{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T03:57:46Z","timestamp":1777089466668,"version":"3.51.4"},"reference-count":64,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2022,10,1]],"date-time":"2022-10-01T00:00:00Z","timestamp":1664582400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2022,10,1]],"date-time":"2022-10-01T00:00:00Z","timestamp":1664582400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2022,10,1]],"date-time":"2022-10-01T00:00:00Z","timestamp":1664582400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2022,10,1]],"date-time":"2022-10-01T00:00:00Z","timestamp":1664582400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2022,10,1]],"date-time":"2022-10-01T00:00:00Z","timestamp":1664582400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2022,10,1]],"date-time":"2022-10-01T00:00:00Z","timestamp":1664582400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,10,1]],"date-time":"2022-10-01T00:00:00Z","timestamp":1664582400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["6207409"],"award-info":[{"award-number":["6207409"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2019YFB2204500"],"award-info":[{"award-number":["2019YFB2204500"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Neurocomputing"],"published-print":{"date-parts":[[2022,10]]},"DOI":"10.1016\/j.neucom.2022.08.053","type":"journal-article","created":{"date-parts":[[2022,8,10]],"date-time":"2022-08-10T12:33:51Z","timestamp":1660134831000},"page":"157-166","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":30,"special_numbering":"C","title":["Balanced Spatial Feature Distillation and Pyramid Attention Network for Lightweight Image Super-resolution"],"prefix":"10.1016","volume":"509","author":[{"given":"Garas","family":"Gendy","sequence":"first","affiliation":[]},{"given":"Nabil","family":"Sabor","sequence":"additional","affiliation":[]},{"given":"Jingchao","family":"Hou","sequence":"additional","affiliation":[]},{"given":"Guanghui","family":"He","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"12","key":"10.1016\/j.neucom.2022.08.053_b0005","doi-asserted-by":"crossref","first-page":"3106","DOI":"10.1109\/TMM.2019.2919431","article-title":"Deep learning for single image super-resolution: A brief review","volume":"21","author":"Yang","year":"2019","journal-title":"IEEE Transactions on Multimedia"},{"issue":"10","key":"10.1016\/j.neucom.2022.08.053_b0010","doi-asserted-by":"crossref","first-page":"3365","DOI":"10.1109\/TPAMI.2020.2982166","article-title":"Deep learning for image super-resolution: A survey","volume":"43","author":"Wang","year":"2020","journal-title":"IEEE transactions on pattern analysis and machine intelligence"},{"issue":"3","key":"10.1016\/j.neucom.2022.08.053_b0015","doi-asserted-by":"crossref","first-page":"848","DOI":"10.1016\/j.sigpro.2009.09.002","article-title":"A super-resolution reconstruction algorithm for surveillance images","volume":"90","author":"Zhang","year":"2010","journal-title":"Signal Processing"},{"key":"10.1016\/j.neucom.2022.08.053_b0020","series-title":"in: Proceedings of the IEEE conference on computer vision and pattern recognition","first-page":"6070","article-title":"Simultaneous super-resolution and cross-modality synthesis of 3d medical images using weakly-supervised joint convolutional sparse coding","author":"Huang","year":"2017"},{"key":"10.1016\/j.neucom.2022.08.053_b0025","doi-asserted-by":"crossref","unstructured":"A. Ducournau, R. Fablet, Deep learning for ocean remote sensing: an application of convolutional neural networks for super-resolution on satellite-derived sst data, in: 2016 9th IAPR Workshop on Pattern Recogniton in Remote Sensing (PRRS), IEEE, 2016, pp. 1\u20136.","DOI":"10.1109\/PRRS.2016.7867019"},{"key":"10.1016\/j.neucom.2022.08.053_b0030","series-title":"Proceedings of the IEEE conference on computer vision and pattern recognition workshops","first-page":"136","article-title":"Enhanced deep residual networks for single image super-resolution, in","author":"Lim","year":"2017"},{"key":"10.1016\/j.neucom.2022.08.053_b0035","first-page":"1673","article-title":"Non-local recurrent network for image restoration, in","author":"Liu","year":"2018","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.neucom.2022.08.053_b0040","series-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","first-page":"1646","article-title":"Accurate image super-resolution using very deep convolutional networks, in","author":"Kim","year":"2016"},{"key":"10.1016\/j.neucom.2022.08.053_b0045","unstructured":"J.