{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T11:12:10Z","timestamp":1775905930690,"version":"3.50.1"},"reference-count":54,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,11,4]],"date-time":"2025-11-04T00:00:00Z","timestamp":1762214400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,11,4]],"date-time":"2025-11-04T00:00:00Z","timestamp":1762214400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Discov Artif Intell"],"DOI":"10.1007\/s44163-025-00507-2","type":"journal-article","created":{"date-parts":[[2025,11,4]],"date-time":"2025-11-04T10:40:05Z","timestamp":1762252805000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Artificial intelligence approach to intrusion detection in industrial control systems with real world dataset generation and model evaluation"],"prefix":"10.1007","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-0785-9069","authenticated-orcid":false,"given":"Ahmad","family":"Houkan","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3195-0741","authenticated-orcid":false,"given":"Ashwin Kumar","family":"Sahoo","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0412-7722","authenticated-orcid":false,"given":"Sarada Prasad","family":"Gochhayat","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0112-7129","authenticated-orcid":false,"given":"Prabodh Kumar","family":"Sahoo","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,11,4]]},"reference":[{"key":"507_CR1","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1109\/MSP.2011.67","volume":"9","author":"R Langner","year":"2011","unstructured":"Langner R. Stuxnet: dissecting a cyberwarfare weapon. IEEE Security Privacy. 2011;9:49\u201351. https:\/\/doi.org\/10.1109\/MSP.2011.67.","journal-title":"IEEE Security Privacy"},{"key":"507_CR2","doi-asserted-by":"publisher","unstructured":"Houkan A, Sahoo AK, Nayak S. Industry 5.0 and sustainable development in the developing world. In: Proc. 2023 IEEE 3rd Int. Conf. Sustainable Energy and Future Electric Transportation (SEFET), Bhubaneswar, India, 2023; pp. 1\u20136 https:\/\/doi.org\/10.1109\/SeFeT57834.2023.10245935","DOI":"10.1109\/SeFeT57834.2023.10245935"},{"key":"507_CR3","unstructured":"Kumar M. Irongate\u2014New Stuxnet-like malware targets industrial control systems. The Hacker News, 2016. https:\/\/thehackernews.com\/2016\/06\/irongate-stuxnet-malware.html. Accessed 18 Oct 2022"},{"key":"507_CR4","unstructured":"Di Pinto A, Dragoni Y, Carcano A. TRITON: The first ICS cyber attack on safety instrument systems. Proc. Black Hat USA, 2018; pp 1\u201326."},{"key":"507_CR5","doi-asserted-by":"publisher","first-page":"143343","DOI":"10.1109\/ACCESS.2024.3466391","volume":"12","author":"M Koca","year":"2024","unstructured":"Koca M, Avci I. A novel hybrid model detection of security vulnerabilities in industrial control systems and IoT using GCN+LSTM. IEEE Access. 2024;12:143343\u201351. https:\/\/doi.org\/10.1109\/ACCESS.2024.3466391.","journal-title":"IEEE Access"},{"issue":"5","key":"507_CR6","doi-asserted-by":"publisher","first-page":"2486","DOI":"10.3906\/elk-2102-89","volume":"29","author":"M Koca","year":"2021","unstructured":"Koca M, Aydin MA, Sertba\u015f A, Zaim AH. A new distributed anomaly detection approach for log IDS management based ondeep learning. Turk J Electr Eng Comput Sci. 2021;29(5):2486\u2013501. https:\/\/doi.org\/10.3906\/elk-2102-89.","journal-title":"Turk J Electr Eng Comput Sci"},{"issue":"11","key":"507_CR7","doi-asserted-by":"publisher","DOI":"10.3390\/app14114646","volume":"14","author":"\u0130 Avc\u0131","year":"2024","unstructured":"Avc\u0131 \u0130, Koca M. Intelligent transportation system technologies, challenges and security. Appl Sci. 2024;14(11):4646. https:\/\/doi.org\/10.3390\/app14114646.","journal-title":"Appl Sci"},{"key":"507_CR8","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-025-02531-1","author":"M Koca","year":"2025","unstructured":"Koca M, \u00c7ift\u00e7i S. A comprehensive bibliometric analysis of Big Data and Cyber Security: intellectual structure, trends, and global collaborations. Knowl