{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T21:20:53Z","timestamp":1762377653046,"version":"3.33.0"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2025,1,17]],"date-time":"2025-01-17T00:00:00Z","timestamp":1737072000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,17]],"date-time":"2025-01-17T00:00:00Z","timestamp":1737072000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100007957","name":"Chongqing Municipal Education Commission","doi-asserted-by":"publisher","award":["KJZD-K202100505"],"award-info":[{"award-number":["KJZD-K202100505"]}],"id":[{"id":"10.13039\/501100007957","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002865","name":"Chongqing Municipal Science and Technology Bureau","doi-asserted-by":"publisher","award":["cstc2020jscx-msxmX0190"],"award-info":[{"award-number":["cstc2020jscx-msxmX0190"]}],"id":[{"id":"10.13039\/501100002865","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013139","name":"Humanities and Social Science Fund of Ministry of Education of China","doi-asserted-by":"publisher","award":["20YJAZH084"],"award-info":[{"award-number":["20YJAZH084"]}],"id":[{"id":"10.13039\/501100013139","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"DOI":"10.1007\/s11227-024-06904-1","type":"journal-article","created":{"date-parts":[[2025,1,17]],"date-time":"2025-01-17T17:38:00Z","timestamp":1737135480000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Discriminant locality preserving projection on Grassmann Manifold for image-set classification"],"prefix":"10.1007","volume":"81","author":[{"given":"Benchao","family":"Li","sequence":"first","affiliation":[]},{"given":"Ting","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Ruisheng","family":"Ran","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,1,17]]},"reference":[{"issue":"3","key":"6904_CR1","doi-asserted-by":"publisher","first-page":"589","DOI":"10.1073\/pnas.57.3.589","volume":"57","author":"Y-C Wong","year":"1967","unstructured":"Wong Y-C (1967) Differential geometry of grassmann manifolds. Proc Natl Acad Sci 57(3):589\u2013594","journal-title":"Proc Natl Acad Sci"},{"key":"6904_CR2","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1023\/B:ACAP.0000013855.14971.91","volume":"80","author":"P-A Absil","year":"2004","unstructured":"Absil P-A, Mahony R, Sepulchre R (2004) Riemannian geometry of grassmann manifolds with a view on algorithmic computation. Acta Appl Math 80:199\u2013220","journal-title":"Acta Appl Math"},{"key":"6904_CR3","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1007\/s11263-005-3222-z","volume":"66","author":"X Pennec","year":"2006","unstructured":"Pennec X, Fillard P, Ayache N (2006) A riemannian framework for tensor computing. Int J Comput Vision 66:41\u201366","journal-title":"Int J Comput Vision"},{"issue":"1","key":"6904_CR4","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1109\/TPAMI.2017.2655048","volume":"40","author":"M Harandi","year":"2017","unstructured":"Harandi M, Salzmann M, Hartley R (2017) Dimensionality reduction on spd manifolds: The emergence of geometry-aware methods. IEEE Trans Pattern Anal Mach Intell 40(1):48\u201362","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"2","key":"6904_CR5","doi-asserted-by":"publisher","first-page":"1887","DOI":"10.1007\/s10489-022-03177-0","volume":"53","author":"W Gao","year":"2023","unstructured":"Gao W, Ma Z, Xiong C, Gao T (2023) Dimensionality reduction of spd data based on riemannian manifold tangent spaces and local affinity. Appl Intell 53(2):1887\u20131911","journal-title":"Appl Intell"},{"issue":"2","key":"6904_CR6","doi-asserted-by":"publisher","first-page":"365","DOI":"10.1016\/S0377-2217(02)00329-6","volume":"143","author":"T Rapcs\u00e1k","year":"2002","unstructured":"Rapcs\u00e1k T (2002) On minimization on stiefel manifolds. Eur J Oper Res 143(2):365\u2013376","journal-title":"Eur J Oper Res"},{"issue":"2","key":"6904_CR7","doi-asserted-by":"publisher","first-page":"1546","DOI":"10.1137\/20M1348522","volume":"31","author":"B Gao","year":"2021","unstructured":"Gao B, Son NT, Absil P-A, Stykel T (2021) Riemannian optimization on the symplectic stiefel manifold. SIAM J Optim 31(2):1546\u20131575","journal-title":"SIAM J Optim"},{"key":"6904_CR8","doi-asserted-by":"crossref","unstructured":"Huang Z, Wu J, Van\u00a0Gool L (2018) Building deep networks on grassmann manifolds. