.org\/10.5344\/ajev.1984.35.2.107","DOI":"10.5344\/ajev.1984.35.2.107"},{"key":"1797_CR5","doi-asserted-by":"publisher","first-page":"665","DOI":"10.1039\/c8fo01800f","volume":"10","author":"M Santagiuliana","year":"2019","unstructured":"Santagiuliana M, van den Hoek IAF, Stieger M, Scholten E, Piqueras-Fiszman B (2019) As good as expected? How consumer expectations and addition of vegetable pieces to soups influence sensory perception and liking. Food Function 10:665\u2013680. https:\/\/doi.org\/10.1039\/c8fo01800f","journal-title":"Food Function"},{"key":"1797_CR6","doi-asserted-by":"publisher","first-page":"2091","DOI":"10.3168\/jds.2006-823","volume":"90","author":"AJ Krause","year":"2007","unstructured":"Krause AJ, Lopetcharat K, Drake MA (2007) Identification of the characteristics that drive consumer liking of butter. J Dairy Sci 90:2091\u20132102. https:\/\/doi.org\/10.3168\/jds.2006-823","journal-title":"J Dairy Sci"},{"key":"1797_CR7","doi-asserted-by":"publisher","first-page":"11463","DOI":"10.1109\/JSEN.2022.3174251","volume":"22","author":"H Lin","year":"2022","unstructured":"Lin H, Chen H, Yin C,\u00a0Zhang Q, Li Z, Shi Y, Men H (2022) Lightweight residual convolutional neural network for soybean classification combined with electronic nose. IEEE Sens J 22:11463\u201311473. https:\/\/doi.org\/10.1109\/JSEN.2022.3174251","journal-title":"IEEE Sens J"},{"key":"1797_CR8","doi-asserted-by":"publisher","first-page":"112214","DOI":"10.1016\/j.postharvbio.2022.112214","volume":"197","author":"S Kang","year":"2023","unstructured":"Kang S, Zhang Q, Li Z, Yin C, Feng N, Shi Y (2023) Determination of the quality of tea from different picking periods: An adaptive pooling attention mechanism coupled with an electronic nose. Postharvest Biol Tec 197:112214. https:\/\/doi.org\/10.1016\/j.postharvbio.2022.112214","journal-title":"Postharvest Biol Tec"},{"key":"1797_CR9","doi-asserted-by":"publisher","first-page":"3637","DOI":"10.3233\/BME-141191","volume":"24","author":"S Xiong","year":"2014","unstructured":"Xiong S, Cheng C, Wu X, Guo X, Yao L, Zhang J (2014) Working memory training using EEG neurofeedback in normal young adults. Biomed Mater 24:3637\u20133644. https:\/\/doi.org\/10.3233\/BME-141191","journal-title":"Biomed Mater"},{"key":"1797_CR10","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.inffus.2018.09.001","volume":"49","author":"E Kanjo","year":"2019","unstructured":"Kanjo E, Younis EMG, Ang CS (2019) Deep learning analysis of mobile physiological, environmental and location sensor data for emotion detection. Inform Fusion 49:46\u201356. https:\/\/doi.org\/10.1016\/j.inffus.2018.09.001","journal-title":"Inform Fusion"},{"key":"1797_CR11","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1590\/S0044-59672009000200025","volume":"48","author":"H Jo","year":"2013","unstructured":"Jo H, Rodiek S, Fujii E, Miyazaki Y, Park BJ, Ann SW (2013) Physiological and psychological response to floral scent. HortScience 48:82\u201388. https:\/\/doi.org\/10.1590\/S0044-59672009000200025","journal-title":"HortScience"},{"key":"1797_CR12","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1016\/j.neulet.2016.09.037","volume":"633","author":"Y Zhang","year":"2016","unstructured":"Zhang Y, Ji X, Zhang S (2016) An approach to EEG-based emotion recognition using combined feature extraction method. Neurosci Lett 633:152\u2013157. https:\/\/doi.org\/10.1016\/j.neulet.2016.09.037","journal-title":"Neurosci Lett"},{"key":"1797_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2637287","volume":"11","author":"E Kroupi","year":"2014","unstructured":"Kroupi E, Yazdani A, Vesin J-M, Ebrahimi T (2014) EEG correlates of pleasant and unpleasant odor perception. ACM Trans Multimed Comput Commun Appl 11:1\u201317. https:\/\/doi.org\/10.1145\/2637287","journal-title":"ACM Trans Multimed Comput Commun Appl"},{"key":"1797_CR14","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1007\/978-3-319-98998-3_10","volume":"885","author":"MA BecerraEmail","year":"2018","unstructured":"BecerraEmail MA, Londo\u00f1o-Delgado E, Pelaez-Becerra SM, Serna-Guar\u00edn L, Castro-Ospina AE, Marin-Castrill\u00f3n D, Peluffo-Ord\u00f3\u00f1ez DH (2018) Odor pleasantness