{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T05:13:39Z","timestamp":1778044419009,"version":"3.51.4"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T00:00:00Z","timestamp":1763424000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T00:00:00Z","timestamp":1763424000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100010418","name":"Institute for Information and Communications Technology Promotion","doi-asserted-by":"publisher","award":["IITP-2025-RS-2020-II201821"],"award-info":[{"award-number":["IITP-2025-RS-2020-II201821"]}],"id":[{"id":"10.13039\/501100010418","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010418","name":"Institute for Information and Communications Technology Promotion","doi-asserted-by":"publisher","award":["IITP-2025-RS-2020-II201821"],"award-info":[{"award-number":["IITP-2025-RS-2020-II201821"]}],"id":[{"id":"10.13039\/501100010418","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010418","name":"Institute for Information and Communications Technology Promotion","doi-asserted-by":"publisher","award":["IITP-2025-RS-2020-II201821"],"award-info":[{"award-number":["IITP-2025-RS-2020-II201821"]}],"id":[{"id":"10.13039\/501100010418","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010418","name":"Institute for Information and Communications Technology Promotion","doi-asserted-by":"publisher","award":["IITP-2025-RS-2020-II201821"],"award-info":[{"award-number":["IITP-2025-RS-2020-II201821"]}],"id":[{"id":"10.13039\/501100010418","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["RS-2024-00343255"],"award-info":[{"award-number":["RS-2024-00343255"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["RS-2024-00343255"],"award-info":[{"award-number":["RS-2024-00343255"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["RS-2024-00343255"],"award-info":[{"award-number":["RS-2024-00343255"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["RS-2024-00343255"],"award-info":[{"award-number":["RS-2024-00343255"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Netw Syst Manage"],"published-print":{"date-parts":[[2026,1]]},"DOI":"10.1007\/s10922-025-10006-5","type":"journal-article","created":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T09:04:20Z","timestamp":1763456660000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep Resource Localization with Traffic Prediction for Adaptive Multi-access Edge Computing Management"],"prefix":"10.1007","volume":"34","author":[{"given":"Nayeon","family":"Jang","sequence":"first","affiliation":[]},{"given":"Huigyu","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Gyurin","family":"Byun","sequence":"additional","affiliation":[]},{"given":"Jeongjun","family":"Park","sequence":"additional","affiliation":[]},{"given":"Moonseong","family":"Kim","sequence":"additional","affiliation":[]},{"given":"Min Young","family":"Chung","sequence":"additional","affiliation":[]},{"given":"Hyunseung","family":"Choo","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,11,18]]},"reference":[{"key":"10006_CR1","doi-asserted-by":"publisher","first-page":"2952","DOI":"10.1109\/OJCOMS.2023.3320646","volume":"4","author":"Y-C Wang","year":"2023","unstructured":"Wang, Y.-C., Xue, J., Wei, C., Kuo, C.-C.J.: An overview on generative ai at scale with edge-cloud computing. IEEE Open J. Commun. Soc. 4, 2952\u20132971 (2023)","journal-title":"IEEE Open J. Commun. Soc."},{"key":"10006_CR2","unstructured":"Radford, A., Narasimhan, K., Salimans, T., Sutskever, I.: Improving language understanding by generative pre-training. OpenAI (2018)"},{"key":"10006_CR3","doi-asserted-by":"publisher","first-page":"637","DOI":"10.1109\/JIOT.2016.2579198","volume":"3","author":"W Shi","year":"2016","unstructured":"Shi, W., Cao, J., Zhang, Q., Li, Y., Xu, L.: Edge computing: vision and challenges. IEEE Internet Things J. 3, 637\u2013646 (2016)","journal-title":"IEEE Internet Things J."