{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,18]],"date-time":"2026-04-18T05:50:50Z","timestamp":1776491450212,"version":"3.51.2"},"reference-count":60,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T00:00:00Z","timestamp":1774915200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T00:00:00Z","timestamp":1774915200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Key Research and Development Program of National Natural Science Foundation of China"},{"name":"Hainan Province Science and Technology Special Fund"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Computing"],"published-print":{"date-parts":[[2026,4]]},"DOI":"10.1007\/s00607-026-01616-1","type":"journal-article","created":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T07:06:39Z","timestamp":1774940799000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Smartguardia: smart contract vulnerability detection based on pruning abstract syntax tree and transfer learning"],"prefix":"10.1007","volume":"108","author":[{"given":"Zhaoxing","family":"Jing","sequence":"first","affiliation":[]},{"given":"Chunjie","family":"Cao","sequence":"additional","affiliation":[]},{"given":"Zhiyuan","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Hongyu","family":"Yan","sequence":"additional","affiliation":[]},{"given":"Jingzhang","family":"Sun","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,3,31]]},"reference":[{"issue":"9","key":"1616_CR1","doi-asserted-by":"publisher","first-page":"2971","DOI":"10.1007\/s00607-024-01314-w","volume":"106","author":"SE Haddouti","year":"2024","unstructured":"Haddouti SE, Khaldoune M, Ayache M, Kettani MDEE (2024) Smart contracts auditing and multi-classification using machine learning algorithms: an efficient vulnerability detection in ethereum blockchain. Computing 106(9):2971\u20133003","journal-title":"Computing"},{"issue":"4","key":"1616_CR2","doi-asserted-by":"publisher","first-page":"3587","DOI":"10.1109\/JIOT.2022.3222521","volume":"10","author":"Y Fu","year":"2022","unstructured":"Fu Y, Li C, Yu FR, Luan TH, Zhao P, Liu S (2022) A survey of blockchain and intelligent networking for the metaverse. IEEE Internet Things J 10(4):3587\u20133610","journal-title":"IEEE Internet Things J"},{"key":"1616_CR3","unstructured":"Bitcoin NS (2008) Bitcoin: a peer-to-peer electronic cash system"},{"issue":"2014","key":"1616_CR4","first-page":"1","volume":"151","author":"G Wood","year":"2014","unstructured":"Wood G et al (2014) Ethereum: a secure decentralised generalised transaction ledger. Ethereum Project Yellow Paper 151(2014):1\u201332","journal-title":"Ethereum Project Yellow Paper"},{"issue":"2","key":"1616_CR5","first-page":"28","volume":"18","author":"N Szabo","year":"1996","unstructured":"Szabo N (1996) Smart contracts: building blocks for digital markets. EXTROPY: J Transhumanist Thought (16) 18(2):28","journal-title":"EXTROPY: J Transhumanist Thought (16)"},{"issue":"3","key":"1616_CR6","doi-asserted-by":"publisher","first-page":"2865","DOI":"10.1007\/s40747-021-00617-1","volume":"9","author":"X Zhai","year":"2023","unstructured":"Zhai X, Liu Y, Li W, Dai H-N, Imran M (2023) TVS: a trusted verification scheme for office documents based on blockchain. Complex Intell Syst 9(3):2865\u20132877","journal-title":"Complex Intell Syst"},{"key":"1616_CR7","unstructured":"Smart Contract Deployment Statistics. https:\/\/dune.com\/pcaversaccio\/smart-contract-deployment-statistics. Accessed 26 Oct 2025"},{"key":"1616_CR8","unstructured":"SlowMist Hacked Statistics. https:\/\/hacked.slowmist.io\/zh\/statistics\/?c=all&d=all. Accessed: 26 June 2023"},{"key":"1616_CR9","doi-asserted-by":"crossref","unstructured":"Nguyen HH, Nguyen AT, Kim D (2022) Mando-guru: vulnerability detection for smart contract source code by heterogeneous graph embeddings. In: Proceedings of the 30th ACM joint European software engineering conference and symposium on the foundations of software engineering","DOI":"10.1145\/3540250.3558927"},{"key":"1616_CR10","unstructured":"Total Value Locked All Chains. https:\/\/defillama.com\/chains. Accessed 26 June 2023"},{"key":"1616_CR11","unstructured":"Understanding Dao Hack