{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T19:23:10Z","timestamp":1757618590409,"version":"3.44.0"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"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":["J. Comput. Sci. Technol."],"published-print":{"date-parts":[[2025,5]]},"DOI":"10.1007\/s11390-025-4821-5","type":"journal-article","created":{"date-parts":[[2025,7,10]],"date-time":"2025-07-10T09:33:43Z","timestamp":1752140023000},"page":"637-653","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["DSparse: A Distributed Training Method for Edge Clusters Based on Sparse Update"],"prefix":"10.1007","volume":"40","author":[{"given":"Xiao-Hui","family":"Peng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi-Xuan","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zheng-Hui","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi-Fan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,7,9]]},"reference":[{"key":"4821_CR1","first-page":"1273","volume-title":"Proc. the 20th International Conference on Artificial Intelligence and Statistics","author":"B McMahan","year":"2017","unstructured":"McMahan B, Moore E, Ramage D, Hampson S, Arcas B A Y. Communication-efficient learning of deep networks from decentralized data. In Proc. the 20th International Conference on Artificial Intelligence and Statistics, Apr. 2017, pp.1273\u20131282."},{"key":"4821_CR2","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1016\/j.neunet.2019.01.012","volume":"113","author":"G I Parisi","year":"2019","unstructured":"Parisi G I, Kemker R, Part J L, Kanan C, Wermter S. Continual lifelong learning with neural networks: A review. Neural Networks, 2019, 113: 54\u201371. DOI: https:\/\/doi.org\/10.1016\/j.neunet.2019.01.012.","journal-title":"Neural Networks"},{"key":"4821_CR3","unstructured":"Chen T, Xu B, Zhang C, Guestrin C. Training deep nets with sublinear memory cost. arXiv: 1604.06174, 2016. https:\/\/arXiv.org\/abs\/1604.06174, May 2025."},{"key":"4821_CR4","volume-title":"Proc. the 9th International Conference on Learning Representations","author":"M Kirisame","year":"2021","unstructured":"Kirisame M, Lyubomirsky S, Haan A, Brennan J, He M, Roesch J, Chen T, Tatlock Z. Dynamic tensor rematerialization. In Proc. the 9th International Conference on Learning Representations, May 2021."},{"key":"4821_CR5","volume-title":"Proc. the 7th International Conference on Learning Representations","author":"L Liu","year":"2019","unstructured":"Liu L, Deng L, Hu X, Zhu M, Li G, Ding Y, Xie Y. Dynamic sparse graph for efficient deep learning. In Proc. the 7th International Conference on Learning Representations, May 2019."},{"key":"4821_CR6","doi-asserted-by":"publisher","DOI":"10.5555\/3495724.3496671","volume-title":"Proc. the 34th International Conference on Neural Information Processing Systems","author":"H Cai","year":"2020","unstructured":"Cai H, Gan C, Zhu L, Han S. TinyTL: Reduce memory, not parameters for efficient on-device learning. In Proc. the 34th International Conference on Neural Information Processing Systems, Dec. 2020, Article No. 947. DOI: https:\/\/doi.org\/10.5555\/3495724.3496671."},{"key":"4821_CR7","doi-asserted-by":"publisher","DOI":"10.5555\/3600270.3601937","volume-title":"Proc. the 36th International Conference on Neural Information Processing Systems","author":"J Lin","year":"2022","unstructured":"Lin J, Zhu L, Chen W M, Wang W C, Gan C, Han S. On-device training under 256KB memory. In Proc. the 36th International Conference on Neural Information Processing Systems, Nov. 28\u2013Dec. 9, 2022, Article No. 1667. DOI: https:\/\/doi.org\/10.5555\/3600270.3601937."},{"key":"4821_CR8","unstructured":"Kaplun G, Gurevich A, Swisa T, David M, Shalev-Shwartz S, Malach E. Less is more: Selective layer fine-tuning with subtuning. arXiv: 2302.06354, 2023. https:\/\/arXiv.org\/abs\/2302.06354, May 2025."},{"key":"4821_CR9","unstructured":"Kwon Y D, Li R, Venieris S I, Chauhan J, Lane N D, Mascolo C. TinyTrain: Deep neural network training at the extreme edge. arXiv: 2307.09988, 2023. https:\/\/arXiv.org\/abs\/2307.09988, May 2025."