Inf Syst. 2025. https:\/\/doi.org\/10.1007\/s10115-025-02531-1.","journal-title":"Knowl Inf Syst"},{"key":"507_CR9","doi-asserted-by":"publisher","unstructured":"Drias Z, Serhrouchni A, Vogel O. Taxonomy of attacks on industrial control protocols. In: 2015 International Conference on Protocol Engineering (ICPE) and International Conference on New Technologies of Distributed Systems (NTDS), 2015; pp 1\u20136. https:\/\/doi.org\/10.1109\/NOTERE.2015.7293513","DOI":"10.1109\/NOTERE.2015.7293513"},{"key":"507_CR10","doi-asserted-by":"publisher","first-page":"112392","DOI":"10.1109\/ACCESS.2022.3216617","volume":"10","author":"S Neupane","year":"2022","unstructured":"Neupane S, et al. Explainable intrusion detection systems (X-IDS): a survey of current methods, challenges, and opportunities. IEEE Access. 2022;10:112392\u2013415. https:\/\/doi.org\/10.1109\/ACCESS.2022.3216617.","journal-title":"IEEE Access"},{"key":"507_CR11","doi-asserted-by":"publisher","first-page":"1775","DOI":"10.1109\/COMST.2023.3280465","volume":"25","author":"N Moustafa","year":"2023","unstructured":"Moustafa N, Koroniotis N, Keshk M, Zomaya AY, Tari Z. Explainable intrusion detection for cyber defenses in the internet of things: opportunities and solutions. IEEE Commun Surv Tutor. 2023;25:1775\u2013807. https:\/\/doi.org\/10.1109\/COMST.2023.3280465.","journal-title":"IEEE Commun Surv Tutor"},{"key":"507_CR12","doi-asserted-by":"publisher","unstructured":"Alruwaili FF. Intrusion detection and prevention in industrial IoT: A technological survey. In: 2021 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), 2021. https:\/\/doi.org\/10.1109\/ICECCME52200.2021.9590961","DOI":"10.1109\/ICECCME52200.2021.9590961"},{"key":"507_CR13","doi-asserted-by":"publisher","first-page":"64140","DOI":"10.1109\/ACCESS.2024.3395991","volume":"12","author":"GB Gaggero","year":"2024","unstructured":"Gaggero GB, Armellin A, Portomauro G, Marchese M. Industrial control system-anomaly detection dataset (ICS-ADD) for cyber-physical security monitoring in smart industry environments. IEEE Access. 2024;12:64140\u20139. https:\/\/doi.org\/10.1109\/ACCESS.2024.3395991.","journal-title":"IEEE Access"},{"key":"507_CR14","doi-asserted-by":"publisher","unstructured":"Ramirez R, Chang C-K, Liang S-H. PLC cyber-security challenges in industrial networks. In: 2022 18th IEEE\/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA) 2022. https:\/\/doi.org\/10.1109\/MESA55290.2022.10004463","DOI":"10.1109\/MESA55290.2022.10004463"},{"key":"507_CR15","doi-asserted-by":"publisher","first-page":"917","DOI":"10.3390\/electronics13050917","volume":"13","author":"DS Afenu","year":"2024","unstructured":"Afenu DS, Asiri M, Saxena N. Industrial control systems security validation based on MITRE adversarial tactics, techniques, and common knowledge framework. Electronics. 2024;13:917. https:\/\/doi.org\/10.3390\/electronics13050917.","journal-title":"Electronics"},{"key":"507_CR16","doi-asserted-by":"publisher","first-page":"107982","DOI":"10.1109\/ACCESS.2023.3320928","volume":"11","author":"A Dehlaghi-Ghadim","year":"2023","unstructured":"Dehlaghi-Ghadim A, Moghadam MH, Balador A, Hansson H. Anomaly detection dataset for industrial control systems. IEEE Access. 2023;11:107982\u201396. https:\/\/doi.org\/10.1109\/ACCESS.2023.3320928.","journal-title":"IEEE Access"},{"key":"507_CR17","doi-asserted-by":"publisher","unstructured":"Hilal H, Nangim A. Network security analysis SCADA system automation on industrial process. In: 2017 9th International Conference on Broadband Communication, Wireless Sensors and Powering (BCWSP) 2017. https:\/\/doi.org\/10.1109\/BCWSP.2017.8272569","DOI":"10.1109\/BCWSP.2017.8272569"},{"key":"507_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.jisa.2025.103990","author":"E Amer","year":"2025","unstructured":"Amer E, Al-rimy BAS, El-Sappagh S. Strengthening ICS defense: Modbus-NFA behavior model for enhanced anomaly detection. J Inf Secur Appl. 2025. https:\/\/doi.org\/10.1016\/j.jisa.2025.103990.","journal-title":"J Inf Secur Appl"},{"key":"507_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2024.104143","volume":"148","author":"M Alanazi","year":"2025","unstructured":"Alanazi M, Mahmood A, Chowdhury MJM. ICS-LTU2022: a dataset for ICS vulnerabilities. Comput Secur. 