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32","DOI":"10.1609\/aaai.v32i1.11725"},{"key":"6904_CR9","doi-asserted-by":"crossref","unstructured":"Shigenaka R, Raytchev B, Tamaki T, Kaneda K (2012) Face sequence recognition using grassmann distances and grassmann kernels. In: The 2012 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20137. IEEE","DOI":"10.1109\/IJCNN.2012.6252731"},{"issue":"11","key":"6904_CR10","doi-asserted-by":"publisher","first-page":"2273","DOI":"10.1109\/TPAMI.2011.52","volume":"33","author":"P Turaga","year":"2011","unstructured":"Turaga P, Veeraraghavan A, Srivastava A, Chellappa R (2011) Statistical computations on grassmann and stiefel manifolds for image and video-based recognition. IEEE Trans Pattern Anal Mach Intell 33(11):2273\u20132286","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"4","key":"6904_CR11","doi-asserted-by":"publisher","first-page":"905","DOI":"10.1109\/TSP.2013.2295553","volume":"62","author":"X Dong","year":"2013","unstructured":"Dong X, Frossard P, Vandergheynst P, Nefedov N (2013) Clustering on multi-layer graphs via subspace analysis on grassmann manifolds. IEEE Trans Signal Process 62(4):905\u2013918","journal-title":"IEEE Trans Signal Process"},{"key":"6904_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.cosrev.2021.100378","volume":"40","author":"F Anowar","year":"2021","unstructured":"Anowar F, Sadaoui S, Selim B (2021) Conceptual and empirical comparison of dimensionality reduction algorithms (pca, kpca, lda, mds, svd, lle, isomap, le, ica, t-sne). Comput Sci Rev 40:100378","journal-title":"Comput Sci Rev"},{"issue":"1","key":"6904_CR13","first-page":"2859","volume":"16","author":"JP Cunningham","year":"2015","unstructured":"Cunningham JP, Ghahramani Z (2015) Linear dimensionality reduction: survey, insights, and generalizations. J Machine Learn Res 16(1):2859\u20132900","journal-title":"J Machine Learn Res"},{"key":"6904_CR14","doi-asserted-by":"crossref","unstructured":"Pearson K (1901) Liii. on lines and planes of closest fit to systems of points in space. The London, Edinburgh, and Dublin philosophical magazine and journal of science 2(11), 559\u2013572","DOI":"10.1080\/14786440109462720"},{"issue":"2","key":"6904_CR15","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1111\/j.1469-1809.1936.tb02137.x","volume":"7","author":"RA Fisher","year":"1936","unstructured":"Fisher RA (1936) The use of multiple measurements in taxonomic problems. Ann Eugen 7(2):179\u2013188","journal-title":"Ann Eugen"},{"issue":"5500","key":"6904_CR16","doi-asserted-by":"publisher","first-page":"2323","DOI":"10.1126\/science.290.5500.2323","volume":"290","author":"ST Roweis","year":"2000","unstructured":"Roweis ST, Saul LK (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500):2323\u20132326","journal-title":"Science"},{"issue":"6","key":"6904_CR17","doi-asserted-by":"publisher","first-page":"1373","DOI":"10.1162\/089976603321780317","volume":"15","author":"M Belkin","year":"2003","unstructured":"Belkin M, Niyogi P (2003) Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput 15(6):1373\u20131396","journal-title":"Neural Comput"},{"key":"6904_CR18","doi-asserted-by":"crossref","unstructured":"McInnes L, Healy J, Melville J (2018) Umap: Uniform manifold approximation and projection for dimension reduction. ArXiv e-prints arXiv:1802.03426 [stat.ML]","DOI":"10.21105\/joss.00861"},{"issue":"3","key":"6904_CR19","doi-asserted-by":"publisher","first-page":"1410","DOI":"10.1109\/TIP.2016.2520368","volume":"25","author":"E