{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T16:07:14Z","timestamp":1780416434988,"version":"3.54.1"},"reference-count":58,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2022,8,8]],"date-time":"2022-08-08T00:00:00Z","timestamp":1659916800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,8,8]],"date-time":"2022-08-08T00:00:00Z","timestamp":1659916800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61871278"],"award-info":[{"award-number":["61871278"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2021SCU12061"],"award-info":[{"award-number":["2021SCU12061"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100018542","name":"Natural Science Foundation of Sichuan Province","doi-asserted-by":"publisher","award":["2022NSFSC0922"],"award-info":[{"award-number":["2022NSFSC0922"]}],"id":[{"id":"10.13039\/501100018542","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2023,4]]},"DOI":"10.1007\/s10489-022-04008-y","type":"journal-article","created":{"date-parts":[[2022,8,8]],"date-time":"2022-08-08T12:03:21Z","timestamp":1659960201000},"page":"9396-9408","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Image classification based on self-distillation"],"prefix":"10.1007","volume":"53","author":[{"given":"Yuting","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Linbo","family":"Qing","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2967-2682","authenticated-orcid":false,"given":"Xiaohai","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Honggang","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,8,8]]},"reference":[{"issue":"1","key":"4008_CR1","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1016\/j.csi.2011.05.001","volume":"34","author":"X Wang","year":"2012","unstructured":"Wang X, Chen Z, Yun J (2012) An effective method for color image retrieval based on texture. Comput Stand Interfaces 34(1):31\u201335","journal-title":"Comput Stand Interfaces"},{"issue":"10","key":"4008_CR2","doi-asserted-by":"publisher","first-page":"3293","DOI":"10.1016\/j.patcog.2014.04.020","volume":"47","author":"X Wang","year":"2014","unstructured":"Wang X, Wang Z (2014) The method for image retrieval based on multi-factors correlation utilizing block truncation coding. Pattern Recognit 47(10):3293\u20133303","journal-title":"Pattern Recognit"},{"issue":"1","key":"4008_CR3","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1016\/j.jvcir.2012.10.003","volume":"24","author":"X Wang","year":"2013","unstructured":"Wang X, Wang Z (2013) A novel method for image retrieval based on structure elements\u2019 descriptor. J Vis Commun Image Represent 24(1):63\u201374","journal-title":"J Vis Commun Image Represent"},{"key":"4008_CR4","doi-asserted-by":"publisher","first-page":"176","DOI":"10.1016\/j.inffus.2018.03.006","volume":"44","author":"S Unar","year":"2018","unstructured":"Unar S, Wang X, Zhang C (2018) Visual and textual information fusion using kernel method for content based image retrieval. Inform Fusion 44:176\u2013187","journal-title":"Inform Fusion"},{"key":"4008_CR5","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1016\/j.knosys.2019.05.001","volume":"179","author":"S Unar","year":"2019","unstructured":"Unar S, Wang X, Wang C, Wang Y (2019) A decisive content based image retrieval approach for feature fusion in visual and textual images. Knowl-Based Syst 179:8\u201320","journal-title":"Knowl-Based Syst"},{"issue":"12","key":"4008_CR6","doi-asserted-by":"publisher","first-page":"4440","DOI":"10.1109\/TCSVT.2019.2960507","volume":"30","author":"C Wang","year":"2019","unstructured":"Wang C, Wang X, Xia Z, Ma B, Shi Y (2019) Image description with polar harmonic fourier moments. IEEE Trans Circuits Syst Video Technol 