{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T11:02:43Z","timestamp":1740135763636,"version":"3.37.3"},"reference-count":54,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2023,11,22]],"date-time":"2023-11-22T00:00:00Z","timestamp":1700611200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,22]],"date-time":"2023-11-22T00:00:00Z","timestamp":1700611200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100005412","name":"Shahid Chamran University of Ahvaz","doi-asserted-by":"publisher","award":["SCU.EE99.82"],"award-info":[{"award-number":["SCU.EE99.82"]}],"id":[{"id":"10.13039\/501100005412","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Circuits Syst Signal Process"],"published-print":{"date-parts":[[2024,3]]},"DOI":"10.1007\/s00034-023-02543-8","type":"journal-article","created":{"date-parts":[[2023,11,22]],"date-time":"2023-11-22T08:02:32Z","timestamp":1700640152000},"page":"1862-1888","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Bayesian Structured-Sparse Modeling Using a Bernoulli\u2013Laplacian Prior"],"prefix":"10.1007","volume":"43","author":[{"given":"Mayadeh","family":"Kouti","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1655-131X","authenticated-orcid":false,"given":"Karim","family":"Ansari-Asl","sequence":"additional","affiliation":[]},{"given":"Ehsan","family":"Namjoo","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,11,22]]},"reference":[{"key":"2543_CR1","doi-asserted-by":"publisher","first-page":"1982","DOI":"10.1109\/TIT.2010.2040894","volume":"56","author":"RG Baraniuk","year":"2010","unstructured":"R.G. Baraniuk, V. Cevher, M.F. Duarte, C. Hegde, Model-based compressive sensing. IEEE Trans. Inf. Theory 56, 1982\u20132001 (2010)","journal-title":"IEEE Trans. Inf. Theory"},{"key":"2543_CR2","first-page":"877","volume":"3","author":"T Blumensath","year":"2007","unstructured":"T. Blumensath, M. Yaghoobi, M.E. Davies, Iterative hard thresholding and l0 regularisation. IEEE Int. Conf. Acoust. Speech Signal Process ICASSP. 3, 877\u2013880 (2007)","journal-title":"IEEE Int. Conf. Acoust. Speech Signal Process ICASSP."},{"key":"2543_CR3","doi-asserted-by":"crossref","unstructured":"R. Bousseljot, D. Kreiseler, A. Schnabel: Nutzung der EKG-Signaldatenbank CARDIODAT der PTB \u00fcber das Internet. 317\u2013318 (1995).","DOI":"10.1515\/bmte.1995.40.s1.317"},{"key":"2543_CR4","doi-asserted-by":"publisher","first-page":"4203","DOI":"10.1109\/TIT.2005.858979","volume":"51","author":"EJ Candes","year":"2005","unstructured":"E.J. Candes, T. Tao, Decoding by linear programming. IEEE Trans. Inf. Theory 51, 4203\u20134215 (2005)","journal-title":"IEEE Trans. Inf. Theory"},{"key":"2543_CR5","doi-asserted-by":"publisher","first-page":"589","DOI":"10.1016\/j.crma.2008.03.014","volume":"346","author":"EJ Candes","year":"2008","unstructured":"E.J. Candes, The restricted isometry property and its implications for compressed sensing. Comptes Rendus Math. 346, 589\u2013592 (2008)","journal-title":"Comptes Rendus Math."},{"key":"2543_CR6","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1109\/MSP.2007.914731","volume":"25","author":"EJ Candes","year":"2008","unstructured":"E.J. Candes, M.B. Wakin, An introduction to compressive sampling. IEEE Signal Process. Mag. 25, 21\u201330 (2008)","journal-title":"IEEE Signal Process. Mag."},{"key":"2543_CR7","unstructured":"L. Chaari, J.