2025;148:104143. https:\/\/doi.org\/10.1016\/j.cose.2024.104143.","journal-title":"Comput Secur"},{"key":"507_CR20","doi-asserted-by":"publisher","unstructured":"Bhandari S, Kukreja AK, Lazar A, Sim A, Wu K. Feature selection improves tree-based classification for wireless intrusion detection. In: Proceedings of the 3rd International Workshop on Systems and Network Telemetry and Analytics, 2020. https:\/\/doi.org\/10.1145\/3391812.3396274","DOI":"10.1145\/3391812.3396274"},{"key":"507_CR21","doi-asserted-by":"publisher","first-page":"10834","DOI":"10.1109\/ACCESS.2024.3352281","volume":"12","author":"S Dasari","year":"2024","unstructured":"Dasari S, Kaluri R. An effective classification of DDoS attacks in a distributed network by adopting hierarchical machine learning and hyperparameters optimization techniques. IEEE Access. 2024;12:10834\u201345. https:\/\/doi.org\/10.1109\/ACCESS.2024.3352281.","journal-title":"IEEE Access"},{"key":"507_CR22","doi-asserted-by":"publisher","unstructured":"Houkan A, Sahoo AK, Gochhayat SP. Industrial IoT-intrusion detection using PCA-driven decision tree. In: Colak I, Okedu KE, Raju L (eds) Proc. 2024 Int. Conf. on Advances in Electrical Power and Embedded Drive Control (ICPEDC), Lecture Notes in Electrical Engineering, vol 1371, Springer, Singapore, 2025; pp. xx\u2013xx. https:\/\/doi.org\/10.1007\/978-981-96-3694-5_26","DOI":"10.1007\/978-981-96-3694-5_26"},{"key":"507_CR23","unstructured":"Using Wireshark for packet analysis (2024) Studytonight.com. https:\/\/www.studytonight.com\/network-programming-in-python\/using-wireshark. Accessed 20 Sep 2024"},{"key":"507_CR24","unstructured":"Analyzing packet captures with Python, The VNetMan Blog, 2018. https:\/\/vnetman.github.io\/pcap\/python\/pyshark\/scapy\/libpcap\/2018\/10\/25\/analyzing-packet-captures-with-python-part-1.html. Accessed 20 Sep 2024"},{"key":"507_CR25","unstructured":"The Modbus Organization, Modbus application protocol specification, 2020. http:\/\/www.modbus.org\/docs\/Modbus_Application_Protocol_V1_1b.pdf. Accessed 25 Jul 2020"},{"key":"507_CR26","unstructured":"Patel S. IEC-61850 protocol analysis and online intrusion detection system for SCADA networks using machine learning. Dissertation, University of Victoria, 2017"},{"key":"507_CR27","doi-asserted-by":"publisher","first-page":"918","DOI":"10.3390\/pr11030918","volume":"11","author":"Z Wang","year":"2023","unstructured":"Wang Z, et al. A survey on programmable logic controller vulnerabilities, attacks, detections, and forensics. Processes. 2023;11:918. https:\/\/doi.org\/10.3390\/pr11030918.","journal-title":"Processes"},{"key":"507_CR28","unstructured":"Ping (ICMP) flood DDoS attack, Cloudflare, 2024. https:\/\/www.cloudflare.com\/learning\/ddos\/ping-icmp-flood-ddos-attack\/. Accessed 4 Oct 2024"},{"key":"507_CR29","volume-title":"Penetration testing: a hands-on introduction to hacking","author":"G Weidman","year":"2014","unstructured":"Weidman G. Penetration testing: a hands-on introduction to hacking. San Francisco: No Starch Press; 2014."},{"key":"507_CR30","unstructured":"Kurose JF, Ross KW. Computer networking: A top-down approach. Pearson, Harlow, 2017"},{"key":"507_CR31","unstructured":"Senrust. Senrust\/pymcprotocol: MC protocol (Mitsubishi communication protocol) implementation by Python. GitHub, 2024 https:\/\/github.com\/senrust\/pymcprotocol. Accessed 5 Oct 2024"},{"key":"507_CR32","unstructured":"Pymcprotocol, PyPI. https:\/\/pypi.org\/project\/pymcprotocol\/. Accessed 5 Oct 2024"},{"key":"507_CR33","doi-asserted-by":"publisher","first-page":"661","DOI":"10.1007\/s10586-017-0730-x","volume":"20","author":"V Zlomisli\u0107","year":"2017","unstructured":"Zlomisli\u0107 V, Fertalj K, Sruk V. Denial of service attacks, defenses, and research challenges. Cluster Comput. 