{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T22:34:01Z","timestamp":1768775641582,"version":"3.49.0"},"reference-count":54,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2020,1,20]],"date-time":"2020-01-20T00:00:00Z","timestamp":1579478400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,20]],"date-time":"2020-01-20T00:00:00Z","timestamp":1579478400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["11901471"],"award-info":[{"award-number":["11901471"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["11771099"],"award-info":[{"award-number":["11771099"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Comput Optim Appl"],"published-print":{"date-parts":[[2020,4]]},"DOI":"10.1007\/s10589-020-00167-1","type":"journal-article","created":{"date-parts":[[2020,1,20]],"date-time":"2020-01-20T16:02:37Z","timestamp":1579536157000},"page":"753-777","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":50,"title":["Tensor neural network models for tensor singular value decompositions"],"prefix":"10.1007","volume":"75","author":[{"given":"Xuezhong","family":"Wang","sequence":"first","affiliation":[]},{"given":"Maolin","family":"Che","sequence":"additional","affiliation":[]},{"given":"Yimin","family":"Wei","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,1,20]]},"reference":[{"key":"167_CR1","doi-asserted-by":"crossref","first-page":"1241","DOI":"10.1016\/j.laa.2010.05.025","volume":"433","author":"K Braman","year":"2010","unstructured":"Braman, K.: Third-order tensors as linear operators on a space of matrices. Linear Algebra Appl. 433, 1241\u20131253 (2010)","journal-title":"Linear Algebra Appl."},{"key":"167_CR2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/BF01385712","volume":"60","author":"A Bunsegerstner","year":"1991","unstructured":"Bunsegerstner, A., Byers, R., Mehrmann, V., Nichols, N.: Numerical computation of an analytic singular value decomposition of a matrix valued function. Numer. Math. 60, 1\u201339 (1991)","journal-title":"Numer. Math."},{"key":"167_CR3","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1162\/089976699300016863","volume":"11","author":"J Cardoso","year":"1999","unstructured":"Cardoso, J.: High-order contrasts for independent component analysis. Neural Comput. 11, 157\u2013192 (1999)","journal-title":"Neural Comput."},{"key":"167_CR4","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1007\/BF02310791","volume":"35","author":"J Carroll","year":"1970","unstructured":"Carroll, J., Chang, J.: Analysis of individual differences in multidimensional scaling via an $$n$$-way generalization of \u201cEckart-Young\u201d decomposition. Psychometrika 35, 283\u2013319 (1970)","journal-title":"Psychometrika"},{"key":"167_CR5","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.neucom.2017.04.058","volume":"267","author":"M Che","year":"2017","unstructured":"Che, M., Cichocki, A., Wei, Y.: Neural networks for computing best rank-one approximations of tensors and its applications. Neurocomputing 267, 114\u2013133 (2017)","journal-title":"Neurocomputing"},{"key":"167_CR6","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1007\/s10444-018-9622-8","volume":"45","author":"M Che","year":"2019","unstructured":"Che, M., Wei, Y.: Randomized algorithms for the approximations of Tucker and the tensor train decompositions. Adv. Comput. Math. 45, 395\u2013428 (2019)","journal-title":"Adv. Comput. Math."},{"key":"167_CR7","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1561\/2200000059","volume":"9","author":"A Cichocki","year":"2016","unstructured":"Cichocki, A., Lee, N., Oseledets, I.V., Phan, A.H., Zhao, Q., Mandic, D.P.: Tensor networks for dimensionality reduction and large-scale optimization: part 1 low-rank tensor decompositions. Found. Trends Mach. Learn. 9, 249\u2013429 (2016)","journal-title":"Found. Trends Mach. Learn."},{"key":"167_CR8","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1561\/2200000067","volume":"9","author":"A Cichocki","year":"2017","unstructured":"Cichocki, A., Lee, N., Oseledets, I.V., Phan, A.H., Zhao, Q., Mandic, D.P.: Tensor networks for dimensionality reduction and large-scale optimization: part 2 applications and future perspectives. Found. Trends Mach. Learn. 