-H. Kim, J.-H. Choi, M. Cheon, J.-S. Lee, Ram: Residual attention module for single image super-resolution, arXiv preprint arXiv:1811.12043."},{"key":"10.1016\/j.neucom.2022.08.053_b0050","series-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","first-page":"1664","article-title":"Deep back-projection networks for super-resolution, in","author":"Haris","year":"2018"},{"key":"10.1016\/j.neucom.2022.08.053_b0055","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2020.107475","article-title":"Hierarchical dense recursive network for image super-resolution","volume":"107","author":"Jiang","year":"2020","journal-title":"Pattern Recognition"},{"key":"10.1016\/j.neucom.2022.08.053_b0060","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2020.107798","article-title":"Image super-resolution via channel attention and spatial graph convolutional network","volume":"112","author":"Yang","year":"2021","journal-title":"Pattern Recognition"},{"key":"10.1016\/j.neucom.2022.08.053_b0065","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.imavis.2019.02.002","article-title":"Adaptive rational fractal interpolation function for image super-resolution via local fractal analysis","volume":"82","author":"Yao","year":"2019","journal-title":"Image and Vision Computing"},{"key":"10.1016\/j.neucom.2022.08.053_b0070","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.imavis.2019.03.006","article-title":"Image super resolution by dilated dense progressive network","volume":"88","author":"Shamsolmoali","year":"2019","journal-title":"Image and Vision Computing"},{"key":"10.1016\/j.neucom.2022.08.053_b0075","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.neucom.2021.12.090","article-title":"Lightweight hierarchical residual feature fusion network for single-image super-resolution","volume":"478","author":"Qin","year":"2022","journal-title":"Neurocomputing"},{"key":"10.1016\/j.neucom.2022.08.053_b0080","doi-asserted-by":"crossref","unstructured":"C. Dong, C.C. Loy, K. He, X. Tang, Learning a deep convolutional network for image super-resolution, in: European conference on computer vision, Springer, 2014, pp. 184\u2013199.","DOI":"10.1007\/978-3-319-10593-2_13"},{"key":"10.1016\/j.neucom.2022.08.053_b0085","series-title":"in: Proceedings of the IEEE conference on computer vision and pattern recognition","first-page":"5197","article-title":"Single image super-resolution from transformed self-exemplars","author":"Huang","year":"2015"},{"key":"10.1016\/j.neucom.2022.08.053_b0090","unstructured":"J. Gu, G. Xu, Y. Zhang, X. Sun, R. Wen, L. Wang, Wider channel attention network for remote sensing image super-resolution, arXiv preprint arXiv:1812.05329."},{"key":"10.1016\/j.neucom.2022.08.053_b0095","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2019.105448","article-title":"Siamese attentional keypoint network for high performance visual tracking","volume":"193","author":"Gao","year":"2020","journal-title":"Knowledge-based systems"},{"key":"10.1016\/j.neucom.2022.08.053_b0100","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1016\/j.neucom.2021.05.090","article-title":"A lightweight multi-scale channel attention network for image super-resolution","volume":"456","author":"Li","year":"2021","journal-title":"Neurocomputing"},{"key":"10.1016\/j.neucom.2022.08.053_b0105","doi-asserted-by":"crossref","first-page":"35383","DOI":"10.1109\/ACCESS.2020.2974876","article-title":"Lightweight single image super-resolution with multi-scale spatial attention networks","volume":"8","author":"Soh","year":"2020","journal-title":"IEEE Access"},{"key":"10.1016\/j.neucom.2022.08.053_b0110","unstructured":"Y. Mei, Y. Fan, Y. Zhang, J. Yu, Y. Zhou, D. Liu, Y. Fu, T.S. Huang, H. Shi, Pyramid attention networks for image restoration, arXiv preprint arXiv:2004.13824."