{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T15:25:07Z","timestamp":1786980307316,"version":"build-2736575974"},"reference-count":60,"publisher":"Springer Science and Business Media LLC","issue":"23","license":[{"start":{"date-parts":[[2024,12,11]],"date-time":"2024-12-11T00:00:00Z","timestamp":1733875200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,11]],"date-time":"2024-12-11T00:00:00Z","timestamp":1733875200000},"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":["Neural Comput & Applic"],"published-print":{"date-parts":[[2025,8]]},"DOI":"10.1007\/s00521-024-10588-2","type":"journal-article","created":{"date-parts":[[2024,12,11]],"date-time":"2024-12-11T16:56:33Z","timestamp":1733936193000},"page":"18811-18821","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Increasing energy efficiency of bitcoin infrastructure with reinforcement learning and one-shot path planning for the lightning network"],"prefix":"10.1007","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8058-7539","authenticated-orcid":false,"given":"Danila","family":"Valko","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"Kudenko","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,12,11]]},"reference":[{"key":"10588_CR1","doi-asserted-by":"publisher","first-page":"48091","DOI":"10.1109\/ACCESS.2021.3068636","volume":"9","author":"L Badea","year":"2021","unstructured":"Badea L, Mungiu-Pupazan MC (2021) The economic and environmental impact of bitcoin. IEEE Access 9:48091\u201348104. https:\/\/doi.org\/10.1109\/ACCESS.2021.3068636","journal-title":"IEEE Access"},{"key":"10588_CR2","doi-asserted-by":"publisher","first-page":"931","DOI":"10.1038\/s41558-018-0321-8","volume":"8","author":"C Mora","year":"2018","unstructured":"Mora C, Rollins RL, Taladay K, Kantar MB, Chock MK, Shimada M, Franklin EC (2018) Bitcoin emissions alone could push global warming above 2 \u00b0C. Nat Clim Change 8:931\u2013933. https:\/\/doi.org\/10.1038\/s41558-018-0321-8","journal-title":"Nat Clim Change"},{"key":"10588_CR3","doi-asserted-by":"publisher","first-page":"653","DOI":"10.1038\/s41558-019-0535-4","volume":"9","author":"E Masanet","year":"2019","unstructured":"Masanet E, Shehabi A, Lei N, Vranken H, Koomey J, Malmodin J (2019) Implausible projections overestimate near-term bitcoin co2 emissions. Nat Clim Chang 9:653\u2013654. https:\/\/doi.org\/10.1038\/s41558-019-0535-4","journal-title":"Nat Clim Chang"},{"issue":"7","key":"10588_CR4","doi-asserted-by":"publisher","first-page":"1647","DOI":"10.1016\/j.joule.2019.05.012","volume":"3","author":"C Stoll","year":"2019","unstructured":"Stoll C, Klaa\u00dfen L, Gallersd\u00f6rfer U (2019) The carbon footprint of bitcoin. Joule 3(7):1647\u20131661","journal-title":"Joule"},{"issue":"1","key":"10588_CR5","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1016\/j.dcan.2022.06.017","volume":"9","author":"V Kohli","year":"2023","unstructured":"Kohli V, Chakravarty S, Chamola V, Sangwan KS, Zeadally S (2023) An analysis of energy consumption and carbon footprints of cryptocurrencies and possible solutions. Digit Commun Netw 9(1):79\u201389. https:\/\/doi.org\/10.1016\/j.dcan.2022.06.017","journal-title":"Digit Commun Netw"},{"issue":"9","key":"10588_CR6","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1109\/MC.2017.3571056","volume":"50","author":"M Bedford Taylor","year":"2017","unstructured":"Bedford Taylor M (2017) The evolution