},{"key":"4821_CR10","doi-asserted-by":"publisher","first-page":"1469","DOI":"10.1145\/3397271.3401156","volume-title":"Proc. the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval","author":"F Yuan","year":"2020","unstructured":"Yuan F, He X, Karatzoglou A, Zhang L. Parameter-efficient transfer from sequential behaviors for user modeling and recommendation. In Proc. the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, Jul. 2020, pp.1469\u20131478. DOI: https:\/\/doi.org\/10.1145\/3397271.3401156."},{"key":"4821_CR11","first-page":"2790","volume-title":"Proc. the 36th International Conference on Machine Learning","author":"N Houlsby","year":"2019","unstructured":"Houlsby N, Giurgiu A, Jastrzebski S, Morrone B, De Laroussilhe Q, Gesmundo A, Attariyan M, Gelly S. Parameter-efficient transfer learning for NLP. In Proc. the 36th International Conference on Machine Learning, Jun. 2019, pp.2790\u20132799."},{"issue":"3","key":"4821_CR12","doi-asserted-by":"publisher","first-page":"1489","DOI":"10.1109\/TCC.2020.2997008","volume":"10","author":"C Ding","year":"2022","unstructured":"Ding C, Zhou A, Liu Y, Chang R N, Hsu C H, Wang S. A cloud-edge collaboration framework for cognitive service. IEEE Trans. Cloud Computing, 2022, 10(3): 1489\u20131499. DOI: https:\/\/doi.org\/10.1109\/TCC.2020.2997008.","journal-title":"IEEE Trans. Cloud Computing"},{"key":"4821_CR13","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1145\/3318216.3363304","volume-title":"Proc. the 4th ACM\/IEEE Symposium on Edge Computing","author":"Y Lu","year":"2019","unstructured":"Lu Y, Shu Y, Tan X, Liu Y, Zhou M, Chen Q, Pei D. Collaborative learning between cloud and end devices: An empirical study on location prediction. In Proc. the 4th ACM\/IEEE Symposium on Edge Computing, Nov. 2019, pp.139\u2013151. DOI: https:\/\/doi.org\/10.1145\/3318216.3363304."},{"key":"4821_CR14","doi-asserted-by":"publisher","first-page":"3865","DOI":"10.1145\/3447548.3467097","volume-title":"Proc. the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining","author":"J Yao","year":"2021","unstructured":"Yao J, Wang F, Jia K, Han B, Zhou J, Yang H. Device-cloud collaborative learning for recommendation. In Proc. the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, Aug. 2021, pp.3865\u20133874. DOI: https:\/\/doi.org\/10.1145\/3447548.3467097."},{"key":"4821_CR15","doi-asserted-by":"publisher","first-page":"6080","DOI":"10.1609\/aaai.v36i6.20555","volume-title":"Proc. the 36th AAAI Conference on Artificial Intelligence","author":"S Bibikar","year":"2022","unstructured":"Bibikar S, Vikalo H, Wang Z, Chen X. Federated dynamic sparse training: Computing less, communicating less, yet learning better. In Proc. the 36th AAAI Conference on Artificial Intelligence, Feb. 22\u2013Mar. 1, 2022, pp.6080\u20136088. DOI: https:\/\/doi.org\/10.1609\/aaai.v36i6.20555."},{"key":"4821_CR16","doi-asserted-by":"publisher","DOI":"10.5555\/3540261.3542113","volume-title":"Proc. the 35th International Conference on Neural Information Processing Systems","author":"Y L Sung","year":"2021","unstructured":"Sung Y L, Nair V, Raffel C A. Training neural networks with fixed sparse masks. In Proc. the 35th International Conference on Neural Information Processing Systems, Dec. 2021, Article No. 1852. DOI: https:\/\/doi.org\/10.5555\/3540261.3542113."},{"key":"4821_CR17","doi-asserted-by":"publisher","first-page":"2543","DOI":"10.1145\/3447548.3467078","volume-title":"Proc. the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining","author":"A Banitalebi-Dehkordi","year":"2021","unstructured":"Banitalebi-Dehkordi A, Vedula N, Pei J, Xia F, Wang L, Zhang Y. Auto-split: A general framework of collaborative edge-cloud AI. In Proc. the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, Aug. 2021, pp.2543\u20132553. DOI: https:\/\/doi.org\/10.1145\/3447548.3467078."},{"key":"4821_CR18","doi-asserted-by":"publisher","first-page":"2477","DOI":"10.1145\/3340531.3412700","volume-title":"Proc. the 29th ACM International Conference on Information & Knowledge Management","author":"Y Gong","year":"2020","unstructured":"Gong Y, Jiang Z, Feng Y, Hu B, Zhao K, Liu Q, Ou W. EdgeRec: Recommender system on edge in mobile Taobao. In Proc. the 29th ACM International Conference on Information & Knowledge Management, Oct. 2020, pp.2477\u20132484. DOI: https:\/\/doi.org\/10.1145\/3340531.3412700."