30(12):4440\u20134452","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"4008_CR7","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1016\/j.ins.2018.08.028","volume":"470","author":"C Wang","year":"2019","unstructured":"Wang C, Wang X, Xia Z, Zhang C (2019) Ternary radial harmonic fourier moments based robust stereo image zero-watermarking algorithm. Inf Sci 470:109\u2013120","journal-title":"Inf Sci"},{"key":"4008_CR8","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, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"4008_CR9","doi-asserted-by":"crossref","unstructured":"Xie S, Girshick R, Doll\u00e1r P, Tu Z, He K (2017) Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1492\u20131500","DOI":"10.1109\/CVPR.2017.634"},{"key":"4008_CR10","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4700\u20134708","DOI":"10.1109\/CVPR.2017.243"},{"issue":"1","key":"4008_CR11","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1109\/MSP.2017.2765695","volume":"35","author":"Y Cheng","year":"2018","unstructured":"Cheng Y, Wang D, Zhou P, Zhang T (2018) Model compression and acceleration for deep neural networks: the principles, progress, and challenges. IEEE Signal Proc Mag 35(1):126\u2013 136","journal-title":"IEEE Signal Proc Mag"},{"key":"4008_CR12","doi-asserted-by":"crossref","unstructured":"Bashir D, Montanez GD, Sehra S, Segura PS, Lauw J (2020) An information-theoretic perspective on overfitting and underfitting. In: Australasian joint conference on artificial intelligence, Springer, pp 347\u2013358","DOI":"10.1007\/978-3-030-64984-5_27"},{"key":"4008_CR13","doi-asserted-by":"crossref","unstructured":"Yim J, Joo D, Bae J, Kim J (2017) A gift from knowledge distillation: fast optimization, network minimization and transfer learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4133\u20134141","DOI":"10.1109\/CVPR.2017.754"},{"issue":"6","key":"4008_CR14","doi-asserted-by":"publisher","first-page":"1789","DOI":"10.1007\/s11263-021-01453-z","volume":"129","author":"J Gou","year":"2021","unstructured":"Gou J, Yu B, Maybank SJ, Tao D (2021) Knowledge distillation: a survey. Int J Comput Vis 129(6):1789\u20131819","journal-title":"Int J Comput Vis"},{"key":"4008_CR15","unstructured":"Furlanello T, Lipton Z, Tschannen M, Itti L, Anandkumar A (2018) Born again neural networks. In: International conference on machine learning, PMLR, pp 1607\u20131616"},{"key":"4008_CR16","doi-asserted-by":"crossref","unstructured":"Yuan L, Tay FE, Li G, Wang T, Feng J (2020) Revisiting knowledge distillation via label smoothing regularization. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3903\u20133911","DOI":"10.1109\/CVPR42600.2020.00396"},{"key":"4008_CR17","doi-asserted-by":"crossref","unstructured":"Ji M, Shin S, Hwang S, Park G, Moon I (2021) Refine myself by teaching myself: feature refinement via self-knowledge distillation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 10664\u201310673","DOI":"10.1109\/CVPR46437.2021.01052"},{"key":"4008_CR18","doi-asserted-by":"crossref","unstructured":"Zhang L, Song J, Gao A, Chen J, Bao C, Ma K (2019) Be your own teacher: improve the performance of convolutional neural networks via self distillation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3713\u20133722","DOI":"10.1109\/ICCV.2019.00381"},{"issue":"1","key":"4008_CR19","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1080\/21681163.2019.1608307","volume":"8","author":"T Li","year":"2020","unstructured":"Li T, Jin D, Du C, Cao X, Chen H, Yan J, Chen N, Chen Z, Feng Z, Liu S (2020) The image-based analysis and classification of urine sediments using a lenet-5 neural network. Comput Methods Biomech Biomed Eng Imaging Vis 8(1):109\u2013114","journal-title":"Comput Methods Biomech Biomed Eng Imaging Vis"},{"key":"4008_CR20","doi-asserted-by":"crossref","unstructured":"Wang