-Y. Tourneret, H. Batatia: Sparse bayesian regularization using bernoulli-laplacian priors. In: Signal Process. Conf. (EUSIPCO). 1\u20135 (2013)."},{"key":"2543_CR8","doi-asserted-by":"crossref","unstructured":"L. Chaari, H. Batatia, J.-Y. Tourneret: Sparse Bayesian image restoration with linear operator uncertainties with application to EEG signal recovery. Midd. East Conf. Biomed. Eng. (MECBME). 139\u2013142 (2014).","DOI":"10.1109\/MECBME.2014.6783225"},{"key":"2543_CR9","doi-asserted-by":"crossref","unstructured":"R. Chartrand, W. Yin Iteratively reweighted algorithms for compressive sensing. IEEE Int. Conf. Acoust. Speech Signal Process. (ICASSP). 3869\u20133872 (2008).","DOI":"10.1109\/ICASSP.2008.4518498"},{"key":"2543_CR10","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1137\/S003614450037906X","volume":"43","author":"SS Chen","year":"2001","unstructured":"S.S. Chen, D.L. Donoho, M.A. Saunders, Atomic decomposition by basis pursuit. SIAM Rev. 43, 129\u2013159 (2001)","journal-title":"SIAM Rev."},{"key":"2543_CR11","doi-asserted-by":"publisher","first-page":"2888","DOI":"10.1109\/TBME.2015.2450015","volume":"62","author":"F Costa","year":"2015","unstructured":"F. Costa, H. Batatia, L. Chaari, J.-Y. Tourneret, Sparse EEG source localization using bernoulli laplacian priors. IEEE Trans. Biomed. Eng. 62, 2888\u20132898 (2015)","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"2543_CR12","doi-asserted-by":"crossref","unstructured":"F. Costa, H. Batatia, T. Oberlin, J.-Y. Tourneret EEG source localization based on a structured sparsity prior and a partially collapsed Gibbs sampler. IEEE Int. Work. Comput. Adv. Multi-Sensor Adapt. Process. (CAMSAP). 261\u2013264 (2015).","DOI":"10.1109\/CAMSAP.2015.7383786"},{"key":"2543_CR13","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1016\/j.neuroimage.2016.08.064","volume":"144","author":"F Costa","year":"2017","unstructured":"F. Costa, H. Batatia, T. Oberlin, C. Dgiano, J.-Y. Tourneret, Bayesian EEG source localization using a structured sparsity prior. Neuroimage 144, 142\u2013152 (2017)","journal-title":"Neuroimage"},{"key":"2543_CR14","doi-asserted-by":"publisher","first-page":"1413","DOI":"10.1002\/cpa.20042","volume":"57","author":"I Daubechies","year":"2004","unstructured":"I. Daubechies, M. Defrise, C. De Mol, An iterative thresholding algorithm for linear inverse problems with a sparsity constraint. Commun. Pure Appl. Math. 57, 1413\u20131457 (2004)","journal-title":"Commun. Pure Appl. Math."},{"key":"2543_CR15","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1109\/TBCAS.2012.2193668","volume":"6","author":"AM Dixon","year":"2012","unstructured":"A.M. Dixon, E.G. Allstot, D. Gangopadhyay, D.J. Allstot, Compressed sensing system considerations for ECG and EMG wireless biosensors. IEEE Trans. Biomed. Circuits Syst. 6, 156\u2013166 (2012)","journal-title":"IEEE Trans. Biomed. Circuits Syst."},{"key":"2543_CR16","doi-asserted-by":"publisher","first-page":"2059","DOI":"10.1109\/TIP.2009.2024067","volume":"18","author":"N Dobigeon","year":"2009","unstructured":"N. Dobigeon, A.O. Hero, J.-Y. Tourneret, Hierarchical Bayesian sparse image reconstruction with application to MRFM. IEEE Trans. Image Process. 18, 2059\u20132070 (2009)","journal-title":"IEEE Trans. Image Process."