9, 431\u2013673 (2017)","journal-title":"Found. Trends Mach. Learn."},{"key":"167_CR9","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1109\/MSP.2013.2297439","volume":"32","author":"A Cichocki","year":"2015","unstructured":"Cichocki, A., Mandic, D., De Lathauwer, L., Zhou, G., Zhao, Q., Caiafa, C., Phan, H.: Tensor decompositions for signal processing applications: from two-way to multiway component analysis. IEEE Signal Process. Mag. 32, 145\u2013163 (2015)","journal-title":"IEEE Signal Process. Mag."},{"key":"167_CR10","volume-title":"Neural Networks for Optimization and Signal Processing","author":"A Cichocki","year":"1993","unstructured":"Cichocki, A., Unbehauen, R.: Neural Networks for Optimization and Signal Processing. Wiley, New York (1993)"},{"key":"167_CR11","doi-asserted-by":"crossref","DOI":"10.1002\/9780470747278","volume-title":"Nonnegative Matrix and Tensor Factorizations: Applications to Exploratory Multi-way Data Analysis and Blind Source Separation","author":"A Cichocki","year":"2009","unstructured":"Cichocki, A., Zdunek, R., Phan, A., Amari, S.: Nonnegative Matrix and Tensor Factorizations: Applications to Exploratory Multi-way Data Analysis and Blind Source Separation. Wiley, New York (2009)"},{"key":"167_CR12","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1016\/0165-1684(94)90029-9","volume":"36","author":"P Comon","year":"1994","unstructured":"Comon, P.: Independent component analysis, a new concept? Sig. Process. 36, 287\u2013314 (1994)","journal-title":"Sig. Process."},{"key":"167_CR13","doi-asserted-by":"crossref","unstructured":"Comon, P.: Tensor decompositions: state of the art and applications. In: Mathematics in Signal Processing, V (Coventry, 2000), vol.\u00a071 of Institute of Mathematics Applications Conference Series New Series, Oxford Univ. Press, Oxford, pp.\u00a01\u201324. (2002)","DOI":"10.1093\/oso\/9780198507345.003.0001"},{"key":"167_CR14","doi-asserted-by":"crossref","first-page":"1254","DOI":"10.1137\/060661569","volume":"30","author":"P Comon","year":"2008","unstructured":"Comon, P., Golub, G., Lim, L., Mourrain, B.: Symmetric tensors and symmetric tensor rank. SIAM J. Matrix Anal. Appl. 30, 1254\u20131279 (2008)","journal-title":"SIAM J. Matrix Anal. Appl."},{"key":"167_CR15","doi-asserted-by":"crossref","first-page":"1253","DOI":"10.1137\/S0895479896305696","volume":"21","author":"L De Lathauwer","year":"2000","unstructured":"De Lathauwer, L., De Moor, B., Vandewalle, J.: A multilinear singular value decomposition. SIAM J. Matrix Anal. Appl. 21, 1253\u20131278 (2000)","journal-title":"SIAM J. Matrix Anal. Appl."},{"key":"167_CR16","doi-asserted-by":"crossref","first-page":"1324","DOI":"10.1137\/S0895479898346995","volume":"21","author":"L De Lathauwer","year":"2000","unstructured":"De Lathauwer, L., De Moor, B., Vandewalle, J.: On the best rank-1 and rank-$$(r_1, r_2,\\dots, r_n)$$ approximation of higher-order tensors. SIAM J. Matrix Anal. Appl. 21, 1324\u20131342 (2000)","journal-title":"SIAM J. Matrix Anal. Appl."},{"key":"167_CR17","doi-asserted-by":"crossref","unstructured":"De\u00a0Lathauwer, L., Hoegaerts, L., Vandewalle, J.: A Grassmann-Rayleigh quotient iteration for dimensionality reduction in ICA. In: International Conference on Independent Component Analysis and Signal Separation, Springer, Berlin, pp.\u00a0335\u2013342 (2004)","DOI":"10.1007\/978-3-540-30110-3_43"},{"key":"167_CR18","doi-asserted-by":"crossref","first-page":"3218","DOI":"10.1109\/78.330379","volume":"42","author":"K Diamantaras","year":"1994","unstructured":"Diamantaras, K., Kung, S.: Cross-correlation neural network models. IEEE Trans. Signal Process. 42, 3218\u20133223 (1994)","journal-title":"IEEE Trans. Signal Process."},{"key":"167_CR19","doi-asserted-by":"crossref","first-page":"800","DOI":"10.1137\/S0895479897330182","volume":"20","author":"L Dieci","year":"1999","unstructured":"Dieci, L., Eirola, T.: On smooth decompositions of matrices. SIAM J. Matrix Anal. Appl. 20, 800\u2013819 (1999)","journal-title":"SIAM J. Matrix Anal. Appl."