W, Liu Q, Wang W (2021) Pyramid-dilated deep convolutional neural network for crowd counting. Appl Intell:1\u201313","DOI":"10.3390\/sym13040703"},{"key":"4008_CR21","doi-asserted-by":"crossref","unstructured":"Zou W, Zhang D, Lee D (2022) A new multi-feature fusion based convolutional neural network for facial expression recognition. Appl Intell:1\u201312","DOI":"10.1007\/s10489-021-02575-0"},{"issue":"6","key":"4008_CR22","doi-asserted-by":"publisher","first-page":"3241","DOI":"10.1007\/s10489-020-01944-5","volume":"51","author":"Q Yu","year":"2021","unstructured":"Yu Q, Kavitha MS, Kurita T (2021) Mixture of experts with convolutional and variational autoencoders for anomaly detection. Appl Intell 51(6):3241\u20133254","journal-title":"Appl Intell"},{"key":"4008_CR23","doi-asserted-by":"crossref","unstructured":"Wang F, Jiang M, Qian C, Yang S, Li C, Zhang H, Wang X, Tang X (2017) Residual attention network for image classification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3156\u20133164","DOI":"10.1109\/CVPR.2017.683"},{"key":"4008_CR24","doi-asserted-by":"crossref","unstructured":"Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7132\u20137141","DOI":"10.1109\/CVPR.2018.00745"},{"key":"4008_CR25","doi-asserted-by":"crossref","unstructured":"Bucilua\u030c C, Caruana R, Niculescu-Mizil A (2006) Model compression. In: Proceedings of the 12th ACM SIGKDD international conference on knowledge discovery and data mining, pp 535\u2013541","DOI":"10.1145\/1150402.1150464"},{"key":"4008_CR26","unstructured":"Hinton G, Vinyals O, Dean J et al (2015) Distilling the knowledge in a neural network. Proc Neural Inform Process Syst, vol 2 (7)"},{"key":"4008_CR27","unstructured":"Adriana R, Nicolas B, Ebrahimi KS, Antoine C, Carlo G, Yoshua B (2015) Fitnets: hints for thin deep nets. In: Proceedings of the international conference on learning representations, pp 1\u201313"},{"key":"4008_CR28","unstructured":"Komodakis N, Zagoruyko S (2017) Paying more attention to attention: improving the performance of convolutional neural networks via attention transfer. In: Proceedings of the international conference on learning representations, pp 1\u201313"},{"key":"4008_CR29","doi-asserted-by":"crossref","unstructured":"Salehi M, Sadjadi N, Baselizadeh S, Rohban MH, Rabiee HR (2021) Multiresolution knowledge distillation for anomaly detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 14902\u201314912","DOI":"10.1109\/CVPR46437.2021.01466"},{"issue":"4","key":"4008_CR30","doi-asserted-by":"publisher","first-page":"4582","DOI":"10.1007\/s10489-021-02634-6","volume":"52","author":"N Dong","year":"2022","unstructured":"Dong N, Zhang Y, Ding M, Xu S, Bai Y (2022) One-stage object detection knowledge distillation via adversarial learning. Appl Intell 52(4):4582\u20134598","journal-title":"Appl Intell"},{"issue":"2","key":"4008_CR31","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1007\/s10489-020-01858-2","volume":"51","author":"OK Oyedotun","year":"2021","unstructured":"Oyedotun OK, Shabayek AER, Aouada D, Ottersten B (2021) Deep network compression with teacher latent subspace learning and lasso. Appl Intell 51(2):834\u2013853","journal-title":"Appl Intell"},{"key":"4008_CR32","doi-asserted-by":"crossref","unstructured":"Yun S, Park J, Lee K, Shin J (2020) Regularizing class-wise predictions via self-knowledge distillation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 13876\u201313885","DOI":"10.1109\/CVPR42600.2020.01389"},{"issue":"7","key":"4008_CR33","doi-asserted-by":"publisher","first-page":"11291","DOI":"10.1007\/s11042-020-10188-x","volume":"80","author":"W Cai","year":"2021","unstructured":"Cai W, Liu B, Wei Z, Li M, Kan J (2021) Tardb-net: triple-attention guided residual dense and bilstm networks for