classification from electroencephalographic signals and emotional states, communications in computer and information. Science 885:128\u2013138. https:\/\/doi.org\/10.1007\/978-3-319-98998-3_10","journal-title":"Science"},{"key":"1797_CR15","doi-asserted-by":"publisher","first-page":"717","DOI":"10.1109\/THMS.2014.2344003","volume":"44","author":"A Saha","year":"2014","unstructured":"Saha A, Konar A, Chatterjee A, Ralescu A, Nagar AK (2014) EEG analysis for olfactory perceptual-ability measurement using a recurrent neural classifier. IEEE Transact Human-Mach Syst 44:717\u2013730. https:\/\/doi.org\/10.1109\/THMS.2014.2344003","journal-title":"IEEE Transact Human-Mach Syst"},{"key":"1797_CR16","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1016\/j.neunet.2019.04.003","volume":"116","author":"Z Liang","year":"2019","unstructured":"Liang Z, Oba S, Ishii S (2019) An unsupervised EEG decoding system for human emotion recognition. Neural Netw 116:257\u2013268. https:\/\/doi.org\/10.1016\/j.neunet.2019.04.003","journal-title":"Neural Netw"},{"key":"1797_CR17","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1007\/s10044-016-0567-6","volume":"21","author":"A Mert","year":"2016","unstructured":"Mert A, Akan A (2016) Emotion recognition from EEG signals by using multivariate empirical mode decomposition. Pattern Anal Appl 21:81\u201389. https:\/\/doi.org\/10.1007\/s10044-016-0567-6","journal-title":"Pattern Anal Appl"},{"key":"1797_CR18","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1109\/tnn.2010.2084099","volume":"22","author":"H Zhang","year":"2011","unstructured":"Zhang H, Chin ZY, Ang KK, Guan C, Wang C (2011) Optimum spatio-spectral filtering network for brain\u2013computer interface. IEEE Transact Neural Networks 22:52\u201363. https:\/\/doi.org\/10.1109\/tnn.2010.2084099","journal-title":"IEEE Transact Neural Networks"},{"key":"1797_CR19","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1016\/j.rvsc.2010.08.006","volume":"91","author":"PL Geenen","year":"2011","unstructured":"Geenen PL, van der Gaag LC, Loeffen WLA, Elbers ARW (2011) Constructing naive Bayesian classifiers for veterinary medicine: a case study in the clinical diagnosis of classical swine fever. Res Vet Sci 91:64\u201371. https:\/\/doi.org\/10.1016\/j.rvsc.2010.08.006","journal-title":"Res Vet Sci"},{"key":"1797_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s12559-014-9264-1","volume":"6","author":"L Duan","year":"2014","unstructured":"Duan L, Zhong H, Miao J, Yang Z, Ma W, Zhang X (2014) A voting optimized strategy based on ELM for improving classification of motor imagery BCI data. Cogn Comput 6:1\u20137. https:\/\/doi.org\/10.1007\/s12559-014-9264-1","journal-title":"Cogn Comput"},{"key":"1797_CR21","doi-asserted-by":"publisher","first-page":"242","DOI":"10.1016\/j.procs.2018.10.392","volume":"143","author":"A Bablani","year":"2018","unstructured":"Bablani A, Edla DR, Dodia S (2018) Classification of EEG data using k-Nearest neighbor approach for concealed information test. Proc Comput Sci 143:242\u2013249. https:\/\/doi.org\/10.1016\/j.procs.2018.10.392","journal-title":"Proc Comput Sci"},{"key":"1797_CR22","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1007\/s10916-006-9043-y","volume":"31","author":"RK Sinha","year":"2007","unstructured":"Sinha RK, Aggarwal Y, Das BN (2007) Backpropagation artificial neural network detects changes in electro-encephalogram power spectra of syncopic patients. J Med Syst 31:63\u201368. https:\/\/doi.org\/10.1007\/s10916-006-9043-y","journal-title":"J Med Syst"},{"key":"1797_CR23","doi-asserted-by":"publisher","first-page":"S2","DOI":"10.1016\/j.ijpsycho.2021.07.007","volume":"168","author":"PM Rossini","year":"2021","unstructured":"Rossini PM (2021) Brain network analysis via graph-theory on E.E.G. recordings in physiological and pathological brain aging. Int J Psychophysiol 168:S2\u2013S3. https:\/\/doi.org\/10.1016\/j.ijpsycho.2021.07.007","journal-title":"Int J Psychophysiol"},{"key":"1797_CR24","doi-asserted-by":"publisher","first-page":"1957","DOI":"10.1109\/TMI.2019.2962517","volume":"39","author":"P Sundaram","year":"2020","unstructured":"Sundaram P, Luessi M, Bianciardi M, Stufflebeam S, Hamalainen