{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,2]],"date-time":"2025-05-02T13:34:46Z","timestamp":1746192886728,"version":"3.37.3"},"reference-count":67,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2022,2,2]],"date-time":"2022-02-02T00:00:00Z","timestamp":1643760000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,2,2]],"date-time":"2022-02-02T00:00:00Z","timestamp":1643760000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2023,3]]},"DOI":"10.1007\/s11042-021-11843-7","type":"journal-article","created":{"date-parts":[[2022,2,2]],"date-time":"2022-02-02T17:02:56Z","timestamp":1643821376000},"page":"9649-9666","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Enhanced Graph Representations for Graph Convolutional Network Models"],"prefix":"10.1007","volume":"82","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0680-2691","authenticated-orcid":false,"given":"Vandana","family":"Bhattacharjee","sequence":"first","affiliation":[]},{"given":"Raj","family":"Sahu","sequence":"additional","affiliation":[]},{"given":"Amit","family":"Dutta","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,2,2]]},"reference":[{"issue":"Nov","key":"11843_CR1","first-page":"2399","volume":"7","author":"M Belkin","year":"2006","unstructured":"Belkin M, Niyogi P, Sindhwani V (2006) Manifold regularization: A geometric framework for learning from labeled and unlabeled examples. J Mach Learn Res (JMLR) 7(Nov):2399\u20132434","journal-title":"J Mach Learn Res (JMLR)"},{"key":"11843_CR2","unstructured":"Berger-Wolf T, Taheri A, Gimpel K (2018) Learning graph representations with recurrent neural network autoencoders. In: KDD\u201918."},{"key":"11843_CR3","doi-asserted-by":"publisher","unstructured":"Chen L-C, Papandreou G, Kokkinos I, Murphy K, Yuille AL (2018) Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans Pattern Anal Mach Intell 40(4):834\u2013848. https:\/\/doi.org\/10.1109\/TPAMI.2017.2699184. Epub 2017 Apr 27.","DOI":"10.1109\/TPAMI.2017.2699184"},{"key":"11843_CR4","doi-asserted-by":"crossref","unstructured":"Cho K, van Merri\u00ebnboer B, Bahdanau D, Bengio Y (2014) On the properties of neural machine translation: encoder\u2013decoder approaches. Syntax, Semantics and Structure in Statistical Translation, p 103","DOI":"10.3115\/v1\/W14-4012"},{"issue":"2","key":"11843_CR5","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1093\/comnet\/cnu039","volume":"3","author":"JR Clough","year":"2015","unstructured":"Clough JR, Gollings J, Loach TV, Evans TS (2015) Transitive reduction of citation networks. J Complex Netw 3(2):189\u2013203","journal-title":"J Complex Netw"},{"key":"11843_CR6","unstructured":"Cui Z, Henrickson K, Ke R (2018) Traffic graph convolutional recurrent neural network: a deep learning framework for network-scale traffic learning and forecasting. arXiv preprintarXiv:1802.07007"},{"key":"11843_CR7","unstructured":"Dai H, Dai B, Song L (2016) Discriminative embeddings of latent variable models for structured data. Proceedings of the 33 rd International Conference on Machine Learning, New York, NY, USA, 2016"},{"key":"11843_CR8","unstructured":"Defferrard M, Bresson X, Vandergheynst P (2016) Convolutional neural networks on graphs with fast localized spectral filtering. In: 30th Conference on Neural Information Processing Systems (NIPS 2016), Barcelona, Spain"},{"key":"11843_CR9","doi-asserted-by":"crossref","unstructured":"Deng J, Dong W, Socher R, Li L-J, Li K, Fei Fei L (2009) ImageNet: A large-scale hierarchical image database. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"11843_CR10","unstructured":"Fang Y, Ronald R (2001) Lattices in citation networks: An investigation into the structure of citation graphs. Scientometrics 50(2):273\u2013287"},{"key":"11843_CR11","doi-asserted-by":"crossref","unstructured":"Gao H, Wang Z, Ji S (2018) Large-scale learnable graph convolutional networks. arXiv:1808.03965v1 [cs.LG]","DOI":"10.1145\/3219819.3219947"},{"key":"11843_CR12","doi-asserted-by":"crossref","unstructured":"Gehring J, Auli M, Grangier D, Dauphin YN (2017) A convolutional encoder model for neural machine translation. Annual Meeting of the Association for Computational Linguistics","DOI":"10.18653\/v1\/P17-1012"},{"key":"11843_CR13","doi-asserted-by":"crossref","unstructured":"Gong C, Tao D, Liu W, Liu L, Yang J (2017) Label propagation via teaching-to-learn and learning-to-teach. IEEE Trans Neural Netw Learn Syst 28 (2017):1452\u20131465","DOI":"10.1109\/TNNLS.2016.2514360"},{"key":"11843_CR14","doi-asserted-by":"crossref","unstructured":"Gori M, Monfardini G, Scarselli F (2005) A new model for learning in graph domains. In: Neural Networks, 2005. IJCNN\u201905. Proceedings. 2005 IEEE International Joint Conference on, volume\u00a02, pp 729\u2013734","DOI":"10.1109\/IJCNN.2005.1555942"},{"key":"11843_CR15","doi-asserted-by":"crossref","unstructured":"Grover A, Leskovec J (2016) node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, New York","DOI":"10.1145\/2939672.2939754"},{"key":"11843_CR16","doi-asserted-by":"crossref","unstructured":"Hammond DK, Vandergheynst P, Gribonval R (2011) Wavelets on graphs via spectral graph theory. Appl Comput Harmon Anal 30(2):129\u2013150","DOI":"10.1016\/j.acha.2010.04.005"},{"key":"11843_CR17","unstructured":"Hamilton WL, Ying R, Leskovec J (2017) Inductive representation learning on large graphs. In: Advances in Neural Information processing Systems"},{"key":"11843_CR18","doi-asserted-by":"crossref","unstructured":"He K, Gkioxari G, Doll\u00e1r P, Girshick R (2017) Mask r-cnn. IEEE International Conference on Computer Vision","DOI":"10.1109\/ICCV.2017.322"},{"key":"11843_CR19","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"11843_CR20","unstructured":"Joan B, Zaremba W, Szlam A, LeCun Y (2014) Spectral networks and locally connected networks on graphs. CoRR abs\/1312.6203"},{"key":"11843_CR21","unstructured":"Karasuyama M, Mamitsuka H (2013) Manifold-based similarity adaptation for label propagation. In: Advances in Neural Information Processing Systems"},{"key":"11843_CR22","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, 1097\u20131105"},{"issue":"7553","key":"11843_CR23","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436\u2013444","journal-title":"Nature"},{"key":"11843_CR24","doi-asserted-by":"crossref","unstructured":"LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278\u20132324","DOI":"10.1109\/5.726791"},{"key":"11843_CR25","unstructured":"Leo E, Ronald R (1990) Introduction to Informetrics: quantitative methods in library, documentation and information science. Elsevier Science Publishers,\u00a0Amsterdam, p 228. ISBN 0-444-88493-9"},{"key":"11843_CR26","unstructured":"Leow YY, Laurent T, Bresson X (2019) GraphTSNE: a visualization technique for graph-structured data. arXiv preprint arXiv:1904.06915"},{"key":"11843_CR27","doi-asserted-by":"crossref","unstructured":"Li Q, Han Z, Wu XM (2018) Deeper insights into graph convolutional networks for semi-supervised learning. In: The 32nd AAAI Conference on Artificial Intelligence","DOI":"10.1609\/aaai.v32i1.11604"},{"key":"11843_CR28","unstructured":"Liao R, Zhao Z, Urtasun R, Zemel RS, Lanczosnet (2019) Multi-scale deep graph convolutional networks. In: Proceedings of the 7th International Conference on Learning Representations"},{"key":"11843_CR29","unstructured":"Liu Y, Lee J, Park M, Kim S, Yang E, Hwang SJ, Yang Y (2019a) Learning to propagate labels: Transductive propagation network for few-shot learning. In: Proceedings of the 7th International Conference on Learning Representations"},{"key":"11843_CR30","doi-asserted-by":"crossref","unstructured":"Luong M-T, Pham H, Manning CD (2015) Effective approaches to attention-based neural machine translation. Conference on Empirical Methods in Natural Language Processing","DOI":"10.18653\/v1\/D15-1166"},{"key":"11843_CR31","unstructured":"Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J (2013) Distributed representations of words and phrases and their compositionality. In: Advances in neural information processing systems (NIPS), pp 3111\u20133119"},{"key":"11843_CR32","unstructured":"Monti F, Bronstein M, Bresson X (2017) Geometric matrix completion with recurrent multi-graph neural networks. 