Vural","year":"2016","unstructured":"Vural E, Guillemot C (2016) Out-of-sample generalizations for supervised manifold learning for classification. IEEE Trans Image Process 25(3):1410\u20131424","journal-title":"IEEE Trans Image Process"},{"issue":"10","key":"6904_CR20","doi-asserted-by":"publisher","first-page":"5227","DOI":"10.1109\/TIP.2019.2915162","volume":"28","author":"G Ta\u015fkin","year":"2019","unstructured":"Ta\u015fkin G, Crawford MM (2019) An out-of-sample extension to manifold learning via meta-modeling. IEEE Trans Image Process 28(10):5227\u20135237","journal-title":"IEEE Trans Image Process"},{"key":"6904_CR21","unstructured":"He X, Niyogi P (2003) Locality preserving projections. Advances in neural information processing systems 16"},{"key":"6904_CR22","doi-asserted-by":"publisher","first-page":"322","DOI":"10.1016\/j.neucom.2018.08.008","volume":"316","author":"W Yu","year":"2018","unstructured":"Yu W, Wang R, Nie F, Wang F, Yu Q, Yang X (2018) An improved locality preserving projection with l1-norm minimization for dimensionality reduction. Neurocomputing 316:322\u2013331","journal-title":"Neurocomputing"},{"issue":"17","key":"6904_CR23","doi-asserted-by":"publisher","first-page":"3654","DOI":"10.1016\/j.neucom.2011.07.007","volume":"74","author":"S-J Wang","year":"2011","unstructured":"Wang S-J, Chen H-L, Peng X-J, Zhou C-G (2011) Exponential locality preserving projections for small sample size problem. Neurocomputing 74(17):3654\u20133662","journal-title":"Neurocomputing"},{"key":"6904_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113352","volume":"151","author":"A Wang","year":"2020","unstructured":"Wang A, Zhao S, Liu J, Yang J, Liu L, Chen G (2020) Locality adaptive preserving projections for linear dimensionality reduction. Expert Syst Appl 151:113352","journal-title":"Expert Syst Appl"},{"issue":"4\u20136","key":"6904_CR25","doi-asserted-by":"publisher","first-page":"912","DOI":"10.1016\/j.neucom.2006.10.032","volume":"70","author":"S Chen","year":"2007","unstructured":"Chen S, Zhao H, Kong M, Luo B (2007) 2d-lpp: A two-dimensional extension of locality preserving projections. Neurocomputing 70(4\u20136):912\u2013921","journal-title":"Neurocomputing"},{"issue":"1","key":"6904_CR26","doi-asserted-by":"publisher","first-page":"339","DOI":"10.1016\/j.patcog.2006.06.022","volume":"40","author":"D Hu","year":"2007","unstructured":"Hu D, Feng G, Zhou Z (2007) Two-dimensional locality preserving projections (2dlpp) with its application to palmprint recognition. Pattern Recogn 40(1):339\u2013342","journal-title":"Pattern Recogn"},{"key":"6904_CR27","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/j.knosys.2019.01.022","volume":"169","author":"W-J Chen","year":"2019","unstructured":"Chen W-J, Li C-N, Shao Y-H, Zhang J, Deng N-Y (2019) 2drlpp: Robust two-dimensional locality preserving projection with regularization. Knowl-Based Syst 169:53\u201366","journal-title":"Knowl-Based Syst"},{"key":"6904_CR28","doi-asserted-by":"crossref","unstructured":"Wang B, Hu Y, Gao J, Sun Y, Ali M, Chen H, Yin B (2017) Locality preserving projections for grassmann manifold. In: International Joint Conference on Artificial Intelligence, pp. 2893\u20132900","DOI":"10.24963\/ijcai.2017\/403"},{"key":"6904_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.123316","volume":"247","author":"D Wei","year":"2024","unstructured":"Wei D, Shen X, Sun Q, Gao X, Ren Z (2024) Learning adaptive grassmann neighbors for image-set analysis. Expert Syst Appl 247:123316","journal-title":"Expert Syst Appl"},{"key":"6904_CR30","doi-asserted-by":"crossref","unstructured":"Hamm J, Lee DD (2008) Grassmann discriminant analysis: a unifying view on subspace-based learning. In: Proceedings of the 25th International Conference on Machine Learning, pp. 376\u2013383","DOI":"10.1145\/1390156.1390204"},{"key":"6904_CR31","doi-asserted-by":"crossref","unstructured":"Harandi