},{"key":"167_CR20","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1137\/070688316","volume":"31","author":"L Eld\u00e9n","year":"2009","unstructured":"Eld\u00e9n, L., Savas, B.: A Newton\u2013Grassmann method for computing the best multilinear rank-$$(r_1, r_2, r_3)$$ approximation of a tensor. SIAM J. Matrix Anal. Appl. 31, 248\u2013271 (2009)","journal-title":"SIAM J. Matrix Anal. Appl."},{"key":"167_CR21","doi-asserted-by":"crossref","first-page":"1215","DOI":"10.1109\/72.950149","volume":"12","author":"D Feng","year":"2001","unstructured":"Feng, D., Bao, Z., Zhang, X.: A cross-associative neural network for SVD of non-squared data matrix in signal processing. IEEE Trans. Neural Netw. 12, 1215\u20131221 (2001)","journal-title":"IEEE Trans. Neural Netw."},{"key":"167_CR22","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1142\/S0129065703001406","volume":"13","author":"S Fiori","year":"2003","unstructured":"Fiori, S.: Singular value decomposition learning on double Stiefel manifold. Int. J. Neural Syst. 13, 155\u2013170 (2003)","journal-title":"Int. J. Neural Syst."},{"key":"167_CR23","doi-asserted-by":"crossref","first-page":"A1","DOI":"10.1137\/100792056","volume":"34","author":"S Goreinov","year":"2012","unstructured":"Goreinov, S., Oseledets, I., Savostyanov, D.: Wedderburn rank reduction and Krylov subspace method for tensor approximation. Part 1: Tucker case. SIAM J. Sci. Comput. 34, A1\u2013A27 (2012)","journal-title":"SIAM J. Sci. Comput."},{"key":"167_CR24","doi-asserted-by":"crossref","first-page":"2029","DOI":"10.1137\/090764189","volume":"31","author":"L Grasedyck","year":"2010","unstructured":"Grasedyck, L.: Hierarchical singular value decomposition of tensors. SIAM J. Matrix Anal. Appl. 31, 2029\u20132054 (2010)","journal-title":"SIAM J. Matrix Anal. Appl."},{"key":"167_CR25","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1002\/gamm.201310004","volume":"36","author":"L Grasedyck","year":"2013","unstructured":"Grasedyck, L., Kressner, D., Tobler, C.: A literature survey of low-rank tensor approximation techniques. GAMM-Mitteilung. 36, 53\u201378 (2013)","journal-title":"GAMM-Mitteilung."},{"key":"167_CR26","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-642-28027-6","volume-title":"Tensor Spaces and Numerical Tensor Calculus","author":"W Hackbusch","year":"2012","unstructured":"Hackbusch, W.: Tensor Spaces and Numerical Tensor Calculus, vol. 42. Springer, Berlin (2012)"},{"key":"167_CR27","volume-title":"Differential Equations, Dynamical Systems and Linear Algebra","author":"M Hirsch","year":"1974","unstructured":"Hirsch, M., Smale, S.: Differential Equations, Dynamical Systems and Linear Algebra. Academic Press, San Diego (1974)"},{"key":"167_CR28","volume-title":"The Stability of Dynamical Systems","author":"M Hirsch","year":"1976","unstructured":"Hirsch, M., Smale, S.: The Stability of Dynamical Systems. SIAM, Philadelphia (1976)"},{"key":"167_CR29","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1137\/090764827","volume":"32","author":"M Ishteva","year":"2011","unstructured":"Ishteva, M., Absil, P., Van Huffel, S., De Lathauwer, L.: Best low multilinear rank approximation of higher-order tensors, based on the Riemannian trust-region scheme. SIAM J. Matrix Anal. Appl. 32, 115\u2013135 (2011)","journal-title":"SIAM J. Matrix Anal. Appl."},{"key":"167_CR30","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1007\/s11075-008-9251-2","volume":"51","author":"M Ishteva","year":"2009","unstructured":"Ishteva, M., De Lathauwer, L., Absil, P., Van Huffel, S.: Differential-geometric Newton method for the best rank-$$(r_1, r_2, r_3)$$ approximation of tensors. Numer. Algorithms 51, 179\u2013194 (2009)","journal-title":"Numer. Algorithms"},{"key":"167_CR31","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1137\/110837711","volume":"34","author":"M Kilmer","year":"2013","unstructured":"Kilmer, M., Braman, K., Hao, N., Hoover, R.: Third-order tensors as operators on matrices: a theoretical and computational framework with applications in imaging. SIAM J. Matrix Anal. Appl. 34, 148\u2013172 (2013)","journal-title":"SIAM J. Matrix Anal. Appl."