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA, pp 3697\u20133707"},{"key":"11843_CR33","doi-asserted-by":"crossref","unstructured":"Perozzi B, Al-Rfou R, Skiena S (2014) Deepwalk: Online learning of social representations. In: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 701\u2013710. ACM, New York","DOI":"10.1145\/2623330.2623732"},{"key":"11843_CR34","unstructured":"Qu M, Bengio Y, Tang J, Gmnn (2019) Graph markov neural networks. In: Proceedings of the 36th International Conference on Machine Learning"},{"key":"11843_CR35","unstructured":"Ren S, He K, Girshick R, Sun J (2015) Faster R-CNN: Towards real-time object detection with region proposal networks. In: Advances in neural information processing systems, 91\u201399"},{"key":"11843_CR36","doi-asserted-by":"crossref","unstructured":"Seo Y, Defferrard M, Vandergheynst P (2018) Structured sequence modeling with graph convolutional recurrent networks. International Conference on Neural Information Processing, 362-373","DOI":"10.1007\/978-3-030-04167-0_33"},{"issue":"1","key":"11843_CR37","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1109\/TNN.2008.2005605","volume":"20","author":"F Scarselli","year":"2009","unstructured":"Scarselli F, Gori M, Tsoi AC, Hagenbuchner M, Monfardini G (2009) The graph neural network model. IEEE Trans Neural Networks 20(1):61\u201380","journal-title":"IEEE Trans Neural Networks"},{"issue":"3","key":"11843_CR38","first-page":"93","volume":"29","author":"P Sen","year":"2008","unstructured":"Sen P, Namata G, Bilgic M, Getoor L, Galligher B, Eliassi-Rad T (2008) Collective classification in network data. AI Mag 29(3):93","journal-title":"AI Mag"},{"key":"11843_CR39","unstructured":"Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. In: Proceedings of the International Conference on Learning Representations"},{"key":"11843_CR40","doi-asserted-by":"publisher","first-page":"8355","DOI":"10.1007\/s11042-020-09885-4","volume":"80","author":"X Shi","year":"2021","unstructured":"Shi X, Lv F, Seng D, Zhang J, Chen J, Xing B (2021) Visualizing and understanding graph convolutional network. Multimed Tools Appl 80:8355\u20138375. https:\/\/doi.org\/10.1007\/s11042-020-09885-4","journal-title":"Multimed Tools Appl"},{"key":"11843_CR41","unstructured":"Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: A simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929\u20131958"},{"key":"11843_CR42","doi-asserted-by":"publisher","unstructured":"Sun Y, Liang J, Niu P (2021) Personalized recommendation of english learning based on knowledge graph and graph convolutional network. In: Sun X, Zhang X, Xia Z, Bertino E (eds) Artificial Intelligence and Security. ICAIS 2021, vol 12737. Springer, Cham. https:\/\/doi.org\/10.1007\/978-3-030-78612-0_13","DOI":"10.1007\/978-3-030-78612-0_13"},{"key":"11843_CR43","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1\u20139","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"11843_CR44","doi-asserted-by":"crossref","unstructured":"Tang J, Qu M, Wang M, Zhang M, Yan J, Mei Q (2015) Line: Large-scale information network embedding. In: Proceedings of the 24th International Conference on World Wide Web. ACM, New York,\u00a0pp 1067\u20131077","DOI":"10.1145\/2736277.2741093"},{"key":"11843_CR45","unstructured":"Thomas K, Welling M (2017) Semi-Supervised