MT, Sanderson C, Shirazi S, Lovell BC (2011) Graph embedding discriminant analysis on grassmannian manifolds for improved image set matching. In: CVPR 2011, pp. 2705\u20132712. IEEE","DOI":"10.1109\/CVPR.2011.5995564"},{"key":"6904_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.108335","volume":"122","author":"D Wei","year":"2022","unstructured":"Wei D, Shen X, Sun Q, Gao X, Ren Z (2022) Neighborhood preserving embedding on grassmann manifold for image-set analysis. Pattern Recogn 122:108335","journal-title":"Pattern Recogn"},{"key":"6904_CR33","doi-asserted-by":"crossref","unstructured":"Zhang D, Zhou Z-H, Chen S (2007) Semi-supervised dimensionality reduction. In: Proceedings of the 2007 SIAM International Conference on Data Mining, pp. 629\u2013634. SIAM","DOI":"10.1137\/1.9781611972771.73"},{"issue":"9","key":"6904_CR34","doi-asserted-by":"publisher","first-page":"2789","DOI":"10.1016\/j.patcog.2008.01.001","volume":"41","author":"Y Song","year":"2008","unstructured":"Song Y, Nie F, Zhang C, Xiang S (2008) A unified framework for semi-supervised dimensionality reduction. Pattern Recogn 41(9):2789\u20132799","journal-title":"Pattern Recogn"},{"key":"6904_CR35","doi-asserted-by":"crossref","unstructured":"Harandi M, Sanderson C, Shen C, Lovell BC (2013) Dictionary learning and sparse coding on grassmann manifolds: An extrinsic solution. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3120\u20133127","DOI":"10.1109\/ICCV.2013.387"},{"issue":"1","key":"6904_CR36","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1007\/s00530-019-00629-5","volume":"26","author":"H Yu","year":"2020","unstructured":"Yu H, Xia K, Jiang Y, Qian P (2020) Fr\u00e9chet mean-based grassmann discriminant analysis. Multimedia Syst 26(1):63\u201373","journal-title":"Multimedia Syst"},{"key":"6904_CR37","unstructured":"Zhang S, Yu G (2010) Semi-supervised locality preserving projections with compactness enhancement. In: 2010 International Conference on Educational and Information Technology, vol. 2, pp. 2\u2013460. IEEE"},{"issue":"6","key":"6904_CR38","doi-asserted-by":"publisher","first-page":"2432","DOI":"10.1016\/j.patcog.2011.12.006","volume":"45","author":"B Raducanu","year":"2012","unstructured":"Raducanu B, Dornaika F (2012) A supervised non-linear dimensionality reduction approach for manifold learning. Pattern Recogn 45(6):2432\u20132444","journal-title":"Pattern Recogn"},{"issue":"1","key":"6904_CR39","doi-asserted-by":"publisher","first-page":"2872","DOI":"10.1038\/s41467-021-23102-2","volume":"12","author":"JT Vogelstein","year":"2021","unstructured":"Vogelstein JT, Bridgeford EW, Tang M, Zheng D, Douville C, Burns R, Maggioni M (2021) Supervised dimensionality reduction for big data. Nat Commun 12(1):2872","journal-title":"Nat Commun"},{"issue":"3","key":"6904_CR40","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1016\/j.imavis.2005.11.006","volume":"24","author":"W Yu","year":"2006","unstructured":"Yu W, Teng X, Liu C (2006) Face recognition using discriminant locality preserving projections. Image Vis Comput 24(3):239\u2013248","journal-title":"Image Vis Comput"},{"issue":"10","key":"6904_CR41","doi-asserted-by":"publisher","first-page":"3572","DOI":"10.1016\/j.patcog.2010.04.007","volume":"43","author":"G-F Lu","year":"2010","unstructured":"Lu G-F, Lin Z, Jin Z (2010) Face recognition using discriminant locality preserving projections based on maximum margin criterion. Pattern Recogn 43(10):3572\u20133579","journal-title":"Pattern Recogn"},{"key":"6904_CR42","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1016\/j.patcog.2016.01.029","volume":"55","author":"G-F Lu","year":"2016","unstructured":"Lu G-F, Zou J, Wang Y (2016) L1-norm and maximum margin criterion based discriminant locality preserving projections via trace lasso. Pattern Recogn 55:207\u2013214","journal-title":"Pattern