},{"key":"2543_CR17","doi-asserted-by":"publisher","first-page":"5302","DOI":"10.1109\/TIT.2009.2030471","volume":"55","author":"YC Eldar","year":"2009","unstructured":"Y.C. Eldar, M. Mishali, Robust recovery of signals from a structured union of subspaces. IEEE Trans. Inf. Theory 55, 5302\u20135316 (2009)","journal-title":"IEEE Trans. Inf. Theory"},{"key":"2543_CR18","doi-asserted-by":"publisher","first-page":"3042","DOI":"10.1109\/TSP.2010.2044837","volume":"58","author":"YC Eldar","year":"2010","unstructured":"Y.C. Eldar, P. Kuppinger, H. B\u00f6lcskei, Block-sparse signals: Uncertainty relations and efficient recovery. IEEE Trans. Signal Process. 58, 3042\u20133054 (2010)","journal-title":"IEEE Trans. Signal Process."},{"key":"2543_CR19","doi-asserted-by":"publisher","first-page":"360","DOI":"10.1109\/TSP.2014.2375133","volume":"63","author":"J Fang","year":"2014","unstructured":"J. Fang, Y. Shen, H. Li, P. Wang, Pattern-coupled sparse Bayesian learning for recovery of block-sparse signals. IEEE Trans. Signal Process. 63, 360\u2013372 (2014)","journal-title":"IEEE Trans. Signal Process."},{"key":"2543_CR20","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1161\/01.CIR.101.23.e215","volume":"101","author":"AL Goldberger","year":"2000","unstructured":"A.L. Goldberger, L.A. Amaral, L. Glass, J.M. Hausdorff, P.C. Ivanov, R.G. Mark, J.E. Mietus, G.B. Moody, C.-K. Peng, H.E. Stanley, Physiobank, physiotoolkit, and physionet components of a new research resource for complex physiologic signals. Circulation 101, 215\u2013220 (2000)","journal-title":"Circulation"},{"key":"2543_CR21","doi-asserted-by":"publisher","first-page":"1937","DOI":"10.1088\/0031-9155\/57\/7\/1937","volume":"57","author":"A Gramfort","year":"2012","unstructured":"A. Gramfort, M. Kowalski, M. H\u00e4m\u00e4l\u00e4inen, Mixed-norm estimates for the M\/EEG inverse problem using accelerated gradient methods. Phys. Med. Biol. 57, 1937 (2012)","journal-title":"Phys. Med. Biol."},{"key":"2543_CR22","doi-asserted-by":"publisher","first-page":"3488","DOI":"10.1109\/TSP.2009.2022003","volume":"57","author":"L He","year":"2009","unstructured":"L. He, L. Carin, Exploiting structure in wavelet-based Bayesian compressive sensing. IEEE Trans. Signal Process. 57, 3488\u20133497 (2009)","journal-title":"IEEE Trans. Signal Process."},{"key":"2543_CR23","first-page":"233","volume":"17","author":"L He","year":"2009","unstructured":"L. He, H. Chen, L. Carin, Tree-structured compressive sensing with variational Bayesian analysis. IEEE Signal Process. Lett. 17, 233\u2013236 (2009)","journal-title":"IEEE Signal Process. Lett."},{"key":"2543_CR24","doi-asserted-by":"publisher","first-page":"1978","DOI":"10.1214\/09-AOS778","volume":"38","author":"J Huang","year":"2010","unstructured":"J. Huang, T. Zhang, The benefit of group sparsity. Ann. Stat. 38, 1978\u20132004 (2010)","journal-title":"Ann. Stat."},{"key":"2543_CR25","doi-asserted-by":"crossref","unstructured":"J. Huang, T. Zhang, D. Metaxas, learning with structured sparsity. Proc. Ann. Int. Conf. Mach. Learn. 417\u2013424 (2009).","DOI":"10.1145\/1553374.1553429"},{"key":"2543_CR26","doi-asserted-by":"publisher","first-page":"800","DOI":"10.1049\/el:20080522","volume":"44","author":"Q Huynh-Thu","year":"2008","unstructured":"Q. Huynh-Thu, M. Ghanbari, Scope of validity of PSNR in image\/video quality assessment. Electron. Lett. 44, 800\u2013801 (2008)","journal-title":"Electron. Lett."