of bitcoin hardware. Computer 50(9):58\u201366. https:\/\/doi.org\/10.1109\/MC.2017.3571056","journal-title":"Computer"},{"key":"10588_CR7","doi-asserted-by":"publisher","unstructured":"Wang Y-Z, Wu J, Chen S-H, Chao MC-T, Yang C-H (2019) Micro-architecture optimization for low-power bitcoin mining asics. In: 2019 international symposium on vlsi design, automation and test (VLSI-DAT), pp 1\u20134. https:\/\/doi.org\/10.1109\/VLSI-DAT.2019.8741726","DOI":"10.1109\/VLSI-DAT.2019.8741726"},{"key":"10588_CR8","doi-asserted-by":"publisher","unstructured":"Zamani M, Movahedi M, Raykova M (2018) Rapidchain: scaling blockchain via full sharding. In: Proceedings of the 2018 ACM sigsac conference on computer and communications security. CCS \u201918, pp 931\u2013948. Association for Computing Machinery, New York, NY, USA. https:\/\/doi.org\/10.1145\/3243734.3243853","DOI":"10.1145\/3243734.3243853"},{"key":"10588_CR9","doi-asserted-by":"crossref","unstructured":"Zhang Y-H, Liu XF (2021) Traffic redundancy in blockchain systems: the impact of logical and physical network structures. In: 2021 IEEE international symposium on circuits and systems (ISCAS), pp 1\u20135. IEEE","DOI":"10.1109\/ISCAS51556.2021.9401386"},{"key":"10588_CR10","unstructured":"Poon J, Dryja T (2016) The bitcoin lightning network: scalable off-chain instant payments. Retrieved Feb 22, 2023 from https:\/\/lightning.network\/lightning-network-paper.pdf"},{"key":"10588_CR11","unstructured":"Lightning network in-progress specifications. GitHub. Retrieved Feb 22, 2023 from https:\/\/github.com\/lightning\/bolts"},{"key":"10588_CR12","unstructured":"Real-time lightning network statistics. JSON data. Retrieved Feb 22, 2023 from https:\/\/1ml.com\/statistics"},{"key":"10588_CR13","unstructured":"Barratt O, Scott D(2021) Comparing bitcoin & lightning energy usage to the real world. Retrieved Feb 22, 2023 from https:\/\/blog.coincorner.com\/comparing-bitcoin-lightning-energy-usage-to-the-real-world-2d64c62b1783"},{"key":"10588_CR14","unstructured":"Prihodko P, Zhigulin SN, Sahno M, Ostrovskiy AB, Osuntokun O (2016) Flare : an approach to routing in lightning network white paper. Retrieved Feb 22, 2023 from https:\/\/bitfury.com\/content\/downloads\/whitepaper_flare_an_approach_to_routing_in_lightning_network_7_7_2016.pdf"},{"key":"10588_CR15","unstructured":"Finding routes in the lightning network. Builder\u2019s guide. Retrieved Feb 22, 2023 from https:\/\/docs.lightning.engineering\/the-lightning-network\/pathfinding\/finding-routes-in-the-lightning-network"},{"key":"10588_CR16","unstructured":"Pacia C (2015) Lightning network skepticism. Retrieved Feb22, 2023 from https:\/\/chrispacia.wordpress.com\/2015\/12\/23\/lightning-network-skepticism"},{"key":"10588_CR17","unstructured":"Spoke-hub distribution paradigm. Wikipedia. Retrieved Feb 22, 2023 from https:\/\/en.wikipedia.org\/wiki\/Spoke%E2%80%93hub_distribution_paradigm"},{"key":"10588_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2022.112620","volume":"164","author":"J-H Lin","year":"2022","unstructured":"Lin J-H, Marchese E, Tessone CJ, Squartini T (2022) The weighted bitcoin lightning network. Chaos, Solitons Fractals 164:112620. https:\/\/doi.org\/10.1016\/j.chaos.2022.112620","journal-title":"Chaos, Solitons Fractals"},{"key":"10588_CR19","unstructured":"The lightning network daemon. GitHub. Retrieved Feb 22, 2023 from