Journalists. https:\/\/www.coindesk.com\/understanding-dao-hack-journalists"},{"key":"1616_CR12","unstructured":"Digital Currency Ethereum Is Cratering Because of a $50 Million Hack. https:\/\/www.businessinsider.com\/dao-hacked-ethereum-crashing-in-value-tens-of-millions-allegedly-stolen-2016-6"},{"key":"1616_CR13","unstructured":"CertiK: GMX Incident Analysis. Accessed 25 Nov 2025. https:\/\/www.certik.com\/resources\/blog\/gmx-incident-analysis"},{"key":"1616_CR14","unstructured":"Halborn: explained: the GemPad Hack. Accessed 25 Nov 2025. https:\/\/www.halborn.com\/blog\/post\/explained-the-gempad-hack-december-2024"},{"key":"1616_CR15","unstructured":"Penpie: Penpie post-mortem report. Accessed 25 Nov 2025. https:\/\/blog.penpiexyz.io\/penpie-post-mortem-report-1ac9863b663a"},{"key":"1616_CR16","doi-asserted-by":"crossref","unstructured":"Zhuang Y, Liu Z, Qian P, Liu Q, Wang X, He Q (2021) Smart contract vulnerability detection using graph neural networks. In: Proceedings of the twenty-ninth international conference on international joint conferences on artificial intelligence, pp 3283\u20133290","DOI":"10.24963\/ijcai.2020\/454"},{"key":"1616_CR17","doi-asserted-by":"crossref","unstructured":"Liu Z, Qian P, Wang X, Zhu L, He Q, Ji S (2021) Smart contract vulnerability detection: from pure neural network to interpretable graph feature and expert pattern fusion. arXiv preprint arXiv:2106.09282","DOI":"10.24963\/ijcai.2021\/379"},{"key":"1616_CR18","doi-asserted-by":"publisher","first-page":"19685","DOI":"10.1109\/ACCESS.2020.2969429","volume":"8","author":"P Qian","year":"2020","unstructured":"Qian P, Liu Z, He Q, Zimmermann R, Wang X (2020) Towards automated reentrancy detection for smart contracts based on sequential models. IEEE Access 8:19685\u201319695","journal-title":"IEEE Access"},{"key":"1616_CR19","unstructured":"Zhangyin F, Daya G, Nan D, Xiaocheng F (2020) Codebert: a pre-trained model for programming and natural languages. In: EMNLP"},{"key":"1616_CR20","doi-asserted-by":"crossref","unstructured":"Wu H, Zhang Z, Wang S, Lei Y, Lin B, Qin Y, Zhang H, Mao X (2021) Peculiar: smart contract vulnerability detection based on crucial data flow graph and pre-training techniques. In: 2021 IEEE 32nd international symposium on software reliability engineering (ISSRE), pp 378\u2013389 . IEEE","DOI":"10.1109\/ISSRE52982.2021.00047"},{"key":"1616_CR21","unstructured":"Guo D, Ren S, Lu S, Feng Z, Tang D, Shujie L, Zhou L, Duan N, Svyatkovskiy A, Fu S, et al. (2020) Graphcodebert: pre-training code representations with data flow. In: International conference on learning representations"},{"key":"1616_CR22","unstructured":"A Historical Collection of Reentrancy Attacks. https:\/\/github.com\/pcaversaccio\/reentrancy-attacks"},{"issue":"2","key":"1616_CR23","doi-asserted-by":"publisher","first-page":"984","DOI":"10.1109\/TR.2022.3161634","volume":"71","author":"B Li","year":"2022","unstructured":"Li B, Pan Z, Hu T (2022) Redefender: detecting reentrancy vulnerabilities in smart contracts automatically. IEEE Trans Reliab 71(2):984\u2013999","journal-title":"IEEE Trans Reliab"},{"issue":"9","key":"1616_CR24","doi-asserted-by":"publisher","first-page":"3577","DOI":"10.3390\/s22093577","volume":"22","author":"L Zhang","year":"2022","unstructured":"Zhang L, Chen W, Wang W, Jin Z, Zhao C, Cai Z, Chen H (2022) Cbgru: a detection method of smart contract vulnerability based on a hybrid model. Sensors 22(9):3577","journal-title":"Sensors"},{"key":"1616_CR25","doi-asserted-by":"crossref","unstructured":"Liu Z, Qian P, Wang X, Zhuang Y, Qiu L, Wang X (2021) Combining graph neural networks with expert knowledge for smart contract vulnerability detection. IEEE Trans Knowl Data Eng","DOI":"10.1109\/TKDE.2021.3095196"},{"issue":"FSE","key":"1616_CR26","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1145\/3643734","volume":"1","author":"Z Wang","year":"2024","unstructured":"Wang Z, Chen J, Wang Y, Zhang Y, Zhang W, Zheng Z (2024) Efficiently detecting reentrancy vulnerabilities in complex smart contracts. Proc ACM Softw Eng 1(FSE):8\u20131821. https:\/\/doi.org\/10.1145\/3643734","journal-title":"Proc ACM Softw