},{"issue":"5","key":"10006_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3326066","volume":"52","author":"C-H Hong","year":"2019","unstructured":"Hong, C.-H., Varghese, B.: Resource management in fog\/edge computing: a survey on architectures, infrastructure, and algorithms. ACM Comput. Surv. (CSUR) 52(5), 1\u201337 (2019)","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"10006_CR5","doi-asserted-by":"publisher","first-page":"7257","DOI":"10.1109\/TWC.2021.3081991","volume":"20","author":"Z Lin","year":"2021","unstructured":"Lin, Z., Bi, S., Zhang, Y.-J.A.: Optimizing ai service placement and resource allocation in mobile edge intelligence systems. IEEE Trans. Wirel. Commun. 20, 7257\u20137271 (2021)","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"10006_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2023.102079","volume":"103","author":"S Liu","year":"2024","unstructured":"Liu, S., He, M., Wu, Z., Lu, P., Gu, W.: Spatial-temporal graph neural network traffic prediction based load balancing with reinforcement learning in cellular networks. Inform. Fusion 103, 102079 (2024)","journal-title":"Inform. Fusion"},{"key":"10006_CR7","doi-asserted-by":"publisher","first-page":"86181","DOI":"10.1109\/ACCESS.2022.3199372","volume":"10","author":"X Wan","year":"2022","unstructured":"Wan, X., Liu, H., Xu, H., Zhang, X.: Network traffic prediction based on lstm and transfer learning. IEEE Access 10, 86181\u201386190 (2022)","journal-title":"IEEE Access"},{"key":"10006_CR8","doi-asserted-by":"publisher","first-page":"6999","DOI":"10.1109\/TNNLS.2021.3084827","volume":"33","author":"Z Li","year":"2021","unstructured":"Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE Trans. Neural Netw. Learn. Syst. 33, 6999\u20137019 (2021)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10006_CR9","first-page":"5668","volume":"33","author":"H Yao","year":"2019","unstructured":"Yao, H., Tang, X., Wei, H., Zheng, G., Li, Z.: Revisiting spatial-temporal similarity: a deep learning framework for traffic prediction. Proc. AAAI Conf. Artif. Intell. 33, 5668\u20135675 (2019)","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"10006_CR10","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1016\/j.comcom.2021.01.021","volume":"170","author":"M Abbasi","year":"2021","unstructured":"Abbasi, M., Shahraki, A., Taherkordi, A.: Deep learning for network traffic monitoring and analysis (ntma): a survey. Comput. Commun. 170, 19\u201341 (2021)","journal-title":"Comput. Commun."},{"key":"10006_CR11","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1007\/s10922-022-09693-1","volume":"31","author":"A Thantharate","year":"2023","unstructured":"Thantharate, A., Beard, C.: Adaptive6g: adaptive resource management for network slicing architectures in current 5g and future 6g systems. J. Netw. Syst. Manag. 31, 9 (2023)","journal-title":"J. Netw. Syst. Manag."},{"key":"10006_CR12","doi-asserted-by":"publisher","first-page":"74429","DOI":"10.1109\/ACCESS.2018.2881964","volume":"6","author":"R Li","year":"2018","unstructured":"Li, R., Zhao, Z., Sun, Q., I, C.-L., Yang, C., Chen, X., Zhao, M., Zhang, H.: Deep reinforcement learning for resource management in network slicing. IEEE Access 6, 74429\u201374441 (2018)","journal-title":"IEEE Access"},{"key":"10006_CR13","doi-asserted-by":"publisher","first-page":"8577","DOI":"10.1109\/JIOT.2019.2921159","volume":"6","author":"C Qiu","year":"2019","unstructured":"Qiu, C., Hu, Y., Chen, Y., Zeng, B.: Deep deterministic policy gradient (ddpg)-based energy harvesting wireless communications. IEEE Internet Things J. 6, 8577\u20138588 (2019)","journal-title":"IEEE Internet Things J."