},{"key":"2543_CR27","doi-asserted-by":"publisher","first-page":"2346","DOI":"10.1109\/TSP.2007.914345","volume":"56","author":"S Ji","year":"2008","unstructured":"S. Ji, Y. Xue, L. Carin, Bayesian compressive sensing. IEEE Trans. Signal Process. 56, 2346\u20132356 (2008)","journal-title":"IEEE Trans. Signal Process."},{"key":"2543_CR28","doi-asserted-by":"crossref","unstructured":"M. Korki, J. Zhangy, C. Zhang, H. Zayyani, An iterative Bayesian algorithm for block-sparse signal reconstruction. IEEE Int. Conf. Acoust. Speech Signal Process. (ICASSP). 2174\u20132178 (2015).","DOI":"10.1109\/ICASSP.2015.7178356"},{"key":"2543_CR29","doi-asserted-by":"publisher","first-page":"2498","DOI":"10.1109\/TSP.2013.2250967","volume":"61","author":"M Kowalski","year":"2013","unstructured":"M. Kowalski, K. Siedenburg, M. D\u00f6rfler, Social sparsity! neighborhood systems enrich structured shrinkage operators. IEEE Trans. Signal Process. 61, 2498\u20132511 (2013)","journal-title":"IEEE Trans. Signal Process."},{"key":"2543_CR30","doi-asserted-by":"publisher","first-page":"1371","DOI":"10.1109\/TSP.2011.2105478","volume":"59","author":"X Lv","year":"2011","unstructured":"X. Lv, G. Bi, C. Wan, The group lasso for stable recovery of block-sparse signal representations. IEEE Trans. Signal Process. 59, 1371\u20131382 (2011)","journal-title":"IEEE Trans. Signal Process."},{"key":"2543_CR31","doi-asserted-by":"publisher","first-page":"1023","DOI":"10.1080\/01621459.1988.10478694","volume":"83","author":"TJ Mitchell","year":"1988","unstructured":"T.J. Mitchell, J.J. Beauchamp, Bayesian variable selection in linear regression. J. Am. Stat. Assoc. 83, 1023\u20131032 (1988)","journal-title":"J. Am. Stat. Assoc."},{"key":"2543_CR32","first-page":"1","volume":"1","author":"A Mohammad-Djafari","year":"2012","unstructured":"A. Mohammad-Djafari, Bayesian approach with prior models which enforce sparsity in signal and image processing. E EURASIP J. Adv. Signal Process. 1, 1\u201319 (2012)","journal-title":"E EURASIP J. Adv. Signal Process."},{"key":"2543_CR33","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1016\/j.acha.2008.07.002","volume":"26","author":"D Needell","year":"2009","unstructured":"D. Needell, J.A. Tropp, CoSaMP: Iterative signal recovery from incomplete and inaccurate samples. Appl. Comput. Harmon. Anal. 26, 301\u2013321 (2009)","journal-title":"Appl. Comput. Harmon. Anal."},{"key":"2543_CR34","first-page":"501","volume":"9","author":"NG Polson","year":"2010","unstructured":"N.G. Polson, J.G. Scott, Shrink globally, act locally: sparse Bayesian regularization and prediction. Bayesian Stat. 9, 501\u2013538 (2010)","journal-title":"Bayesian Stat."},{"key":"2543_CR35","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1016\/j.sigpro.2016.06.027","volume":"130","author":"J Ren","year":"2017","unstructured":"J. Ren, C. Wei, L. Yu, H. Zhang, H. Sun, Dynamic recovery for block sparse signals. Signal Process. 130, 197\u2013203 (2017)","journal-title":"Signal Process."},{"key":"2543_CR36","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4757-3071-5","volume-title":"Monte Carlo statistical methods","author":"CP Robert","year":"1999","unstructured":"C.P. Robert, G. Casella, G. Casella, Monte Carlo statistical methods (Springer, New York, 1999)"},{"key":"2543_CR37","doi-asserted-by":"crossref","unstructured":"A. Salman, E. G. Allstot, A. Y. Chen, A. M. Dixon, D. Gangopadhyay, D. J. Allstot, Compressive sampling of EMG bio-signals. IEEE Int. Symp. Circuits Syst. (ISCAS). 