Eng"},{"key":"1616_CR27","doi-asserted-by":"publisher","unstructured":"Sun Y, Wu D, Xue Y, Liu H, Wang H, Xu Z, Xie X, Liu Y (2024) Gptscan: detecting logic vulnerabilities in smart contracts by combining gpt with program analysis. In: Proceedings of the 46th IEEE\/ACM international conference on software engineering (ICSE), pp 2048\u20132060 . https:\/\/doi.org\/10.1145\/3597503.3639117","DOI":"10.1145\/3597503.3639117"},{"key":"1616_CR28","doi-asserted-by":"publisher","DOI":"10.1007\/s10515-025-00546-0","author":"M Li","year":"2026","unstructured":"Li M, Shen Q, Ren X et al (2026) Hmf: enhancing reentrancy vulnerability detection and repair with a hybrid model framework. Autom Softw Eng. https:\/\/doi.org\/10.1007\/s10515-025-00546-0","journal-title":"Autom Softw Eng"},{"key":"1616_CR29","doi-asserted-by":"crossref","unstructured":"Feng H, Fu X, Sun H, Wang H, Zhang Y (2020) Efficient vulnerability detection based on abstract syntax tree and deep learning. In: IEEE INFOCOM 2020-IEEE conference on computer communications workshops (INFOCOM WKSHPS), pp 722\u2013727 . IEEE","DOI":"10.1109\/INFOCOMWKSHPS50562.2020.9163061"},{"key":"1616_CR30","doi-asserted-by":"crossref","unstructured":"Gu M, Feng H, Sun H, Liu P, Yue Q, Hu J, Cao C, Zhang Y (2022) Hierarchical attention network for interpretable and fine-grained vulnerability detection. In: IEEE INFOCOM 2022-IEEE conference on computer communications workshops (INFOCOM WKSHPS), pp 1\u20136 . IEEE","DOI":"10.1109\/INFOCOMWKSHPS54753.2022.9798297"},{"key":"1616_CR31","doi-asserted-by":"crossref","unstructured":"Zhang J, Wang X, Zhang H, Sun H, Wang K, Liu X (2019) A novel neural source code representation based on abstract syntax tree. In: 2019 IEEE\/ACM 41st international conference on software engineering (ICSE), pp 783\u2013794 . IEEE","DOI":"10.1109\/ICSE.2019.00086"},{"key":"1616_CR32","unstructured":"Jiang X, Zheng Z, Lyu C, Li L, Lyu L (2021) Treebert: a tree-based pre-trained model for programming language. In: Uncertainty in artificial intelligence, pp 54\u201363 . PMLR"},{"key":"1616_CR33","unstructured":"Kanade A, Maniatis P, Balakrishnan G, Shi K (2020) Learning and evaluating contextual embedding of source code. In: International conference on machine learning, pp 5110\u20135121. PMLR"},{"key":"1616_CR34","doi-asserted-by":"crossref","unstructured":"Svyatkovskiy A, Deng SK, Fu S, Sundaresan N (2020) Intellicode compose: code generation using transformer. In: Proceedings of the 28th ACM joint meeting on European software engineering conference and symposium on the foundations of software engineering, pp 1433\u20131443","DOI":"10.1145\/3368089.3417058"},{"key":"1616_CR35","unstructured":"Karampatsis R-M, Sutton C (2020) Scelmo: source code embeddings from language models. arXiv preprint arXiv:2004.13214"},{"key":"1616_CR36","doi-asserted-by":"crossref","unstructured":"Ahmad W, Chakraborty S, Ray B, Chang K (2021) Unified pre-training for program understanding and generation. In: Proceedings of the 2021 conference of the North American chapter of the association for computational linguistics: human language technologies","DOI":"10.18653\/v1\/2021.naacl-main.211"},{"key":"1616_CR37","doi-asserted-by":"crossref","unstructured":"Guo D, Lu S, Duan N, Wang Y, Zhou M, Yin J (2022) Unixcoder: unified cross-modal pre-training for code representation. In: Proceedings of the 60th annual meeting of the association for computational linguistics (volume 1: long papers), pp 7212\u20137225","DOI":"10.18653\/v1\/2022.acl-long.499"},{"issue":"C","key":"1616_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2022.107839","volume":"100","author":"A Lakhan","year":"2022","unstructured":"Lakhan A, Mohammed MA, Kadry S, AlQahtani SA, Maashi MS, Abdulkareem KH (2022) Federated learning-aware multi-objective modeling and blockchain-enable system for IIoT applications. Comput Electr Eng 100(C):107839. https:\/\/doi.org\/10.1016\/j.compeleceng.2022.107839","journal-title":"Comput Electr Eng"},{"key":"1616_CR39","doi-asserted-by":"publisher","first-page":"3293","DOI":"10.1007\/s11276-022-03054-1","volume":"28","author":"S Chen","year":"2022","unstructured":"Chen S, Ge X, Wang Q, Miao Y, Ruan