},{"key":"10006_CR14","doi-asserted-by":"crossref","unstructured":"Zeng, J., Hu, J., Zhang, Y.: Adaptive traffic signal control with deep recurrent q-learning. In: 2018 IEEE intelligent vehicles symposium (IV), pp. 1215\u20131220. IEEE, Changshu, China (2018)","DOI":"10.1109\/IVS.2018.8500414"},{"key":"10006_CR15","doi-asserted-by":"publisher","first-page":"935","DOI":"10.1109\/TNET.2021.3053771","volume":"29","author":"Q Wu","year":"2021","unstructured":"Wu, Q., Chen, X., Zhou, Z., Chen, L., Zhang, J.: Deep reinforcement learning with spatio-temporal traffic forecasting for data-driven base station sleep control. IEEE\/ACM Trans. Netw. 29, 935\u2013948 (2021)","journal-title":"IEEE\/ACM Trans. Netw."},{"key":"10006_CR16","doi-asserted-by":"crossref","unstructured":"Lea, C., Flynn, M.D., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks for action segmentation and detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 156\u2013165 (2017)","DOI":"10.1109\/CVPR.2017.113"},{"key":"10006_CR17","doi-asserted-by":"crossref","unstructured":"Park, J., Mwasinga, L.J., Yang, H., Raza, S.M., Le, D.-T., Kim, M., Chung, M.Y., Choo, H.: Regional correlation aided mobile traffic prediction with spatiotemporal deep learning. In: 2024 IEEE 21st Consumer Communications & Networking Conference (CCNC), pp. 566\u2013569. IEEE (2024)","DOI":"10.1109\/CCNC51664.2024.10454764"},{"key":"10006_CR18","unstructured":"Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017)"},{"issue":"6","key":"10006_CR19","doi-asserted-by":"publisher","first-page":"4927","DOI":"10.1109\/TITS.2021.3054840","volume":"23","author":"X Yin","year":"2022","unstructured":"Yin, X., Wu, G., Wei, J., Shen, Y., Qi, H., Yin, B.: Deep learning on traffic prediction: methods, analysis, and future directions. IEEE Trans. Intell. Transp. Syst. 23(6), 4927\u20134943 (2022)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10006_CR20","doi-asserted-by":"crossref","unstructured":"Shao, H., Soong, B.-H.: Traffic flow prediction with long short-term memory networks (lstms). In: 2016 IEEE Region 10 Conference (TENCON), pp. 2986\u20132989. IEEE, Singapore (2016)","DOI":"10.1109\/TENCON.2016.7848593"},{"key":"10006_CR21","doi-asserted-by":"publisher","first-page":"2523","DOI":"10.3390\/ijerph16142523","volume":"16","author":"R Tanzer","year":"2019","unstructured":"Tanzer, R., Malings, C., Hauryliuk, A., Subramanian, R., Presto, A.A.: Demonstration of a low-cost multi-pollutant network to quantify intra-urban spatial variations in air pollutant source impacts and to evaluate environmental justice. Int. J. Env. Res. Public Health 16, 2523 (2019)","journal-title":"Int. J. Env. Res. Public Health"},{"key":"10006_CR22","doi-asserted-by":"publisher","first-page":"6910","DOI":"10.1109\/TITS.2020.2997352","volume":"22","author":"H Zheng","year":"2021","unstructured":"Zheng, H., Lin, F., Feng, X., Chen, Y.: A hybrid deep learning model with attention-based conv-lstm networks for short-term traffic flow prediction. IEEE Trans. Intell. Transport. Syst. 22, 6910\u20136920 (2021)","journal-title":"IEEE Trans. Intell. Transport. Syst."},{"key":"10006_CR23","doi-asserted-by":"publisher","first-page":"39651","DOI":"10.1109\/ACCESS.2023.3268437","volume":"11","author":"D Wang","year":"2023","unstructured":"Wang, D., Bao, Y.-Y., Wang, C.-M.: A hybrid deep learning method based on Ceemdan and attention mechanism for network traffic prediction. IEEE Access 11, 39651\u201339663 (2023)","journal-title":"IEEE Access"},{"key":"10006_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2020.106290","volume":"141","author":"S Manochandar","year":"2020","unstructured":"Manochandar, S., Punniyamoorthy, M., Jeyachitra, R.K.: Development of new seed with modified validity measures for k-means clustering. Comput. G Ind. Eng. 141, 106290 (2020)","journal-title":"Comput. G Ind. Eng."