https:\/\/github.com\/lightningnetwork\/lnd"},{"key":"10588_CR20","unstructured":"Core Lightning (CLN): a specification compliant lightning network implementation in C. GitHub. Retrieved Feb 22, 2023 from https:\/\/github.com\/ElementsProject\/lightning"},{"key":"10588_CR21","unstructured":"Eclair (French for lightning) is a scala implementation of the lightning network. GitHub. Retrieved Feb 22 2023 from https:\/\/github.com\/ACINQ\/eclair"},{"key":"10588_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.pmcj.2022.101584","volume":"83","author":"P Zabka","year":"2022","unstructured":"Zabka P, Foerster K-T, Schmid S, Decker C (2022) Empirical evaluation of nodes and channels of the lightning network. Pervasive Mob Comput 83:101584. https:\/\/doi.org\/10.1016\/j.pmcj.2022.101584","journal-title":"Pervasive Mob Comput"},{"key":"10588_CR23","doi-asserted-by":"publisher","unstructured":"Seres IA, Guly\u00e1s L, Nagy DA, Burcsi P (2019) Topological analysis of bitcoin\u2019s lightning network. arXiv. https:\/\/doi.org\/10.48550\/ARXIV.1901.04972","DOI":"10.48550\/ARXIV.1901.04972"},{"key":"10588_CR24","doi-asserted-by":"publisher","unstructured":"Bere, F, Seres IA, Benczur AA (2019) A cryptoeconomic traffic analysis of bitcoin\u2019s lightning network. arXiv. https:\/\/doi.org\/10.48550\/ARXIV.1911.09432","DOI":"10.48550\/ARXIV.1911.09432"},{"key":"10588_CR25","unstructured":"Shell B (2022) How many transactions can the lightning network handle? Retrieved Feb 22, 2023 from https:\/\/voltage.cloud\/blog\/bitcoin-education\/how-many-transactions-can-the-lightning-network-handle"},{"key":"10588_CR26","unstructured":"Helseth A (2021) The state of lightning. Arcane Research Report. Retrieved Feb 22, 2023 from https:\/\/arcane.no\/research\/the-growth-of-the-lightning-network"},{"key":"10588_CR27","volume-title":"Reinforcement learning: an introduction","author":"RS Sutton","year":"2018","unstructured":"Sutton RS, Barto AG (2018) Reinforcement learning: an introduction, 2nd edn. The MIT Press, Cambridge","edition":"2"},{"key":"10588_CR28","doi-asserted-by":"publisher","unstructured":"Schulman J, Wolski F, Dhariwal P, Radford A, Klimov O (2017). Proximal policy optimization algorithms arXiv. https:\/\/doi.org\/10.48550\/ARXIV.1707.06347","DOI":"10.48550\/ARXIV.1707.06347"},{"key":"10588_CR29","doi-asserted-by":"publisher","first-page":"1056","DOI":"10.7717\/peerj-cs.1056","volume":"8","author":"A Skrynnik","year":"2022","unstructured":"Skrynnik A, Andreychuk A, Yakovlev K, Panov A (2022) Pathfinding in stochastic environments: learning vs planning. Peer J Comput Sci 8:1056. https:\/\/doi.org\/10.7717\/peerj-cs.1056","journal-title":"Peer J Comput Sci"},{"key":"10588_CR30","doi-asserted-by":"publisher","unstructured":"Asgari K, Mohammadian AA, Tefagh M (2022) DyFEn: agent-based fee setting in payment channel networks. arXiv. https:\/\/doi.org\/10.48550\/ARXIV.2210.08197","DOI":"10.48550\/ARXIV.2210.08197"},{"key":"10588_CR31","unstructured":"The Proximal Policy Optimization algorithm. Stable Baselines3 Library. Retrieved Feb 22, 2023 from https:\/\/stable-baselines3.readthedocs.io\/en\/master\/modules\/ppo.html (2021)"},{"key":"10588_CR32","doi-asserted-by":"publisher","unstructured":"Brockman G, Cheung V, Pettersson L, Schneider J, Schulman J, Tang J, Zaremba W (2016). OpenAI gym arXiv. https:\/\/doi.org\/10.48550\/arXiv.1606.01540","DOI":"10.48550\/arXiv.1606.01540"},{"issue":"8","key":"10588_CR33","doi-asserted-by":"publisher","DOI":"10.1088\/1367-2630\/aba062","volume":"22","author":"J-H