2095\u20132098 (2011).","DOI":"10.1109\/ISCAS.2011.5938011"},{"key":"2543_CR38","doi-asserted-by":"crossref","unstructured":"M. Shekaramiz, T. K. Moon, J. H. Gunther AMP-B-SBL, An algorithm for clustered sparse signals using approximate message passing. IEEE Ann. Ubiquit. Comput. Elec. Mob. Comm. Conf. (UEMCON). 1\u20135 (2016).","DOI":"10.1109\/UEMCON.2016.7777899"},{"key":"2543_CR39","doi-asserted-by":"crossref","unstructured":"M. Shekaramiz, T. K. Moon, J. H. Gunther, Sparse Recovery Via Variational Bayesian Inference: Comparing Bernoullis-Gaussians-Inverse Gamma And Gaussians-Inverse Gammas Modeling. IEEE Asilomar Conf. Signal. Syst. Comput. 1969\u20131973: (2018).","DOI":"10.1109\/ACSSC.2018.8645341"},{"key":"2543_CR40","doi-asserted-by":"publisher","first-page":"247","DOI":"10.3390\/e21030247","volume":"21","author":"M Shekaramiz","year":"2019","unstructured":"M. Shekaramiz, T.K. Moon, J.H. Gunther, Bayesian compressive sensing of sparse signals with unknown clustering patterns. Entropy 21, 247 (2019)","journal-title":"Entropy"},{"key":"2543_CR41","doi-asserted-by":"crossref","unstructured":"M. Shekaramiz, T. K. Moon, Compressive Sensing via Variational Bayesian Inference. IEEE Intermount. Eng. Tech. Comput. (IETC). 1\u20136: (2020).","DOI":"10.1109\/IETC47856.2020.9249197"},{"key":"2543_CR42","unstructured":"G. Swirszcz, N. Abe, A. C. Lozano, Grouped orthogonal matching pursuit for variable selection and prediction. Adv. Neural Inf. Process. Syst. 1150\u20131158 (2009)."},{"key":"2543_CR43","doi-asserted-by":"publisher","first-page":"4655","DOI":"10.1109\/TIT.2007.909108","volume":"53","author":"JA Tropp","year":"2007","unstructured":"J.A. Tropp, A.C. Gilbert, Signal recovery from random measurements via orthogonal matching pursuit. IEEE Trans. Inf. Theory 53, 4655\u20134666 (2007)","journal-title":"IEEE Trans. Inf. Theory"},{"key":"2543_CR44","doi-asserted-by":"publisher","first-page":"890","DOI":"10.1137\/080714488","volume":"31","author":"E Van Den Berg","year":"2009","unstructured":"E. Van Den Berg, M.P. Friedlander, Probing the Pareto frontier for basis pursuit solutions. SIAM J. Sci. Comput. 31, 890\u2013912 (2009)","journal-title":"SIAM J. Sci. Comput."},{"key":"2543_CR45","unstructured":"S. Viswanath, M. Ghulyani, M. Arigovindan, Structurally adaptive multi-derivative regularization for image recovery from sparse fourier samples. arXiv preprint arXiv:2105, 12775 (2021)."},{"key":"2543_CR46","doi-asserted-by":"publisher","DOI":"10.1016\/j.sigpro.2019.107255","volume":"166","author":"L Wang","year":"2020","unstructured":"L. Wang, L. Zhao, L. Yu, J. Wang, G. Bi, Structured Bayesian learning for recovery of clustered sparse signal. Signal Process. 166, 107255 (2020)","journal-title":"Signal Process."},{"key":"2543_CR47","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1162\/neco.1995.7.1.117","volume":"7","author":"PM Williams","year":"1995","unstructured":"P.M. Williams, Bayesian regularization and pruning using a Laplace prior. Neural Comput. 7, 117\u2013143 (1995)","journal-title":"Neural Comput."},{"key":"2543_CR48","doi-asserted-by":"publisher","first-page":"3704","DOI":"10.1109\/TSP.2007.894265","volume":"55","author":"DP Wipf","year":"2007","unstructured":"D.P. Wipf, B.D. Rao, An empirical Bayesian strategy for solving the simultaneous sparse approximation problem. IEEE Trans. Signal Process. 55, 3704\u20133716 (2007)","journal-title":"IEEE Trans. Signal Process."