},{"key":"10.1016\/j.neucom.2022.08.053_b0115","doi-asserted-by":"crossref","unstructured":"H. Liu, F. Cao, C. Wen, Q. Zhang, Lightweight multi-scale residual networks with attention for image super-resolution, Knowledge-Based Systems 203.","DOI":"10.1016\/j.knosys.2020.106103"},{"issue":"2","key":"10.1016\/j.neucom.2022.08.053_b0120","doi-asserted-by":"crossref","first-page":"372","DOI":"10.1109\/TBC.2020.3028356","article-title":"Lightweight image super-resolution by multi-scale aggregation","volume":"67","author":"Wan","year":"2020","journal-title":"IEEE Transactions on Broadcasting"},{"key":"10.1016\/j.neucom.2022.08.053_b0125","doi-asserted-by":"crossref","unstructured":"X. Cheng, X. Li, J. Yang, Triple-attention mixed-link network for single-image super-resolution, Applied Sciences 9 (15).","DOI":"10.3390\/app9152992"},{"key":"10.1016\/j.neucom.2022.08.053_b0130","unstructured":"H. Chen, J. Gu, Z. Zhang, Attention in attention network for image super-resolution, arXiv preprint arXiv:2104.09497."},{"key":"10.1016\/j.neucom.2022.08.053_b0135","doi-asserted-by":"crossref","unstructured":"C. Dong, C.C. Loy, X. Tang, Accelerating the super-resolution convolutional neural network, in: European conference on computer vision, Springer, 2016, pp. 391\u2013407.","DOI":"10.1007\/978-3-319-46475-6_25"},{"key":"10.1016\/j.neucom.2022.08.053_b0140","unstructured":"G. Urban, K.J. Geras, S.E. Kahou, O. Aslan, S. Wang, R. Caruana, A. Mohamed, M. Philipose, M. Richardson, Do deep convolutional nets really need to be deep and convolutional?, arXiv preprint arXiv:1603.05691."},{"key":"10.1016\/j.neucom.2022.08.053_b0145","series-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","first-page":"3929","article-title":"Learning deep cnn denoiser prior for image restoration, in","author":"Zhang","year":"2017"},{"key":"10.1016\/j.neucom.2022.08.053_b0150","series-title":"in: Proceedings of the IEEE conference on computer vision and pattern recognition","first-page":"4700","article-title":"Densely connected convolutional networks","author":"Huang","year":"2017"},{"key":"10.1016\/j.neucom.2022.08.053_b0155","series-title":"Memnet: A persistent memory network for image restoration, in: Proceedings of the IEEE international conference on computer vision","first-page":"4539","author":"Tai","year":"2017"},{"issue":"8","key":"10.1016\/j.neucom.2022.08.053_b0160","doi-asserted-by":"crossref","first-page":"2310","DOI":"10.1109\/TCSVT.2018.2864777","article-title":"Cascaded deep networks with multiple receptive fields for infrared image super-resolution","volume":"29","author":"He","year":"2018","journal-title":"IEEE transactions on circuits and systems for video technology"},{"key":"10.1016\/j.neucom.2022.08.053_b0165","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"3867","article-title":"Feedback network for image super-resolution, in","author":"Li","year":"2019"},{"key":"10.1016\/j.neucom.2022.08.053_b0170","series-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","first-page":"1664","article-title":"Deep back-projection networks for super-resolution, in","author":"Haris","year":"2018"},{"issue":"7","key":"10.1016\/j.neucom.2022.08.053_b0175","doi-asserted-by":"crossref","first-page":"2547","DOI":"10.1109\/TCSVT.2020.3027732","article-title":"Mdcn: Multi-scale dense cross network for image super-resolution","volume":"31","author":"Li","year":"2020","journal-title":"IEEE Transactions on Circuits and Systems for Video Technology"},{"key":"10.1016\/j.neucom.2022.08.053_b0180","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"4917","article-title":"Exploring sparsity in image super-resolution for efficient inference, in","author":"Wang","year":"2021"},{"key":"10.1016\/j.neucom.2022.08.053_b0185","series-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","first-page":"723","article-title":"Fast and accurate single image super-resolution via information distillation network, in","author":"Hui","year":"2018"},{"key":"10.1016\/j.neucom.2022.08.053_b0190","series-title":"Proceedings