M, Solo V (2020) Individual resting-state brain networks enabled by massive multivariate conditional mutual information. IEEE Trans Med Imaging 39:1957\u20131966. https:\/\/doi.org\/10.1109\/TMI.2019.2962517","journal-title":"IEEE Trans Med Imaging"},{"key":"1797_CR25","doi-asserted-by":"publisher","first-page":"566","DOI":"10.1016\/j.physa.2016.10.068","volume":"468","author":"H Yu","year":"2016","unstructured":"Yu H, Liu J, Cai L, Wang J, Cao Y, Hao C (2016) Functional brain networks in healthy subjects under acupuncture stimulation: an EEG study based on nonlinear synchronization likelihood analysis. Physica A 468:566\u2013577. https:\/\/doi.org\/10.1016\/j.physa.2016.10.068","journal-title":"Physica A"},{"key":"1797_CR26","doi-asserted-by":"publisher","first-page":"569","DOI":"10.1007\/s11571-018-9495-z","volume":"12","author":"J Chen","year":"2018","unstructured":"Chen J, Wang H, Hua C, Wang Q, Liu C (2018) Graph analysis of functional brain network topology using minimum spanning tree in driver drowsiness. Cogn Neurodyn 12:569\u2013580. https:\/\/doi.org\/10.1007\/s11571-018-9495-z","journal-title":"Cogn Neurodyn"},{"key":"1797_CR27","doi-asserted-by":"publisher","first-page":"93711","DOI":"10.1109\/ACCESS.2019.2927768","volume":"7","author":"Z Wang","year":"2019","unstructured":"Wang Z, Tong Y, Heng X (2019) Phase-locking value based graph convolutional neural networks for emotion recognition. IEEE Access 7:93711\u201393722. https:\/\/doi.org\/10.1109\/ACCESS.2019.2927768","journal-title":"IEEE Access"},{"key":"1797_CR28","doi-asserted-by":"publisher","first-page":"170928","DOI":"10.1109\/ACCESS.2020.3025370","volume":"8","author":"AD Marimpis","year":"2020","unstructured":"Marimpis AD, Dimitriadis SI, Goebel R (2020) A multiplex connectivity map of valence-arousal emotional model. IEEE Access 8:170928\u2013170938. https:\/\/doi.org\/10.1109\/ACCESS.2020.3025370","journal-title":"IEEE Access"},{"key":"1797_CR29","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1016\/j.medengphy.2015.01.002","volume":"37","author":"U Melia","year":"2015","unstructured":"Melia U, Guaita M, Vallverd\u00fa M, Embid C, Vilaseca I, Salamero M, Santamaria J (2015) Mutual information measures applied to EEG signals for sleepiness characterization. Med Eng Phys 37:297\u2013308. https:\/\/doi.org\/10.1016\/j.medengphy.2015.01.002","journal-title":"Med Eng Phys"},{"key":"1797_CR30","doi-asserted-by":"publisher","first-page":"638","DOI":"10.1002\/hbm.20530","volume":"30","author":"L Wang","year":"2010","unstructured":"Wang L, Zhu C, He Y, Zang Y, Cao Q, Zhang H, Zhong Q, Wang Y (2010) Altered small-world brain functional networks in children with attention-deficit\/hyperactivity disorder. Hum Brain Mapp 30:638\u2013649. https:\/\/doi.org\/10.1002\/hbm.20530","journal-title":"Hum Brain Mapp"},{"key":"1797_CR31","doi-asserted-by":"publisher","first-page":"651","DOI":"10.1016\/j.neuroimage.2015.05.046","volume":"118","author":"KA Garrison","year":"2015","unstructured":"Garrison KA, Scheinost D, Finn ES, Shen X, Constable RT (2015) The (in) stability of functional brain network measures across thresholds. Neuroimage 118:651\u2013661. https:\/\/doi.org\/10.1016\/j.neuroimage.2015.05.046","journal-title":"Neuroimage"},{"key":"1797_CR32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2020\/8851415","volume":"24","author":"Y Liang","year":"2020","unstructured":"Liang Y, Chen C, Li F, Yao D, Xu P, Yu L (2020) Altered functional connectivity after epileptic seizure revealed by scalp EEG. Neural Plast 24:1\u20138. https:\/\/doi.org\/10.1155\/2020\/8851415","journal-title":"Neural Plast"},{"key":"1797_CR33","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1016\/j.ijpsycho.2015.02.011","volume":"103","author":"CJ Stam","year":"2016","unstructured":"Stam CJ, van Straaten EC, Van Dellen E, Tewarie P, Gong G, Hillebrand A, Meier J, Van Mieghem P (2016) The relation between structural and functional connectivity patterns in complex brain networks. Int J Psychophysiol 103:149\u2013160. https:\/\/doi.org\/10.1016\/j.ijpsycho.2015.02.011","journal-title":"Int J Psychophysiol"},{"key":"1797_CR34","doi-asserted-by":"publisher","first-page":"694","DOI":"10.1097\/PHM.0000000000001395","volume":"99","author":"F