},{"key":"4821_CR19","doi-asserted-by":"publisher","DOI":"10.1109\/sc41405.2020.00024","volume-title":"Proc. the 2020 International Conference for High Performance Computing, Networking, Storage and Analysis","author":"S Rajbhandari","year":"2020","unstructured":"Rajbhandari S, Rasley J, Ruwase O, He Y. ZeRO: Memory optimizations toward training trillion parameter models. In Proc. the 2020 International Conference for High Performance Computing, Networking, Storage and Analysis, Nov. 2020. DOI: https:\/\/doi.org\/10.1109\/sc41405.2020.00024."},{"key":"4821_CR20","doi-asserted-by":"publisher","first-page":"634","DOI":"10.1109\/OJCOMS.2020.2994737","volume":"1","author":"D Liu","year":"2020","unstructured":"Liu D, Chen X, Zhou Z, Ling Q. HierTrain: Fast hierarchical edge AI learning with hybrid parallelism in mobile-edge-cloud computing. IEEE Open Journal of the Communications Society, 2020, 1: 634\u2013645. DOI: https:\/\/doi.org\/10.1109\/ojcoms.2020.2994737.","journal-title":"IEEE Open Journal of the Communications Society"},{"issue":"4","key":"4821_CR21","doi-asserted-by":"publisher","first-page":"3200","DOI":"10.1109\/TMC.2023.3272567","volume":"23","author":"Y Chen","year":"2024","unstructured":"Chen Y, Yang Q, He S, Shi Z, Chen J, Guizani M. FT-PipeHD: A fault-tolerant pipeline-parallel distributed training approach for heterogeneous edge devices. IEEE Trans. Mobile Computing, 2024, 23(4): 3200\u20133212. DOI: https:\/\/doi.org\/10.1109\/tmc.2023.3272567.","journal-title":"IEEE Trans. Mobile Computing"},{"issue":"5","key":"4821_CR22","doi-asserted-by":"publisher","first-page":"993","DOI":"10.1007\/s00607-020-00896-5","volume":"103","author":"G Carvalho","year":"2021","unstructured":"Carvalho G, Cabral B, Pereira V, Bernardino J. Edge computing: Current trends, research challenges and future directions. Computing, 2021, 103(5): 993\u20131023. DOI: https:\/\/doi.org\/10.1007\/s00607-020-00896-5.","journal-title":"Computing"},{"key":"4821_CR23","first-page":"1","volume-title":"Proc. the 6th International Conference on Learning Representations","author":"P Micikevicius","year":"2018","unstructured":"Micikevicius P, Narang S, Alben J, Diamos G, Elsen E, Garcia D, Ginsburg B, Houston M, Kuchaiev O, Venkatesh G, Wu H. Mixed precision training. In Proc. the 6th International Conference on Learning Representations, Apr. 30\u2013May 3, 2018, pp.1\u201312."},{"key":"4821_CR24","doi-asserted-by":"publisher","first-page":"1403","DOI":"10.1109\/icdcs47774.2020.00185","volume-title":"Proc. the 40th IEEE International Conference on Distributed Computing Systems (ICDCS)","author":"T Huang","year":"2020","unstructured":"Huang T, Tao L, Zhou J T. Adaptive precision training for resource constrained devices. In Proc. the 40th IEEE International Conference on Distributed Computing Systems (ICDCS), Nov. 29\u2013Dec.1, 2020, pp.1403\u20131408. DOI: https:\/\/doi.org\/10.1109\/icdcs47774.2020.00185."},{"key":"4821_CR25","doi-asserted-by":"publisher","first-page":"3123","DOI":"10.5555\/2969442.2969588","volume-title":"Proc. the 29th International Conference on Neural Information Processing Systems","author":"M Courbariaux","year":"2015","unstructured":"Courbariaux M, Bengio Y, David J P. BinaryConnect: Training deep neural networks with binary weights during propagations. In Proc. the 29th International Conference on Neural Information Processing Systems, Dec. 2015, pp.3123\u20133131. DOI: https:\/\/doi.org\/10.5555\/2969442.2969588."},{"key":"4821_CR26","doi-asserted-by":"publisher","first-page":"4510","DOI":"10.1109\/CVPR.2018.00474","volume-title":"Proc. the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"M Sandler","year":"2018","unstructured":"Sandler M, Howard A, Zhu M, Zhmoginov A, Chen L C. Mobilenetv2: Inverted residuals and linear bottlenecks. In Proc. the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Jun. 2018, pp.4510\u20134520. DOI: https:\/\/doi.org\/10.1109\/cvpr.2018.00474."},{"key":"4821_CR27","doi-asserted-by":"publisher","first-page":"722","DOI":"10.1109\/icvgip.2008.47","volume-title":"Proc. the 6th Indian Conference on Computer Vision, Graphics & Image Processing","author":"M E Nilsback","year":"2008","unstructured":"Nilsback M E, Zisserman A. Automated flower classification over a large number of classes. In Proc. the 6th Indian Conference on Computer Vision, Graphics & Image Processing, Dec. 2008, pp.722\u2013729. DOI: https:\/\/doi.org\/10.1109\/icvgip.2008.47."