of the 27th ACM International Conference on Multimedia","first-page":"2024","article-title":"Lightweight image super-resolution with information multi-distillation network, in","author":"Hui","year":"2019"},{"key":"10.1016\/j.neucom.2022.08.053_b0195","first-page":"41","article-title":"Residual feature distillation network for lightweight image super-resolution, in","author":"Liu","year":"2020","journal-title":"European Conference on Computer Vision, Springer"},{"key":"10.1016\/j.neucom.2022.08.053_b0200","series-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","first-page":"770","article-title":"Deep residual learning for image recognition, in","author":"He","year":"2016"},{"key":"10.1016\/j.neucom.2022.08.053_b0205","series-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","first-page":"7794","article-title":"Non-local neural networks, in","author":"Wang","year":"2018"},{"key":"10.1016\/j.neucom.2022.08.053_b0210","series-title":"in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"5690","article-title":"Image super-resolution with cross-scale non-local attention and exhaustive self-exemplars mining","author":"Mei","year":"2020"},{"key":"10.1016\/j.neucom.2022.08.053_b0215","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"11065","article-title":"Second-order attention network for single image super-resolution, in","author":"Dai","year":"2019"},{"issue":"11","key":"10.1016\/j.neucom.2022.08.053_b0220","doi-asserted-by":"crossref","first-page":"3911","DOI":"10.1109\/TCSVT.2019.2915238","article-title":"Channel-wise and spatial feature modulation network for single image super-resolution","volume":"30","author":"Hu","year":"2019","journal-title":"IEEE Transactions on Circuits and Systems for Video Technology"},{"key":"10.1016\/j.neucom.2022.08.053_b0225","doi-asserted-by":"crossref","unstructured":"B. Niu, W. Wen, W. Ren, X. Zhang, L. Yang, S. Wang, K. Zhang, X. Cao, H. Shen, Single image super-resolution via a holistic attention network, in: European conference on computer vision, Springer, 2020, pp. 191\u2013207.","DOI":"10.1007\/978-3-030-58610-2_12"},{"key":"10.1016\/j.neucom.2022.08.053_b0230","series-title":"in: Proceedings of the Asian Conference on Computer Vision","article-title":"Accurate and efficient single image super-resolution with matrix channel attention network","author":"Ma","year":"2020"},{"key":"10.1016\/j.neucom.2022.08.053_b0235","series-title":"in: Proceedings of the 28th ACM International Conference on Multimedia","first-page":"2562","article-title":"Attention cube network for image restoration","author":"Hang","year":"2020"},{"issue":"2","key":"10.1016\/j.neucom.2022.08.053_b0240","doi-asserted-by":"crossref","first-page":"512","DOI":"10.1109\/TCSVT.2020.2988895","article-title":"Multi-grained attention networks for single image super-resolution","volume":"31","author":"Wu","year":"2020","journal-title":"IEEE Transactions on Circuits and Systems for Video Technology"},{"key":"10.1016\/j.neucom.2022.08.053_b0245","doi-asserted-by":"crossref","first-page":"2325","DOI":"10.1109\/TIP.2021.3050856","article-title":"Interpretable detail-fidelity attention network for single image super-resolution","volume":"30","author":"Huang","year":"2021","journal-title":"IEEE Transactions on Image Processing"},{"key":"10.1016\/j.neucom.2022.08.053_b0250","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"2359","article-title":"Residual feature aggregation network for image super-resolution, in","author":"Liu","year":"2020"},{"key":"10.1016\/j.neucom.2022.08.053_b0255","first-page":"56","article-title":"Efficient image super-resolution using pixel attention, in","author":"Zhao","year":"2020","journal-title":"European Conference on Computer Vision, Springer"},{"key":"10.1016\/j.neucom.2022.08.053_b0260","doi-asserted-by":"crossref","unstructured":"G. Gendy, H. Mohammed, N. Sabor, G. He, A deep pyramid attention network for single image super-resolution, in: 2021 9th International Japan-Africa Conference on Electronics, Communications, and Computations (JAC-ECC), IEEE, 2021, pp. 