Lin","year":"2020","unstructured":"Lin J-H, Primicerio K, Squartini T, Decker C, Tessone CJ (2020) Lightning network: a second path towards centralisation of the bitcoin economy. New J Phys 22(8):083022. https:\/\/doi.org\/10.1088\/1367-2630\/aba062","journal-title":"New J Phys"},{"key":"10588_CR34","doi-asserted-by":"publisher","unstructured":"Rohrer, E., Malliaris, J., Tschorsch, F (2019) Discharged payment channels: quantifying the lightning network\u2019s resilience to topology-based attacks. arXiv. https:\/\/doi.org\/10.48550\/ARXIV.1904.10253","DOI":"10.48550\/ARXIV.1904.10253"},{"key":"10588_CR35","doi-asserted-by":"publisher","unstructured":"Tochner S, Schmid S, Zohar A (2019) hijacking routes in payment channel networks: a predictability tradeoff. arXiv. https:\/\/doi.org\/10.48550\/ARXIV.1909.06890","DOI":"10.48550\/ARXIV.1909.06890"},{"issue":"2","key":"10588_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.telpol.2023.102696","volume":"48","author":"P Zabka","year":"2024","unstructured":"Zabka P, F\u00f6rster K-T, Decker C, Schmid S (2024) A centrality analysis of the lightning network. Telecommun Policy 48(2):102696. https:\/\/doi.org\/10.1016\/j.telpol.2023.102696","journal-title":"Telecommun Policy"},{"key":"10588_CR37","doi-asserted-by":"publisher","unstructured":"Kumble SP, Roos S (2021) Comparative analysis of lightning\u2019s routing clients. In: 2021 IEEE international conference on decentralized applications and infrastructures (DAPPS), pp 79\u201384. https:\/\/doi.org\/10.1109\/DAPPS52256.2021.00014","DOI":"10.1109\/DAPPS52256.2021.00014"},{"key":"10588_CR38","unstructured":"Malavolta G, Moreno-Sanchez P, Kate A, Maffei M (2016) SilentWhispers: enforcing security and privacy in decentralized credit networks. Cryptology ePrint Archive, Paper 2016\/1054. Retrieved Feb 22, 2023 from https:\/\/eprint.iacr.org\/2016\/1054"},{"key":"10588_CR39","doi-asserted-by":"publisher","unstructured":"Roos S, Moreno-Sanchez P, Kate A, Goldberg I (2017) Settling payments fast and private: efficient decentralized routing for path-based transactions. arXiv. https:\/\/doi.org\/10.48550\/ARXIV.1709.05748","DOI":"10.48550\/ARXIV.1709.05748"},{"key":"10588_CR40","doi-asserted-by":"publisher","unstructured":"Wang, P., Xu, H., Jin, X., Wang, T.: Flash: Efficient dynamic routing for offchain networks. In: Proceedings of the 15th International Conference on Emerging Networking Experiments And Technologies. CoNEXT \u201919, pp. 370\u2013381. Association for Computing Machinery, New York, NY, USA (2019). doi: https:\/\/doi.org\/10.1145\/3359989.3365411","DOI":"10.1145\/3359989.3365411"},{"key":"10588_CR41","doi-asserted-by":"publisher","first-page":"55916","DOI":"10.1109\/ACCESS.2019.2913776","volume":"7","author":"Z Mammeri","year":"2019","unstructured":"Mammeri Z (2019) Reinforcement learning based routing in networks: review and classification of approaches. IEEE Access 7:55916\u201355950. https:\/\/doi.org\/10.1109\/ACCESS.2019.2913776","journal-title":"IEEE Access"},{"issue":"3","key":"10588_CR42","doi-asserted-by":"publisher","first-page":"3671","DOI":"10.32604\/cmc.2021.017475","volume":"68","author":"D Godfrey","year":"2021","unstructured":"Godfrey D, Kim B-S, Miao H, Shah B, Hayat B, Khan I, Sung T-E, Kim K-I (2021) Q-learning based routing protocol for