},{"key":"167_CR32","doi-asserted-by":"crossref","first-page":"641","DOI":"10.1016\/j.laa.2010.09.020","volume":"435","author":"M Kilmer","year":"2011","unstructured":"Kilmer, M., Martin, C.: Factorization strategies for third-order tensors. Linear Algebra Appl. 435, 641\u2013658 (2011)","journal-title":"Linear Algebra Appl."},{"key":"167_CR33","doi-asserted-by":"crossref","first-page":"434","DOI":"10.1137\/050639703","volume":"29","author":"O Koch","year":"2007","unstructured":"Koch, O., Lubich, C.: Dynamical low-rank approximation. SIAM J. Matrix Anal. Appl. 29, 434\u2013454 (2007)","journal-title":"SIAM J. Matrix Anal. Appl."},{"key":"167_CR34","doi-asserted-by":"crossref","first-page":"2360","DOI":"10.1137\/09076578X","volume":"31","author":"O Koch","year":"2010","unstructured":"Koch, O., Lubich, C.: Dynamical tensor approximation. SIAM J. Matrix Anal. Appl. 31, 2360\u20132375 (2010)","journal-title":"SIAM J. Matrix Anal. Appl."},{"key":"167_CR35","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1137\/07070111X","volume":"51","author":"T Kolda","year":"2009","unstructured":"Kolda, T., Bader, B.: Tensor decompositions and applications. SIAM Rev. 51, 455\u2013500 (2009)","journal-title":"SIAM Rev."},{"key":"167_CR36","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1023\/B:JOGO.0000015310.27011.02","volume":"28","author":"L Liao","year":"2004","unstructured":"Liao, L., Qi, H., Qi, L.: Neurodynamical optimization. J. Global Optim. 28, 175\u2013195 (2004)","journal-title":"J. Global Optim."},{"key":"167_CR37","unstructured":"Lu, C.: Tensor\u2013Tensor Product Toolbox, Carnegie Mellon University, (2018). https:\/\/github.com\/canyilu\/tproduct"},{"issue":"4","key":"167_CR38","doi-asserted-by":"publisher","first-page":"925","DOI":"10.1109\/tpami.2019.2891760","volume":"42","author":"Canyi Lu","year":"2020","unstructured":"Lu, C., Feng, J., Chen, Y., Liu, W., Lin, Z., Yan, S.: Tensor robust principal component analysis with a new tensor nuclear norm, IEEE Trans. Pattern Anal. Mach. Intell. (2019). https:\/\/doi.org\/10.1109\/tpami.2019.2891760","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"167_CR39","doi-asserted-by":"crossref","first-page":"470","DOI":"10.1137\/120885723","volume":"34","author":"C Lubich","year":"2013","unstructured":"Lubich, C., Rohwedder, T., Schneider, R., Vandereycken, B.: Dynamical approximation by hierarchical tucker and tensor-train tensors. SIAM J. Matrix Anal. Appl. 34, 470\u2013494 (2013)","journal-title":"SIAM J. Matrix Anal. Appl."},{"key":"167_CR40","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1016\/j.laa.2019.12.035","volume":"590","author":"Y Miao","year":"2020","unstructured":"Miao, Y., Qi, L., Wei, Y.: Generalized tensor function via the tensor singular value decomposition based on the T-product. Linear Algebra Appl. 590, 258\u2013303 (2020)","journal-title":"Linear Algebra Appl"},{"key":"167_CR41","unstructured":"Newman, E., Horesh, L., Avron, H., Kilmer, M.: Stable tensor neural networks for rapid deep learning, arXiv:1811.06569v1 (2018)"},{"key":"167_CR42","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1109\/79.221324","volume":"10","author":"C Nikias","year":"1993","unstructured":"Nikias, C., Mendel, J.: Signal processing with higher-order spectra. IEEE Signal Process. Mag. 10, 10\u201337 (1993)","journal-title":"IEEE Signal Process. Mag."},{"key":"167_CR43","doi-asserted-by":"crossref","first-page":"2295","DOI":"10.1137\/090752286","volume":"33","author":"IV Oseledets","year":"2011","unstructured":"Oseledets, I.V.: Tensor-train decomposition. SIAM J. Sci. Comput. 33, 2295\u20132317 (2011)","journal-title":"SIAM J. Sci. Comput."},{"key":"167_CR44","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.laa.2017.03.014","volume":"542","author":"S Qiao","year":"2017","unstructured":"Qiao, S., Wang, X., Wei, Y.: Two finite-time convergent Zhang neural network models for time-varying complex matrix Drazin inverse. Linear Algebra Appl. 542, 101\u2013117 (2017)","journal-title":"Linear Algebra Appl."