X (2022) DDPG-based intelligent rechargeable fog computation offloading for IoT. Wirel Netw 28:3293\u20133304. https:\/\/doi.org\/10.1007\/s11276-022-03054-1","journal-title":"Wirel Netw"},{"key":"1616_CR40","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1016\/j.ins.2020.01.046","volume":"519","author":"J Li","year":"2020","unstructured":"Li J, Lin J (2020) A probability distribution detection based hybrid ensemble QoS prediction approach. Inf Sci 519:289\u2013305. https:\/\/doi.org\/10.1016\/j.ins.2020.01.046","journal-title":"Inf Sci"},{"key":"1616_CR41","doi-asserted-by":"crossref","unstructured":"Chen M, Liu Q, Huang S, Dang C (2020) Environmental cost control system of manufacturing enterprises using artificial intelligence based on value chain of circular economy. Enterpr Inf Syst 16","DOI":"10.1080\/17517575.2020.1856422"},{"issue":"2","key":"1616_CR42","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1016\/j.dcan.2022.10.005","volume":"9","author":"W Wang","year":"2023","unstructured":"Wang W, Huang H, Yin Z, Gadekallu TR, Alazab M, Su C (2023) Smart contract token-based privacy-preserving access control system for industrial internet of things. Digit Commun Netw 9(2):337\u2013346. https:\/\/doi.org\/10.1016\/j.dcan.2022.10.005","journal-title":"Digit Commun Netw"},{"key":"1616_CR43","doi-asserted-by":"crossref","unstructured":"Rafique W, Shah B, Hakak S, Khan M, Anwar S (2023) Blockchain based secure interoperable framework for the internet of medical things. In: Anwar S, Ullah A, Rocha Sousa MJ (eds) Proceedings of international conference on information technology and applications, pp 533\u2013545. Springer, Singapore","DOI":"10.1007\/978-981-19-9331-2_46"},{"issue":"12","key":"1616_CR44","doi-asserted-by":"publisher","first-page":"4621","DOI":"10.3390\/s22124621","volume":"22","author":"L Zhang","year":"2022","unstructured":"Zhang L, Li Y, Jin T, Wang W, Jin Z, Zhao C, Cai Z, Chen H (2022) SPCBIG-EC: a robust serial hybrid model for smart contract vulnerability detection. Sensors 22(12):4621. https:\/\/doi.org\/10.3390\/s22124621","journal-title":"Sensors"},{"key":"1616_CR45","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2022.109289","volume":"217","author":"L Zhang","year":"2022","unstructured":"Zhang L, Wang J, Wang W, Jin Z, Su Y, Chen H (2022) Smart contract vulnerability detection combined with multi-objective detection. Comput Netw 217:109289. https:\/\/doi.org\/10.1016\/j.comnet.2022.109289","journal-title":"Comput Netw"},{"key":"1616_CR46","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.126133","volume":"266","author":"E Elahi","year":"2025","unstructured":"Elahi E, Anwar S, Al-kfairy M, Rodrigues JJPC, Ngueilbaye A, Halim Z, Waqas M (2025) Graph attention-based neural collaborative filtering for item-specific recommendation system using knowledge graph. Expert Syst Appl 266:126133. https:\/\/doi.org\/10.1016\/j.eswa.2024.126133","journal-title":"Expert Syst Appl"},{"key":"1616_CR47","doi-asserted-by":"crossref","unstructured":"Arshad U, Anwar S, Shah B, Halim Z (2022) Futuristic blockchain based computer vision technique for environmentally informed smoking cessation: a revolutionary approach to predictive modeling. In: International conference on information technology and applications, pp 113\u2013126. Springer, Singapore","DOI":"10.1007\/978-981-99-8324-7_11"},{"key":"1616_CR48","doi-asserted-by":"crossref","unstructured":"Arshad U, Tubaishat A, Ullah A, Halim Z, Anwar S (2025) Blockchain-enhanced machine learning for dynamic routing and secure communications in autonomous vehicle networks. In: AAAI 2025 summer symposium series. Dubai, United Arab Emirates. https:\/\/summer-symposia-proceedings.aaai.org\/preprints\/pdfs\/5295.pdf","DOI":"10.1609\/aaaiss.v6i1.36020"},{"issue":"C","key":"1616_CR49","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2023.108702","volume":"108","author":"M Amin","year":"2023","unstructured":"Amin M, Al-Obeidat F, Tubaishat A, Shah B, Anwar S, Tanveer TA (2023) Cyber security and beyond: detecting malware and concept drift in AI-based sensor data streams using statistical techniques. Comput Electr Eng 