Classification with Graph Convolutional Networks. In: Proceedings of the 5th International Conference on Learning representation"},{"key":"11843_CR46","unstructured":"Veli\u010dkovi\u0107 P, Cucurull G, Casanova A et al (2018) Graph attention networks. In: Proceedings of the International Conference on Learning Representations"},{"key":"11843_CR47","doi-asserted-by":"crossref","unstructured":"Wang D, Cui P, Zhu W (2016) Structural deep network embedding. In: Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, New York,\u00a0pp 1225\u20131234","DOI":"10.1145\/2939672.2939753"},{"key":"11843_CR48","doi-asserted-by":"publisher","unstructured":"Wang F, Zhang C (2008) Label propagation through linear neighborhoods. IEEE Trans Knowl Data Eng 20(1):55\u201367. https:\/\/doi.org\/10.1109\/TKDE.2007.190672","DOI":"10.1109\/TKDE.2007.190672"},{"key":"11843_CR49","doi-asserted-by":"crossref","unstructured":"Wang H, Leskovec J (2020) Unifying graph convolutional neural networks and label propagation. arXiv:2002.06755v1 [cs.LG]","DOI":"10.1145\/3490478"},{"issue":"1","key":"11843_CR50","first-page":"105","volume":"11","author":"L Wangzhong","year":"2007","unstructured":"Wangzhong L, Janssen J, Milios E, Japkowic N, Yongzheng Z (2007) Node similarity in the citation graph. Knowl Inf Syst 11(1):105\u2013129","journal-title":"Knowl Inf Syst"},{"key":"11843_CR51","doi-asserted-by":"crossref","unstructured":"Weston J, Ratle F, Mobahi H, Collobert R (2012) Deep learning via semi supervised embedding. Neural Networks: Tricks of the Trade. Springer, Berlin, pp 639\u2013655","DOI":"10.1007\/978-3-642-35289-8_34"},{"key":"11843_CR52","doi-asserted-by":"publisher","first-page":"22907","DOI":"10.1007\/s11042-020-08803-y","volume":"80","author":"G Xiao","year":"2021","unstructured":"Xiao G, Wang R, Zhang C et al (2021) Demand prediction for a public bike sharing program based on spatio-temporal graph convolutional networks. Multimed Tools Appl 80:22907\u201322925. https:\/\/doi.org\/10.1007\/s11042-020-08803-y","journal-title":"Multimed Tools Appl"},{"key":"11843_CR53","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-020-10107-0","author":"L Xiao","year":"2020","unstructured":"Xiao L, Hu X, Chen Y et al (2020) Multi-head self-attention based gated graph convolutional networks for aspect-based sentiment classification. Multimed Tools Appl. https:\/\/doi.org\/10.1007\/s11042-020-10107-0","journal-title":"Multimed Tools Appl"},{"key":"11843_CR54","unstructured":"Xu K, Hu W, Leskovec J, Jegelka S (2019) How powerful are graph neural networks? In: Proceedings of the 7th International Conference on Learning Representations"},{"key":"11843_CR55","unstructured":"Xu K, Li C, Tian Y, Sonobe T, Kawarabayashi K-i, Jegelka S (2018) Representation learning on graphs with jumping knowledge networks. In: Proceedings of the 35th International Conference on Machine Learning"},{"key":"11843_CR56","doi-asserted-by":"crossref","unstructured":"Yi L, Su H, Guo X et al (2017) Syncspeccnn: synchronized spectral cnn for 3d shape segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2282-2290","DOI":"10.1109\/CVPR.2017.697"},{"key":"11843_CR57","doi-asserted-by":"crossref","unstructured":"Ying R, He R, Chen K (2018) et.al., Graph convolutional networks for web-scale recommender systems. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 974-983","DOI":"10.1145\/3219819.3219890"},{"key":"11843_CR58","doi-asserted-by":"crossref","unstructured":"Yu B, Yin H, Zhu Z (2018) Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence, 3634-3640","DOI":"10.24963\/ijcai.2018\/505"},{"issue":"6","key":"11843_CR59","doi-asserted-by":"publisher","first-page":"664","DOI":"10.1007\/s10115-003-0128-3","volume":"6","author":"A Yuan","year":"2004","unstructured":"Yuan A, Jeannette J, Evangelos EM (2004) Characterizing and mining the citation graph of the computer science literature. Knowl Inf Syst 6(6):664\u2013678","journal-title":"Knowl Inf Syst"},{"key":"11843_CR60","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-021-11033-5","author":"B Zhang","year":"2021","unstructured":"Zhang B, Liu M, Zhou B, Liu X (2021) Graph learning in low dimensional space for graph convolutional networks. Multimed Tools Appl. https:\/\/doi.org\/10.1007\/s11042-021-11033-5","journal-title":"Multimed Tools Appl"},{"key":"11843_CR61","doi-asserted-by":"crossref","unstructured":"Zhang Z, Wang J, Mlle (2007) Modified locally linear embedding using multiple weights. In: Adv Neural Inf Process Syst 19:1593\u20131600","DOI":"10.7551\/mitpress\/7503.003.0204"},{"key":"11843_CR62","doi-asserted-by":"crossref","unstructured":"Zhao, Dangzhi Z, Andreas S (2015) Analysis and visualization of citation networks. Morgan & Claypool Publishers,\u00a0San Rafael. ISBN 978-1-60845-939-1","DOI":"10.1007\/978-3-031-02291-3"},{"key":"11843_CR63","unstructured":"Zhou K, Song Q, Huang X, Hu X (\u00a02019) Auto-GNN: Neural architecture search of graph neural networks. arXiv:1909.03184v2 [cs.LG]"},{"key":"11843_CR64","unstructured":"Zhu X, Ghahramani Z, Lafferty J (2003) Semi-supervised learning using gaussian fields and harmonic functions. In: International Conference on Machine Learning (ICML), vol 3, pp 912\u2013919"},{"key":"11843_CR65","doi-asserted-by":"publisher","first-page":"16247","DOI":"10.1007\/s11042-020-08790-0","volume":"80","author":"X Zhu","year":"2021","unstructured":"Zhu X, Mao Z, Chen Z et al (2021) Object-difference drived graph convolutional networks for visual question answering. Multimed Tools Appl 80:16247\u201316265. https:\/\/doi.org\/10.1007\/s11042-020-08790-0","journal-title":"Multimed Tools Appl"},{"key":"11843_CR66","unstructured":"Zhu X, Lafferty J, Rosenfeld R (2005) Semi-supervised learning with graphs. PhD thesis, Carnegie Mellon University, school of language technologies institute"},{"issue":"14","key":"11843_CR67","doi-asserted-by":"publisher","first-page":"i190","DOI":"10.1093\/bioinformatics\/btx252","volume":"33","author":"M Zitnik","year":"2017","unstructured":"Zitnik M, Leskovec J (2017) Predicting multicellular function through multi-layer tissue networks. Bioinformatics 33(14):i190\u2013i198","journal-title":"Bioinformatics"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-021-11843-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-021-11843-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-021-11843-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,16]],"date-time":"2023-11-16T20:52:55Z","timestamp":1700167975000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-021-11843-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,2]]},"references-count":67,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2023,3]]}},"alternative-id":["11843"],"URL":"https:\/\/doi.org\/10.1007\/s11042-021-11843-7","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"type":"print","value":"1380-7501"},{"type":"electronic","value":"1573-7721"}],"subject":[],"published":{"date-parts":[[2022,2,2]]},"assertion":[{"value":"16 March 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 August 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 December 2021","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 February 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"None.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest\/Competing interests"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"All the authors are aware of this submission. They have reviewed and consented to participate in this journal submission.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"All the authors are aware of this submission. They have reviewed and consented to participate in this journal submission.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}]}}