},{"key":"167_CR45","doi-asserted-by":"crossref","first-page":"384","DOI":"10.1109\/31.52732","volume":"37","author":"A Rodriguezvazquez","year":"1990","unstructured":"Rodriguezvazquez, A., Dominguezcastro, R., Rueda, A., Huertas, J., Sanchezsinencio, E.: Nonlinear switched capacitor \u2018neural\u2019 networks for optimization problems. IEEE Trans. Circuits Syst. 37, 384\u2013398 (1990)","journal-title":"IEEE Trans. Circuits Syst."},{"key":"167_CR46","doi-asserted-by":"crossref","first-page":"891","DOI":"10.1016\/j.laa.2011.12.007","volume":"438","author":"B Savas","year":"2013","unstructured":"Savas, B., Eld\u00e9n, L.: Krylov-type methods for tensor computations I. Linear Algebra Appl. 438, 891\u2013918 (2013)","journal-title":"Linear Algebra Appl."},{"key":"167_CR47","doi-asserted-by":"crossref","first-page":"3352","DOI":"10.1137\/090763172","volume":"32","author":"B Savas","year":"2010","unstructured":"Savas, B., Lim, L.: Quasi-Newton methods on Grassmannians and multilinear approximations of tensors. SIAM J. Sci. Comput. 32, 3352\u20133393 (2010)","journal-title":"SIAM J. Sci. Comput."},{"key":"167_CR48","unstructured":"Vasilescu, M., Terzopoulos, D.: Multilinear subspace analysis of image ensembles. In: IEEE Computer Society Conference Computer Vision and Pattern Recognition, vol.\u00a02, IEEE, pp.\u00a093\u201399 (2003)"},{"issue":"2","key":"167_CR49","doi-asserted-by":"publisher","first-page":"394","DOI":"10.1080\/10556788.2019.1578766","volume":"35","author":"Xuezhong Wang","year":"2019","unstructured":"Wang, X., Che, M., Qi, L., Wei, Y.: Modified gradient dynamic approach to the tensor complementarity problem. Optim. Methods Softw. (2019). https:\/\/doi.org\/10.1080\/10556788.2019.1578766","journal-title":"Optimization Methods and Software"},{"key":"167_CR50","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.neucom.2019.03.025","volume":"351","author":"X Wang","year":"2019","unstructured":"Wang, X., Che, M., Wei, Y.: Neural networks based approach solving multi-linear systems with $$\\cal{M}$$-tensors. Neurocomputing 351, 33\u201342 (2019)","journal-title":"Neurocomputing"},{"key":"167_CR51","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1007\/BF01385862","volume":"63","author":"K Wright","year":"1992","unstructured":"Wright, K.: Differential equations for the analytic singular value decomposition of a matrix. Numer. Math. 63, 283\u2013295 (1992)","journal-title":"Numer. Math."},{"key":"167_CR52","volume-title":"Mathematical Control Theory: an Introduction","author":"J Zabczyk","year":"2015","unstructured":"Zabczyk, J.: Mathematical Control Theory: an Introduction. Birkh\u00e4user, Basel (2015)"},{"key":"167_CR53","doi-asserted-by":"crossref","first-page":"1511","DOI":"10.1109\/TSP.2016.2639466","volume":"65","author":"Z Zhang","year":"2017","unstructured":"Zhang, Z., Aeron, S.: Exact tensor completion using t-SVD. IEEE Trans. Signal Process. 65, 1511\u20131526 (2017)","journal-title":"IEEE Trans. Signal Process."},{"key":"167_CR54","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1007\/BF02238196","volume":"36","author":"G Zielke","year":"1986","unstructured":"Zielke, G.: Report on test matrices for generalized inverses. Computing 36, 105\u2013162 (1986)","journal-title":"Computing"}],"container-title":["Computational Optimization and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10589-020-00167-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10589-020-00167-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10589-020-00167-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,30]],"date-time":"2024-07-30T11:15:48Z","timestamp":1722338148000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10589-020-00167-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1,20]]},"references-count":54,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2020,4]]}},"alternative-id":["167"],"URL":"https:\/\/doi.org\/10.1007\/s10589-020-00167-1","relation":{},"ISSN":["0926-6003","1573-2894"],"issn-type":[{"value":"0926-6003","type":"print"},{"value":"1573-2894","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,1,20]]},"assertion":[{"value":"24 March 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 January 2020","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}