Recogn"},{"key":"6904_CR43","doi-asserted-by":"crossref","unstructured":"Al-Samhi W, Al-Soswa M, Al-Dhabi Y (2021) Time series data classification on grassmann manifold. In: Journal of Physics: Conference Series, vol. 1848, p. 012037. IOP Publishing","DOI":"10.1088\/1742-6596\/1848\/1\/012037"},{"key":"6904_CR44","doi-asserted-by":"crossref","unstructured":"Yang C-H, Vemur, BC (2021) Nested grassmanns for dimensionality reduction with applications to shape analysis. In: International Conference on Information Processing in Medical Imaging, pp. 136\u2013149. Springer","DOI":"10.1007\/978-3-030-78191-0_11"},{"key":"6904_CR45","unstructured":"Mohammadi M, Babai M, Wilkinson M (2024) Generalized relevance learning grassmann quantization. arXiv preprint arXiv:2403.09183"},{"issue":"1","key":"6904_CR46","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1109\/TCSVT.2003.818352","volume":"14","author":"Q Liu","year":"2004","unstructured":"Liu Q, Lu H, Ma S (2004) Improving kernel fisher discriminant analysis for face recognition. IEEE Trans Circuits Syst Video Technol 14(1):42\u201349","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"6904_CR47","doi-asserted-by":"crossref","unstructured":"Leibe B, Schiele B (2003) Analyzing appearance and contour based methods for object categorization. In: 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings., vol. 2, p. 409. IEEE","DOI":"10.1109\/CVPR.2003.1211497"},{"issue":"6","key":"6904_CR48","doi-asserted-by":"publisher","first-page":"643","DOI":"10.1109\/34.927464","volume":"23","author":"AS Georghiades","year":"2001","unstructured":"Georghiades AS, Belhumeur PN, Kriegman DJ (2001) From few to many: Illumination cone models for face recognition under variable lighting and pose. IEEE Trans Pattern Anal Mach Intell 23(6):643\u2013660","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"6904_CR49","doi-asserted-by":"crossref","unstructured":"Garcia-Hernando G, Yuan S, Baek S, Kim T-K (2018) First-person hand action benchmark with rgb-d videos and 3d hand pose annotations. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 409\u2013419","DOI":"10.1109\/CVPR.2018.00050"},{"key":"6904_CR50","doi-asserted-by":"crossref","unstructured":"Lai K, Bo L, Ren X, Fox D (2012) Detection-based object labeling in 3d scenes. In: 2012 Ieee International Conference on Robotics and Automation, pp. 1330\u20131337. IEEE","DOI":"10.1109\/ICRA.2012.6225316"},{"key":"6904_CR51","doi-asserted-by":"crossref","unstructured":"Rodriguez MD, Ahmed J, Shah M (2008) Action mach a spatio-temporal maximum average correlation height filter for action recognition. In: 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1\u20138. IEEE","DOI":"10.1109\/CVPR.2008.4587727"},{"key":"6904_CR52","doi-asserted-by":"crossref","unstructured":"Xia L, Chen CC, Aggarwal J (2012) View invariant human action recognition using histograms of 3d joints. In: Computer Vision and Pattern Recognition Workshops (CVPRW), 2012 IEEE Computer Society Conference On, pp. 20\u201327. IEEE","DOI":"10.1109\/CVPRW.2012.6239233"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-024-06904-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-024-06904-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-024-06904-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,17]],"date-time":"2025-01-17T17:38:10Z","timestamp":1737135490000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-024-06904-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,17]]},"references-count":52,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2025,1]]}},"alternative-id":["6904"],"URL":"https:\/\/doi.org\/10.1007\/s11227-024-06904-1","relation":{},"ISSN":["1573-0484"],"issn-type":[{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,17]]},"assertion":[{"value":"27 December 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 January 2025","order":2,"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"}}],"article-number":"397"}}