},{"key":"2543_CR49","first-page":"2481","volume":"21","author":"G Yu","year":"2011","unstructured":"G. Yu, G. Sapiro, S. Mallat, Solving inverse problems with piecewise linear estimators: from Gaussian mixture models to structured sparsity. IEEE Trans. Image Process. 21, 2481\u20132499 (2011)","journal-title":"IEEE Trans. Image Process."},{"key":"2543_CR50","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1016\/j.sigpro.2011.07.015","volume":"92","author":"L Yu","year":"2012","unstructured":"L. Yu, H. Sun, J.-P. Barbot, G. Zheng, Bayesian compressive sensing for cluster structured sparse signals. Signal Process. 92, 259\u2013269 (2012)","journal-title":"Signal Process."},{"key":"2543_CR51","doi-asserted-by":"publisher","first-page":"770","DOI":"10.1049\/iet-spr.2014.0157","volume":"10","author":"L Yu","year":"2016","unstructured":"L. Yu, C. Wei, J. Jia, H. Sun, Compressive sensing for cluster structured sparse signals: variational Bayes approach. IET Signal Process. 10, 770\u2013779 (2016)","journal-title":"IET Signal Process."},{"key":"2543_CR52","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1111\/j.1467-9868.2005.00532.x","volume":"68","author":"M Yuan","year":"2006","unstructured":"M. Yuan, Y. Lin, Model selection and estimation in regression with grouped variables. J. R Stat. Soc. Ser. B Stat. Meth. 68, 49\u201367 (2006)","journal-title":"J. R Stat. Soc. Ser. B Stat. Meth."},{"key":"2543_CR53","doi-asserted-by":"publisher","first-page":"2009","DOI":"10.1109\/TSP.2013.2241055","volume":"61","author":"Z Zhang","year":"2013","unstructured":"Z. Zhang, B.D. Rao, Extension of SBL algorithms for the recovery of block sparse signals with intra-block correlation. IEEE Trans. Signal Process. 61, 2009\u20132015 (2013)","journal-title":"IEEE Trans. Signal Process."},{"key":"2543_CR54","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1016\/j.neucom.2019.02.001","volume":"338","author":"S Zheng","year":"2019","unstructured":"S. Zheng, C. Ding, Sparse classification using group matching pursuit. Neurocomputing 338, 83\u201391 (2019)","journal-title":"Neurocomputing"}],"container-title":["Circuits, Systems, and Signal Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00034-023-02543-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00034-023-02543-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00034-023-02543-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,26]],"date-time":"2024-02-26T09:08:23Z","timestamp":1708938503000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00034-023-02543-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,22]]},"references-count":54,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,3]]}},"alternative-id":["2543"],"URL":"https:\/\/doi.org\/10.1007\/s00034-023-02543-8","relation":{},"ISSN":["0278-081X","1531-5878"],"issn-type":[{"type":"print","value":"0278-081X"},{"type":"electronic","value":"1531-5878"}],"subject":[],"published":{"date-parts":[[2023,11,22]]},"assertion":[{"value":"19 August 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 October 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 October 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 November 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The author confirms that there are no known conflicts of interest associated with this publication.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}