congestion avoidance. Comput Mater Cont 68(3):3671\u20133692. https:\/\/doi.org\/10.32604\/cmc.2021.017475","journal-title":"Comput Mater Cont"},{"key":"10588_CR43","doi-asserted-by":"publisher","unstructured":"Davis V, Harrison B (2022) Learning a scalable algorithm for improving betweenness in the lightning network. In: 2022 fourth international conference on blockchain computing and applications (BCCA), pp 119\u2013126. https:\/\/doi.org\/10.1109\/BCCA55292.2022.9922233","DOI":"10.1109\/BCCA55292.2022.9922233"},{"key":"10588_CR44","doi-asserted-by":"publisher","unstructured":"D\u2019Angelo G, Severini L, Velaj Y (2016) On the maximum betweenness improvement problem. Electronic notes in theoretical computer science. In: Proceedings of ICTCS 2015, the 16th Italian conference on theoretical computer science, vol 322, pp 153\u2013168. https:\/\/doi.org\/10.1016\/j.entcs.2016.03.011","DOI":"10.1016\/j.entcs.2016.03.011"},{"key":"10588_CR45","doi-asserted-by":"publisher","unstructured":"Kulvicius T, Herzog S, L\u00fcddecke T, Tamosiunaite M, W\u00f6rg\u00f6tter F (2020). One-shot path planning for multi-agent systems using fully convolutional neural network. https:\/\/doi.org\/10.48550\/ARXIV.2004.00568","DOI":"10.48550\/ARXIV.2004.00568"},{"key":"10588_CR46","unstructured":"Valko D, Kudenko D (2023) Increasing energy efficiency of bitcoin infrastructure with reinforcement learning and one-shot path planning for the lightning network. In: Proc. of the Adaptive and Learning Agents Workshop (ALA 2023) at AAMAS 2023, May 29-30. ALA 2023. Cruz, Hayes, Wang, Yates (eds.), London, UK. https:\/\/alaworkshop2023.github.io\/"},{"key":"10588_CR47","doi-asserted-by":"publisher","unstructured":"Decker C (2020) Lightning network research: topology datasets. GitHub. Retrieved February 22, 2023 from https:\/\/github.com\/lnresearch\/topology. https:\/\/doi.org\/10.5281\/zenodo.4088530","DOI":"10.5281\/zenodo.4088530"},{"key":"10588_CR48","doi-asserted-by":"crossref","unstructured":"Rozemberczki B, Kiss O, Sarkar R (2020) Little ball of fur: a python library for graph sampling. In: Proceedings of the 29th ACM international conference on information and knowledge management (CIKM \u201920), pp. 3133\u20133140. ACM","DOI":"10.1145\/3340531.3412758"},{"key":"10588_CR49","doi-asserted-by":"publisher","unstructured":"Leskovec J, Faloutsos C (2006) Sampling from large graphs. In: Proceedings of the 12th ACM SIGKDD international conference on knowledge discovery and data mining. KDD \u201906, pp. 631\u2013636. Association for Computing Machinery, New York, NY, USA. https:\/\/doi.org\/10.1145\/1150402.1150479","DOI":"10.1145\/1150402.1150479"},{"key":"10588_CR50","doi-asserted-by":"publisher","unstructured":"Kumble SP, Epema D, Roos, S (2021) How lightning\u2019s routing diminishes its anonymity. In: Proceedings of the 16th international conference on availability, reliability and security. ARES 21. Association for Computing Machinery, New York, NY, USA. https:\/\/doi.org\/10.1145\/3465481.3465761","DOI":"10.1145\/3465481.3465761"},{"key":"10588_CR51","doi-asserted-by":"publisher","unstructured":"Lottick K, Susai S, Friedler SA, Wilson JP (2019) Energy usage reports: environmental awareness as part of algorithmic accountability. arXiv. https:\/\/doi.org\/10.48550\/ARXIV.1911.08354","DOI":"10.48550\/ARXIV.1911.08354"},{"key":"10588_CR52","doi-asserted-by":"publisher","first-page":"801","DOI":"10.1016\/j.joule.2018.04.016","volume":"2","author":"AWG