Molteni","year":"2020","unstructured":"Molteni F, Formaggio E, Bosco A, Guanziroli E, Piccione F, Masiero S, Del Felice A (2020) Brain connectivity modulation after exoskeleton-assisted gait in chronic hemiplegic stroke survivors: a pilot study. Am J Phys Med Rehabil 99:694\u2013700. https:\/\/doi.org\/10.1097\/PHM.0000000000001395","journal-title":"Am J Phys Med Rehabil"},{"key":"1797_CR35","doi-asserted-by":"publisher","first-page":"949","DOI":"10.1109\/JBHI.2016.2578954","volume":"21","author":"S Afshari","year":"2016","unstructured":"Afshari S, Jalili M (2016) Directed functional networks in Alzheimer\u2019s disease: disruption of global and local connectivity measures. IEEE J Biomed Health Inform 21:949\u2013955. https:\/\/doi.org\/10.1109\/JBHI.2016.2578954","journal-title":"IEEE J Biomed Health Inform"},{"key":"1797_CR36","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1162\/NECO_a_00914","volume":"29","author":"Y Wang","year":"2017","unstructured":"Wang Y, Ghumare E, Vandenberghe R, Dupont P (2017) Comparison of different generalizations of clustering coefficient and local efficiency for weighted undirected graphs. Neural Comput 29:313\u2013331. https:\/\/doi.org\/10.1162\/NECO_a_00914","journal-title":"Neural Comput"},{"key":"1797_CR37","doi-asserted-by":"publisher","first-page":"29780","DOI":"10.1038\/srep29780","volume":"6","author":"M Jalili","year":"2016","unstructured":"Jalili M (2016) Functional brain networks: does the choice of dependency estimator and binarization method matter? Sci Rep 6:29780. https:\/\/doi.org\/10.1038\/srep29780","journal-title":"Sci Rep"},{"key":"1797_CR38","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1007\/s10548-013-0277-y","volume":"27","author":"MM Shafi","year":"2014","unstructured":"Shafi MM, Brandon Westover M, Oberman L, Cash SS, Pascual-Leone A (2014) Modulation of EEG functional connectivity networks in subjects undergoing repetitive transcranial magnetic stimulation. Brain Topogr 27:172\u2013191. https:\/\/doi.org\/10.1007\/s10548-013-0277-y","journal-title":"Brain Topogr"},{"key":"1797_CR39","doi-asserted-by":"publisher","first-page":"1004","DOI":"10.1016\/j.saa.2014.08.011","volume":"137","author":"M Ghaedi","year":"2015","unstructured":"Ghaedi M, Ansari A, Bahari F, Ghaedi AM, Vafaei A (2015) A Hybrid artificial neural network and particle swarm optimization for prediction of removal of hazardous dye brilliant green from aqueous solution using zinc sulfide nanoparticle loaded on activated carbon. Spectrochim Acta Part A Mol Biomol Spectrosc 137:1004\u20131015. https:\/\/doi.org\/10.1016\/j.saa.2014.08.011","journal-title":"Spectrochim Acta Part A Mol Biomol Spectrosc"},{"key":"1797_CR40","doi-asserted-by":"publisher","first-page":"639","DOI":"10.1016\/j.saa.2014.04.175","volume":"132","author":"M Ghaedi","year":"2014","unstructured":"Ghaedi M, Ghaedi AM, Ansari A, Mohammadi F, Vafaei A (2014) Artificial neural network and particle swarm optimization for removal of methyl orange by gold nanoparticles loaded on activated carbon and tamarisk. Spectrochim Acta Part A Mol Biomol Spectrosc 132:639\u2013654. https:\/\/doi.org\/10.1016\/j.saa.2014.04.175","journal-title":"Spectrochim Acta Part A Mol Biomol Spectrosc"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-023-01797-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-023-01797-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-023-01797-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,17]],"date-time":"2023-06-17T08:24:47Z","timestamp":1686990287000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-023-01797-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,23]]},"references-count":40,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2023,8]]}},"alternative-id":["1797"],"URL":"https:\/\/doi.org\/10.1007\/s13042-023-01797-7","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,23]]},"assertion":[{"value":"17 April 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 February 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 February 2023","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 that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}