14\u201319.","DOI":"10.1109\/JAC-ECC54461.2021.9691443"},{"key":"10.1016\/j.neucom.2022.08.053_b0265","series-title":"in: Proceedings of the IEEE conference on computer vision and pattern recognition workshops","first-page":"114","article-title":"Ntire 2017 challenge on single image super-resolution: Methods and results","author":"Timofte","year":"2017"},{"key":"10.1016\/j.neucom.2022.08.053_b0270","first-page":"135.1","article-title":"Low-complexity single-image super-resolution based on nonnegative neighbor embedding, in","author":"Bevilacqua","year":"2012","journal-title":"Proceedings of the British Machine Vision Conference"},{"key":"10.1016\/j.neucom.2022.08.053_b0275","series-title":"International conference on curves and surfaces","first-page":"711","article-title":"On single image scale-up using sparse-representations","author":"Zeyde","year":"2010"},{"key":"10.1016\/j.neucom.2022.08.053_b0280","doi-asserted-by":"crossref","unstructured":"D. Martin, C. Fowlkes, D. Tal, J. Malik, A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics, in: Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001, Vol. 2, IEEE, 2001, pp. 416\u2013423.","DOI":"10.1109\/ICCV.2001.937655"},{"issue":"20","key":"10.1016\/j.neucom.2022.08.053_b0285","doi-asserted-by":"crossref","first-page":"21811","DOI":"10.1007\/s11042-016-4020-z","article-title":"Sketch-based manga retrieval using manga109 dataset","volume":"76","author":"Matsui","year":"2017","journal-title":"Multimedia Tools and Applications"},{"issue":"4","key":"10.1016\/j.neucom.2022.08.053_b0290","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/TIP.2003.819861","article-title":"Image quality assessment: from error visibility to structural similarity","volume":"13","author":"Wang","year":"2004","journal-title":"IEEE transactions on image processing"},{"key":"10.1016\/j.neucom.2022.08.053_b0295","unstructured":"D.P. Kingma, J. Ba, Adam: A method for stochastic optimization, arXiv preprint arXiv:1412.6980."},{"key":"10.1016\/j.neucom.2022.08.053_b0300","unstructured":"A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, A. Lerer, Automatic differentiation in pytorch."},{"key":"10.1016\/j.neucom.2022.08.053_b0305","series-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","first-page":"624","article-title":"Deep laplacian pyramid networks for fast and accurate super-resolution, in","author":"Lai","year":"2017"},{"key":"10.1016\/j.neucom.2022.08.053_b0310","series-title":"Proceedings of the European Conference on Computer Vision (ECCV)","first-page":"252","article-title":"Fast, accurate and lightweight super-resolution with cascading residual network, in","author":"Ahn","year":"2018"},{"key":"10.1016\/j.neucom.2022.08.053_b0315","unstructured":"Z. Lu, H. Liu, J. Li, L. Zhang, Efficient transformer for single image super-resolution, arXiv preprint arXiv:2108.11084."},{"key":"10.1016\/j.neucom.2022.08.053_b0320","unstructured":"Y. Zhang, K. Li, K. Li, B. Zhong, Y. Fu, Residual non-local attention networks for image restoration, arXiv preprint arXiv:1903.10082."}],"container-title":["Neurocomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231222010396?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231222010396?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T20:48:33Z","timestamp":1760388513000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0925231222010396"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10]]},"references-count":64,"alternative-id":["S0925231222010396"],"URL":"https:\/\/doi.org\/10.1016\/j.neucom.2022.08.053","relation":{},"ISSN":["0925-2312"],"issn-type":[{"value":"0925-2312","type":"print"}],"subject":[],"published":{"date-parts":[[2022,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Balanced Spatial Feature Distillation and Pyramid Attention Network for Lightweight Image Super-resolution","name":"articletitle","label":"Article Title"},{"value":"Neurocomputing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neucom.2022.08.053","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2022 Elsevier B.V. All rights reserved.","name":"copyright","label":"Copyright"}]}}