},{"key":"4821_CR28","doi-asserted-by":"publisher","DOI":"10.1109\/ijcnn52387.2021.9533927","volume-title":"Proc. the 2021 International Joint Conference on Neural Networks (IJCNN)","author":"H Ren","year":"2021","unstructured":"Ren H, Anicic D, Runkler T A. TinyOL: TinyML with online-learning on microcontrollers. In Proc. the 2021 International Joint Conference on Neural Networks (IJCNN), Jul. 2021. DOI: https:\/\/doi.org\/10.1109\/ijcnn52387.2021.9533927."},{"key":"4821_CR29","doi-asserted-by":"publisher","first-page":"1223","DOI":"10.5555\/2999134.2999271","volume-title":"Proc. the 26th International Conference on Neural Information Processing Systems","author":"J Dean","year":"2012","unstructured":"Dean J, Corrado G S, Monga R, Chen K, Devin M, Le Q V, Mao M Z, Ranzato M, Senior A, Tucker P, Yang K, Ng A Y. Large scale distributed deep networks. In Proc. the 26th International Conference on Neural Information Processing Systems, Dec. 2012, pp.1223\u20131231. DOI: https:\/\/doi.org\/10.5555\/2999134.2999271."},{"key":"4821_CR30","doi-asserted-by":"publisher","first-page":"19","DOI":"10.5555\/2968826.2968829","volume-title":"Proc. the 28th International Conference on Neural Information Processing Systems","author":"M Li","year":"2014","unstructured":"Li M, Andersen D G, Smola A, Yu K. Communication efficient distributed machine learning with the parameter server. In Proc. the 28th International Conference on Neural Information Processing Systems, Dec. 2014, pp.19\u201327. DOI: https:\/\/doi.org\/10.5555\/2968826.2968829."},{"key":"4821_CR31","doi-asserted-by":"publisher","first-page":"1223","DOI":"10.5555\/2999611.2999748","volume-title":"Proc. the 27th International Conference on Neural Information Processing Systems","author":"Q Ho","year":"2013","unstructured":"Ho Q, Cipar J, Cui H, Lee S, Kim J K, Lee S, Gibbons P B, Gibson G A, Ganger G P, Xing E P. More effective distributed ML via a stale synchronous parallel parameter server. In Proc. the 27th International Conference on Neural Information Processing Systems, Dec. 2013, pp.1223\u20131231. DOI: https:\/\/doi.org\/10.5555\/2999611.2999748."},{"key":"4821_CR32","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1145\/3341301.3359642","volume-title":"Proc. the 27th ACM Symposium on Operating Systems Principles","author":"Y Peng","year":"2019","unstructured":"Peng Y, Zhu Y, Chen Y, Bao Y, Yi B, Lan C, Wu C, Guo C. A generic communication scheduler for distributed DNN training acceleration. In Proc. the 27th ACM Symposium on Operating Systems Principles, Oct. 2019, pp.16\u201329. DOI: https:\/\/doi.org\/10.1145\/3341301.3359642."},{"issue":"12","key":"4821_CR33","doi-asserted-by":"publisher","first-page":"3005","DOI":"10.14778\/3415478.3415530","volume":"13","author":"S Li","year":"2020","unstructured":"Li S, Zhao Y, Varma R, Salpekar O, Noordhuis P, Li T, Paszke A, Smith J, Vaughan B, Damania P, Chintala S. PyTorch distributed: Experiences on accelerating data parallel training. Proceedings of the VLDB Endowment, 2020, 13(12): 3005\u20133018. DOI: https:\/\/doi.org\/10.14778\/3415478.3415530.","journal-title":"Proceedings of the VLDB Endowment"},{"key":"4821_CR34","unstructured":"Sergeev A, Del Balso M. Horovod: Fast and easy distributed deep learning in TensorFlow. arXiv: 1802.05799, 2018. https:\/\/arXiv.org\/abs\/1802.05799, May 2025."},{"key":"4821_CR35","doi-asserted-by":"publisher","first-page":"312","DOI":"10.1145\/3636534.3649363","volume-title":"Proc. the 30th Annual International Conference on Mobile Computing and Networking","author":"S Ye","year":"2024","unstructured":"Ye S, Zeng L, Chu X, Xing G, Chen X. Asteroid: Resource-efficient hybrid pipeline parallelism for collaborative DNN training on heterogeneous edge devices. In Proc. the 30th Annual International Conference on Mobile Computing and Networking, Nov. 2024, pp.312\u2013326. DOI: https:\/\/doi.org\/10.1145\/3636534.3649363."