hyperspectral image classification. Multimed Tools Appl 80(7):11291\u201311312","journal-title":"Multimed Tools Appl"},{"key":"4008_CR34","doi-asserted-by":"publisher","first-page":"1949","DOI":"10.1109\/TIP.2021.3049959","volume":"30","author":"Z Zhang","year":"2021","unstructured":"Zhang Z, Lin Z, Xu J, Jin W-D, Lu S-P, Fan D-P (2021) Bilateral attention network for rgb-d salient object detection. IEEE Trans Image Process 30:1949\u20131961","journal-title":"IEEE Trans Image Process"},{"issue":"20","key":"4008_CR35","doi-asserted-by":"publisher","first-page":"13053","DOI":"10.1007\/s00500-021-06146-w","volume":"25","author":"W He","year":"2021","unstructured":"He W, Pan C, Xu W, Zhang N (2021) Multi-attention embedded network for salient object detection. Soft Comput 25(20):13053\u201313067","journal-title":"Soft Comput"},{"key":"4008_CR36","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1016\/j.future.2022.02.007","volume":"132","author":"W Xiao","year":"2022","unstructured":"Xiao W, Liu H, Ma Z, Chen W (2022) Attention-based deep neural network for driver behavior recognition. Futur Gener Comput Syst 132:152\u2013161","journal-title":"Futur Gener Comput Syst"},{"issue":"12","key":"4008_CR37","doi-asserted-by":"publisher","first-page":"e2021474118","DOI":"10.1073\/pnas.2021474118","volume":"118","author":"CT Ellis","year":"2021","unstructured":"Ellis CT, Skalaban LJ, Yates TS, Turk-Browne NB (2021) Attention recruits frontal cortex in human infants. Proceedings of the National Academy of Sciences 118(12):e2021474118. National Acad Sciences","journal-title":"Proceedings of the National Academy of Sciences"},{"key":"4008_CR38","doi-asserted-by":"crossref","unstructured":"Woo S, Park J, Lee J, Kweon IS (2018) Cbam: convolutional block attention module. In: Proceedings of the european conference on computer vision, pp 3\u201319","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"4008_CR39","doi-asserted-by":"crossref","unstructured":"Lu E, Hu X (2021) Image super-resolution via channel attention and spatial attention. Appl Intell:1\u201329","DOI":"10.1007\/s10489-021-02464-6"},{"key":"4008_CR40","doi-asserted-by":"crossref","unstructured":"Niu J, Xie Z, Li Y, Cheng S, Fan J (2021) Scale fusion light cnn for hyperspectral face recognition with knowledge distillation and attention mechanism. Appl Intell:1\u201315","DOI":"10.1007\/s10489-021-02721-8"},{"issue":"2","key":"4008_CR41","doi-asserted-by":"publisher","first-page":"652","DOI":"10.1109\/TPAMI.2019.2938758","volume":"43","author":"S Gao","year":"2019","unstructured":"Gao S, Cheng M, Zhao K, Zhang X, Yang M, Torr P (2019) Res2net: a new multi-scale backbone architecture. IEEE Trans Pattern Anal Mach Intell 43(2):652\u2013662","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"4008_CR42","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I (2017) Attention is all you need. Adv Neural Inf Proc Syst, vol 30"},{"key":"4008_CR43","doi-asserted-by":"crossref","unstructured":"Han K, Wang Y, Chen H, Chen X, Guo J, Liu Z, Tang Y, Xiao A, Xu C, Xu Y et al (2022) A survey on vision transformer. IEEE Trans Pattern Anal Mach Intell. IEEE","DOI":"10.1109\/TPAMI.2022.3152247"},{"issue":"2","key":"4008_CR44","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1007\/s41095-021-0229-5","volume":"7","author":"M Guo","year":"2021","unstructured":"Guo M, Cai J, Liu Z, Mu T, Martin RR, Hu S (2021) Pct: point cloud transformer. Comput Vis Med 7(2):187\u2013199","journal-title":"Comput Vis Med"},{"key":"4008_CR45","doi-asserted-by":"crossref","unstructured":"Chen CR, Fan Q, Panda R (2021) Crossvit: cross-attention multi-scale vision transformer for image classification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 357\u2013366","DOI":"10.1109\/ICCV48922.2021.00041"},{"key":"4008_CR46","unstructured":"Zhang H, Zu K, Lu J, Zou Y, Meng D (2021) EPSANet: An efficient pyramid squeeze attention block on convolutional neural network. arXiv:2105.14447"},{"key":"4008_CR47","doi-asserted-by":"crossref","unstructured":"Hou Q, Zhou D, Feng J (2021) Coordinate attention for efficient mobile network design. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 13713\u201313722","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"4008_CR48","doi-asserted-by":"crossref","unstructured":"He T, Zhang Z, Zhang H, Zhang Z, Xie J, Li M (2019) Bag of tricks for image classification with convolutional neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 558\u2013567","DOI":"10.1109\/CVPR.2019.00065"},{"key":"4008_CR49","unstructured":"Krizhevsky A, Hinton G et al (2009) Learning multiple layers of features from tiny images. Citeseer"},{"key":"4008_CR50","unstructured":"Vinyals O, Blundell C, Lillicrap T, Wierstra D et al (2016) Matching networks for one shot learning. Adv Neural Inform Process Syst, vol 29"},{"key":"4008_CR51","doi-asserted-by":"crossref","unstructured":"Tung F, Mori G (2019) Similarity-preserving knowledge distillation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1365\u20131374","DOI":"10.1109\/ICCV.2019.00145"},{"key":"4008_CR52","doi-asserted-by":"crossref","unstructured":"Peng B, Jin X, Liu J, Li D, Wu Y, Liu Y, Zhou S, Zhang Z (2019) Correlation congruence for knowledge distillation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5007\u20135016","DOI":"10.1109\/ICCV.2019.00511"},{"key":"4008_CR53","doi-asserted-by":"crossref","unstructured":"Ahn S, Hu SX, Damianou A, Lawrence ND, Dai Z (2019) Variational information distillation for knowledge transfer. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 9163\u20139171","DOI":"10.1109\/CVPR.2019.00938"},{"key":"4008_CR54","doi-asserted-by":"crossref","unstructured":"Park W, Kim D, Lu Y, Cho M (2019) Relational knowledge distillation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3967\u20133976","DOI":"10.1109\/CVPR.2019.00409"},{"key":"4008_CR55","doi-asserted-by":"crossref","unstructured":"Passalis N, Tefas A (2018) Learning deep representations with probabilistic knowledge transfer. In: Proceedings of the european conference on computer vision, pp 268\u2013284","DOI":"10.1007\/978-3-030-01252-6_17"},{"key":"4008_CR56","unstructured":"Kim J, Park S, Kwak N (2018) Paraphrasing complex network: Network compression via factor transfer. Adv Neural Inform Process Syst, vol 31"},{"key":"4008_CR57","unstructured":"Tian Y, Krishnan D, Isola P (2019) Contrastive representation distillation. In: Proceedings of the international conference on learning representations, pp 1\u201315"},{"key":"4008_CR58","doi-asserted-by":"crossref","unstructured":"Xu G, Liu Z, Li X, Loy CC (2020) Knowledge distillation meets self-supervision. In: Proceedings of the european conference on computer vision, Springer, pp 588\u2013604","DOI":"10.1007\/978-3-030-58545-7_34"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-04008-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-022-04008-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-04008-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,30]],"date-time":"2023-04-30T09:24:14Z","timestamp":1682846654000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-022-04008-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,8]]},"references-count":58,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2023,4]]}},"alternative-id":["4008"],"URL":"https:\/\/doi.org\/10.1007\/s10489-022-04008-y","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,8]]},"assertion":[{"value":"13 July 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 August 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}