},{"key":"10006_CR25","doi-asserted-by":"publisher","first-page":"969","DOI":"10.1080\/13658816.2019.1697879","volume":"34","author":"Y Zhang","year":"2019","unstructured":"Zhang, Y., Cheng, T., Ren, Y., Xie, K.: A novel residual graph convolution deep learning model for short-term network-based traffic forecasting. Int. J. Geogr. Inf. Sci. 34, 969\u2013995 (2019)","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"10006_CR26","doi-asserted-by":"crossref","unstructured":"Yang, S., Yu, X., Zhou, Y.: Lstm and gru neural network performance comparison study: taking yelp review dataset as an example. In: 2020 International Workshop on Electronic Communication and Artificial Intelligence (IWECAI), pp. 98\u2013101 (2020)","DOI":"10.1109\/IWECAI50956.2020.00027"},{"key":"10006_CR27","doi-asserted-by":"publisher","first-page":"8076","DOI":"10.1109\/TIT.2022.3192506","volume":"68","author":"A Ghosh","year":"2020","unstructured":"Ghosh, A., Chung, J., Yin, D., Ramchandran, K.: An efficient framework for clustered federated learning. IEEE Trans. Inform. Theory 68, 8076\u20138091 (2020)","journal-title":"IEEE Trans. Inform. Theory"},{"key":"10006_CR28","doi-asserted-by":"publisher","first-page":"1015","DOI":"10.1109\/JSAC.2020.3018809","volume":"39","author":"B Liu","year":"2021","unstructured":"Liu, B., Liu, C., Peng, M.: Resource allocation for energy-efficient mec in noma-enabled massive iot networks. IEEE J. Select. Areas Commun. 39, 1015\u20131027 (2021)","journal-title":"IEEE J. Select. Areas Commun."},{"key":"10006_CR29","doi-asserted-by":"crossref","unstructured":"Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785\u2013794 (2016)","DOI":"10.1145\/2939672.2939785"},{"key":"10006_CR30","unstructured":"Agarwal, R., Schuurmans, D., Norouzi, M.: An optimistic perspective on offline reinforcement learning. In: Proceedings of the International Conference on Machine Learning (ICML), pp. 104\u2013114 (2020)"},{"issue":"4","key":"10006_CR31","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1007\/s10922-022-09672-6","volume":"30","author":"F Poltronieri","year":"2022","unstructured":"Poltronieri, F., Stefanelli, C., Suri, N., Tortonesi, M.: Value is king: the mecforge deep reinforcement learning solution for resource management in 5g and beyond. J. Netw. Syst. Manag. 30(4), 63 (2022)","journal-title":"J. Netw. Syst. Manag."},{"issue":"2","key":"10006_CR32","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1007\/s10922-024-09815-x","volume":"32","author":"N Harris Jr","year":"2024","unstructured":"Harris, N., Jr., Khorsandroo, S.: Ddt: A reinforcement learning approach to dynamic flow timeout assignment in software defined networks. J. Netw. Syst. Manag. 32(2), 35 (2024)","journal-title":"J. Netw. Syst. Manag."},{"key":"10006_CR33","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1109\/JAS.2016.7508798","volume":"3","author":"L Li","year":"2016","unstructured":"Li, L., Lv, Y., Wang, F.-Y.: Traffic signal timing via deep reinforcement learning. IEEE\/CAA J. Autom. Sinica 3, 247\u2013254 (2016)","journal-title":"IEEE\/CAA J. Autom. Sinica"},{"key":"10006_CR34","doi-asserted-by":"publisher","first-page":"847","DOI":"10.1016\/j.future.2019.07.019","volume":"102","author":"H Lu","year":"2020","unstructured":"Lu, H., Gu, C., Luo, F., Ding, W., Liu, X.: Optimization of lightweight task offloading strategy for mobile edge computing based on deep reinforcement learning. Future Gener. Comput. Syst. 102, 847\u2013861 (2020)","journal-title":"Future Gener. Comput. Syst."},{"key":"10006_CR35","first-page":"271","volume-title":"Communications in Computer and Information Science","author":"Y Lu","year":"2022","unstructured":"Lu, Y., Luo, M.-X., Wang, X.: Large-scale mobile edge computing with joint offloading decision and resource allocation. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds.) Communications in Computer and Information Science, pp. 271\u2013286. Springer, Cham (2022)"},{"key":"10006_CR36","doi-asserted-by":"crossref","unstructured":"Lin, Z., Lin, Y., Yang, J., Zhang, Q.: Energy-efficient joint resource allocation and computation offloading in noma-enabled vehicular fog computing. Mob. Netw. Appl. (2024)","DOI":"10.1007\/s11036-023-02265-w"},{"issue":"1","key":"10006_CR37","doi-asserted-by":"publisher","first-page":"590","DOI":"10.1109\/TNSM.2023.3292272","volume":"21","author":"J Chang","year":"2023","unstructured":"Chang, J., Wang, J., Li, B., Zhao, Y., Li, D.: Attention-based deep reinforcement learning for edge user allocation. IEEE Trans. Netw. Serv. Manag. 21(1), 590\u2013604 (2023)","journal-title":"IEEE Trans. Netw. Serv. Manag."},{"key":"10006_CR38","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1109\/MSP.2017.2743240","volume":"34","author":"K Arulkumaran","year":"2017","unstructured":"Arulkumaran, K., Deisenroth, M.P., Brundage, M., Bharath, A.A.: Deep reinforcement learning: a brief survey. IEEE Signal Process. Mag. 34, 26\u201338 (2017)","journal-title":"IEEE Signal Process. Mag."},{"key":"10006_CR39","unstructured":"Schulman, J., Moritz, P., Levine, S., Jordan, M., Abbeel, P.: High-dimensional continuous control using generalized advantage estimation. arXiv preprint arXiv:1506.02438 (2015)"},{"key":"10006_CR40","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/sdata.2015.55","volume":"2","author":"G Barlacchi","year":"2015","unstructured":"Barlacchi, G., De Nadai, M., Larcher, R., Casella, A., Chitic, C., Torrisi, G., Antonelli, F., Vespignani, A., Pentland, A., Lepri, B.: A multi-source dataset of urban life in the city of Milan and the province of Trentino. Sci. Data 2, 1\u201315 (2015)","journal-title":"Sci. Data"},{"issue":"1","key":"10006_CR41","first-page":"1","volume":"19","author":"X Zhang","year":"2022","unstructured":"Zhang, X., Zhang, J., Peng, C., Wang, X.: Multimodal optimization of edge server placement considering system response time. ACM Trans. Sens. Netw. 19(1), 1\u201320 (2022)","journal-title":"ACM Trans. Sens. Netw."},{"key":"10006_CR42","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpdc.2023.104745","volume":"182","author":"LJ Mwasinga","year":"2023","unstructured":"Mwasinga, L.J., Le, D.-T., Raza, S.M., Challa, R., Kim, M., Choo, H.: Rasm: Resource-aware service migration in edge computing based on deep reinforcement learning. J. Parallel Distrib. Comput. 182, 104745 (2023)","journal-title":"J. Parallel Distrib. Comput."},{"key":"10006_CR43","unstructured":"Amazon Web Services: AWS Fargate Pricing. Retrieved December 26, 2024, from https:\/\/aws.amazon.com\/ko\/fargate\/pricing\/ (2024)"}],"container-title":["Journal of Network and Systems Management"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10922-025-10006-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10922-025-10006-5","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10922-025-10006-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T04:39:51Z","timestamp":1778042391000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10922-025-10006-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,18]]},"references-count":43,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,1]]}},"alternative-id":["10006"],"URL":"https:\/\/doi.org\/10.1007\/s10922-025-10006-5","relation":{},"ISSN":["1064-7570","1573-7705"],"issn-type":[{"value":"1064-7570","type":"print"},{"value":"1573-7705","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,18]]},"assertion":[{"value":"22 March 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 June 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 October 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 November 2025","order":4,"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 no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"29"}}