},{"key":"4821_CR36","first-page":"779","volume-title":"Proc. the 5th Conference on Machine Learning and Systems","author":"R Guo","year":"2022","unstructured":"Guo R, Guo V, Kim A, Hildred J, Daudjee K. Hydrozoa: Dynamic hybrid-parallel DNN training on serverless containers. In Proc. the 5th Conference on Machine Learning and Systems, Aug. 29\u2013Sept. 1, 2022, pp.779\u2013794."},{"key":"4821_CR37","doi-asserted-by":"publisher","DOI":"10.1145\/3498361.3538663","volume-title":"Proc. the 20th Annual International Conference on Mobile Systems, Applications and Services","author":"H Kim","year":"2022","unstructured":"Kim H, Ko J. Memory-efficient DNN training on mobile devices. In Proc. the 20th Annual International Conference on Mobile Systems, Applications and Services, Jun. 27\u2013Jul. 1, 2022, pp.619. DOI: https:\/\/doi.org\/10.1145\/3498361.3538663."},{"key":"4821_CR38","unstructured":"Zhou H, Lan T, Venkataramani G, Ding W. On the convergence of heterogeneous federated learning with arbitrary adaptive online model pruning. arXiv: 2201.11803, 2022. https:\/\/arXiv.org\/abs\/2201.11803, May 2025."},{"key":"4821_CR39","series-title":"Technical Report TR-2009","volume-title":"Learning multiple layers of features from tiny images","author":"A Krizhevsky","year":"2009","unstructured":"Krizhevsky A. Learning multiple layers of features from tiny images. Technical Report TR-2009, University of Toronto, 2009. https:\/\/www.cs.toronto.edu\/~kriz\/learning-features-2009-TR.pdf, May 2025."},{"key":"4821_CR40","doi-asserted-by":"publisher","first-page":"770","DOI":"10.1109\/CVPR.2016.90","volume-title":"Proc. the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","author":"K He","year":"2016","unstructured":"He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In Proc. the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 2016, pp.770\u2013778. DOI: https:\/\/doi.org\/10.1109\/cvpr.2016.90."},{"key":"4821_CR41","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-00889-5_1","volume-title":"Proc. the 4th International Workshop on Deep Learning in Medical Image Analysis and the 8th International Workshop on Multimodal Learning for Clinical Decision Support","author":"Z Zhou","year":"2018","unstructured":"Zhou Z, Rahman Siddiquee M M, Tajbakhsh N, Liang J. UNet++: A nested U-Net architecture for medical image segmentation. In Proc. the 4th International Workshop on Deep Learning in Medical Image Analysis and the 8th International Workshop on Multimodal Learning for Clinical Decision Support, Sept. 2018, pp.3\u201311. DOI: https:\/\/doi.org\/10.1007\/978-3-030-00889-5_1."},{"issue":"1","key":"4821_CR42","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1016\/j.cviu.2005.09.012","volume":"106","author":"L Fei-Fei","year":"2007","unstructured":"Fei-Fei L, Fergus R, Perona P. Learning generative visual models from few training examples: An incremental Bayesian approach tested on 101 object categories. Computer Vision and Image Understanding, 2007, 106(1): 59\u201370. DOI: https:\/\/doi.org\/10.1016\/j.cviu.2005.09.012.","journal-title":"Computer Vision and Image Understanding"}],"container-title":["Journal of Computer Science and Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11390-025-4821-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11390-025-4821-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11390-025-4821-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,7]],"date-time":"2025-09-07T02:18:11Z","timestamp":1757211491000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11390-025-4821-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5]]},"references-count":42,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,5]]}},"alternative-id":["4821"],"URL":"https:\/\/doi.org\/10.1007\/s11390-025-4821-5","relation":{},"ISSN":["1000-9000","1860-4749"],"issn-type":[{"type":"print","value":"1000-9000"},{"type":"electronic","value":"1860-4749"}],"subject":[],"published":{"date-parts":[[2025,5]]},"assertion":[{"value":"12 September 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 March 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 July 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Conflict of Interest The authors declare that they have no conflict of interest.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics"}}]}}