108(C):108702. https:\/\/doi.org\/10.1016\/j.compeleceng.2023.108702","journal-title":"Comput Electr Eng"},{"key":"1616_CR50","doi-asserted-by":"publisher","first-page":"547","DOI":"10.1007\/s10723-018-9455-1","volume":"18","author":"T Ali","year":"2020","unstructured":"Ali T, Khan Y, Ali T (2020) An automated permission selection framework for android platform. J Grid Comput 18:547\u2013561. https:\/\/doi.org\/10.1007\/s10723-018-9455-1","journal-title":"J Grid Comput"},{"issue":"5","key":"1616_CR51","doi-asserted-by":"publisher","first-page":"1800","DOI":"10.1109\/TSE.2020.3038681","volume":"48","author":"S Liu","year":"2020","unstructured":"Liu S, Gao C, Chen S, Nie LY, Liu Y (2020) Atom: commit message generation based on abstract syntax tree and hybrid ranking. IEEE Trans Softw Eng 48(5):1800\u20131817","journal-title":"IEEE Trans Softw Eng"},{"key":"1616_CR52","unstructured":"The Ecology of Smart Contract Language. https:\/\/defillama.com\/languages"},{"key":"1616_CR53","unstructured":"Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J (2013) Distributed representations of words and phrases and their compositionality. Adv Neural Inf Process Syst 26"},{"key":"1616_CR54","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 Process Syst 30"},{"key":"1616_CR55","doi-asserted-by":"crossref","unstructured":"Ferreira JF, Cruz P, Durieux T, Abreu R (2020) Smartbugs: a framework to analyze solidity smart contracts. In: Proceedings of the 35th IEEE\/ACM international conference on automated software engineering, pp 1349\u20131352","DOI":"10.1145\/3324884.3415298"},{"key":"1616_CR56","doi-asserted-by":"crossref","unstructured":"Luu L, Chu D-H, Olickel H, Saxena P, Hobor A (2016) Making smart contracts smarter. In: Proceedings of the 2016 ACM SIGSAC conference on computer and communications security, pp 254\u2013269","DOI":"10.1145\/2976749.2978309"},{"key":"1616_CR57","unstructured":"Mueller B (2017) Mythril-reversing and bug hunting framework for the Ethereum blockchain"},{"key":"1616_CR58","doi-asserted-by":"crossref","unstructured":"Feist J, Grieco G, Groce A (2019) Slither: a static analysis framework for smart contracts. In: 2019 IEEE\/ACM 2nd international workshop on emerging trends in software engineering for blockchain (WETSEB), pp 8\u201315 . IEEE","DOI":"10.1109\/WETSEB.2019.00008"},{"key":"1616_CR59","doi-asserted-by":"crossref","unstructured":"Mossberg M, Manzano F, Hennenfent E, Groce A, Grieco G, Feist J, Brunson T, Dinaburg A (2019) Manticore: a user-friendly symbolic execution framework for binaries and smart contracts. In: 2019 34th IEEE\/ACM international conference on automated software engineering (ASE), pp 1186\u20131189. IEEE","DOI":"10.1109\/ASE.2019.00133"},{"key":"1616_CR60","doi-asserted-by":"crossref","unstructured":"Tsankov P, Dan A, Drachsler-Cohen D, Gervais A, Buenzli F, Vechev M (2018) Securify: practical security analysis of smart contracts. In: Proceedings of the 2018 ACM SIGSAC conference on computer and communications security, pp 67\u201382","DOI":"10.1145\/3243734.3243780"}],"container-title":["Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00607-026-01616-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00607-026-01616-1","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00607-026-01616-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,18]],"date-time":"2026-04-18T05:13:21Z","timestamp":1776489201000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00607-026-01616-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,31]]},"references-count":60,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2026,4]]}},"alternative-id":["1616"],"URL":"https:\/\/doi.org\/10.1007\/s00607-026-01616-1","relation":{},"ISSN":["0010-485X","1436-5057"],"issn-type":[{"value":"0010-485X","type":"print"},{"value":"1436-5057","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,31]]},"assertion":[{"value":"9 August 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 January 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 March 2026","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 conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interests"}}],"article-number":"60"}}