Vries","year":"2018","unstructured":"Vries AWG (2018) Bitcoin\u2019s growing energy problem. Joule 2:801\u2013805","journal-title":"Joule"},{"key":"10588_CR53","doi-asserted-by":"publisher","unstructured":"Strubell E, Ganesh A, McCallum A (2019) Energy and policy considerations for deep learning in NLP. arXiv. https:\/\/doi.org\/10.48550\/ARXIV.1906.02243","DOI":"10.48550\/ARXIV.1906.02243"},{"key":"10588_CR54","doi-asserted-by":"publisher","unstructured":"Posani L, Paccoia A, Moschettini M (2019) The carbon footprint of distributed cloud storage. arXiv. https:\/\/doi.org\/10.48550\/ARXIV.1803.06973","DOI":"10.48550\/ARXIV.1803.06973"},{"key":"10588_CR55","doi-asserted-by":"publisher","unstructured":"Patterson D, Gonzalez J, Le Q, Liang C, Munguia L-M, Rothchild D, So D, Texier M, Dean J (2021) Carbon emissions and large neural network training. arXiv. https:\/\/doi.org\/10.48550\/ARXIV.2104.10350","DOI":"10.48550\/ARXIV.2104.10350"},{"key":"10588_CR56","unstructured":"Gitzel R (2022) Software tools to determine the carbon footprint of AI code. Retrieved Feb 22, 2023 from https:\/\/www.linkedin.com\/pulse\/software-tools-determine-carbon-footprint-ai-code-ralf-gitzel\/"},{"key":"10588_CR57","doi-asserted-by":"publisher","unstructured":"Schubert S, Kostic D, Zwaenepoel W, Shin KG (2012) Profiling software for energy consumption. In: 2012 IEEE international conference on green computing and communications, pp 515\u2013522. https:\/\/doi.org\/10.1109\/GreenCom.2012.86","DOI":"10.1109\/GreenCom.2012.86"},{"issue":"12","key":"10588_CR58","doi-asserted-by":"publisher","first-page":"2100707","DOI":"10.1002\/advs.202100707","volume":"8","author":"L Lannelongue","year":"2021","unstructured":"Lannelongue L, Grealey J, Inouye M (2021) Green algorithms: quantifying the carbon footprint of computation. Adv Sci 8(12):2100707. https:\/\/doi.org\/10.1002\/advs.202100707","journal-title":"Adv Sci"},{"key":"10588_CR59","doi-asserted-by":"publisher","unstructured":"Hessel M, Hasselt HV, Modayil J, Silver D (2019) On Inductive Biases in Deep Reinforcement Learning. arXiv. https:\/\/doi.org\/10.48550\/ARXIV.1907.02908","DOI":"10.48550\/ARXIV.1907.02908"},{"key":"10588_CR60","doi-asserted-by":"publisher","unstructured":"Wei H, Liu X, Ying L (2023) Safe reinforcement learning with instantaneous constraints: the role of aggressive exploration. https:\/\/doi.org\/10.48550\/ARXIV.2312.14470","DOI":"10.48550\/ARXIV.2312.14470"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10588-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-024-10588-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10588-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T01:29:51Z","timestamp":1757122191000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-024-10588-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,11]]},"references-count":60,"journal-issue":{"issue":"23","published-print":{"date-parts":[[2025,8]]}},"alternative-id":["10588"],"URL":"https:\/\/doi.org\/10.1007\/s00521-024-10588-2","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,11]]},"assertion":[{"value":"16 November 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 October 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 December 2024","order":3,"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 that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}