2017;20:661\u201371. https:\/\/doi.org\/10.1007\/s10586-017-0730-x.","journal-title":"Cluster Comput"},{"key":"507_CR34","unstructured":"ARP spoofing\u2014Flaws in network security (2024) IONOS Digital Guide. https:\/\/www.ionos.com\/digitalguide\/server\/security\/arp-spoofing-attacks-from-the-internal-network\/. Accessed 5 Oct 2024"},{"key":"507_CR35","doi-asserted-by":"publisher","first-page":"2027","DOI":"10.1109\/COMST.2016.2548426","volume":"18","author":"M Conti","year":"2016","unstructured":"Conti M, Dragoni N, Lesyk V. A survey of man-in-the-middle attacks. IEEE Commun Surv Tutor. 2016;18:2027\u201351. https:\/\/doi.org\/10.1109\/COMST.2016.2548426.","journal-title":"IEEE Commun Surv Tutor"},{"key":"507_CR36","unstructured":"What is a port scan? How to prevent port scan attacks? (2024) Fortinet. https:\/\/www.fortinet.com\/resources\/cyberglossary\/what-is-port-scan. Accessed 5 Oct 2024"},{"key":"507_CR37","unstructured":"Buckbee M. What is a port scanner and how does it work? Varonis, 2024. https:\/\/www.varonis.com\/blog\/port-scanning-techniques. Accessed 5 Oct 2024"},{"key":"507_CR38","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1109\/OJIA.2024.3365576","volume":"5","author":"M Usama","year":"2024","unstructured":"Usama M, Aman MN. Command injection attacks in smart grids: a survey. IEEE Open J Ind Appl. 2024;5:75\u201385. https:\/\/doi.org\/10.1109\/OJIA.2024.3365576.","journal-title":"IEEE Open J Ind Appl"},{"key":"507_CR39","doi-asserted-by":"publisher","unstructured":"Moustafa N, Slay J. UNSW-NB15: A comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set). In: 2015 Military Communications and Information Systems Conference (MilCIS), 2015. https:\/\/doi.org\/10.1109\/MilCIS.2015.7348942","DOI":"10.1109\/MilCIS.2015.7348942"},{"key":"507_CR40","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1080\/19393555.2015.1125974","volume":"25","author":"N Moustafa","year":"2016","unstructured":"Moustafa N, Slay J. The evaluation of network anomaly detection systems: statistical analysis of the UNSW-NB15 data set and the comparison with the KDD99 data set. Inf Secur J Glob Perspect. 2016;25:18\u201331. https:\/\/doi.org\/10.1080\/19393555.2015.1125974.","journal-title":"Inf Secur J Glob Perspect"},{"key":"507_CR41","doi-asserted-by":"crossref","unstructured":"Ullah I, Mahmoud QH. A scheme for generating a dataset for anomalous activity detection in IoT networks. In: Lecture Notes in Computer Science, 2020 pp 508\u2013520.","DOI":"10.1007\/978-3-030-47358-7_52"},{"key":"507_CR42","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1016\/j.gltp.2022.04.020","volume":"3","author":"K Maharana","year":"2022","unstructured":"Maharana K, Mondal S, Nemade B. A review: data pre-processing and data augmentation techniques. Glob Transit Proc. 2022;3:91\u20139. https:\/\/doi.org\/10.1016\/j.gltp.2022.04.020.","journal-title":"Glob Transit Proc"},{"key":"507_CR43","doi-asserted-by":"publisher","first-page":"1477","DOI":"10.1007\/s10994-018-5724-2","volume":"107","author":"P Cerda","year":"2018","unstructured":"Cerda P, Varoquaux G, K\u00e9gl B. Similarity encoding for learning with dirty categorical variables. Mach Learn. 2018;107:1477\u201394. https:\/\/doi.org\/10.1007\/s10994-018-5724-2.","journal-title":"Mach Learn"},{"key":"507_CR44","doi-asserted-by":"publisher","first-page":"83","DOI":"10.37661\/1816-0301-2021-18-3-83-96","volume":"18","author":"VV Starovoitov","year":"2021","unstructured":"Starovoitov VV, Golub YI. Data normalization in machine learning. Informatics. 2021;18:83\u201396. https:\/\/doi.org\/10.37661\/1816-0301-2021-18-3-83-96.","journal-title":"Informatics"},{"key":"507_CR45","first-page":"25","volume":"30","author":"S Kotsiantis","year":"2006","unstructured":"Kotsiantis S, Kanellopoulos D, Pintelas P. Handling imbalanced datasets: a review. GESTS Int Trans Comput Sci Eng. 2006;30:25\u201336.","journal-title":"GESTS Int Trans Comput Sci Eng"},{"key":"507_CR46","doi-asserted-by":"publisher","unstructured":"Log-transformation and its implications for data analysis (2014) Shanghai Arch Psychiatry. https:\/\/doi.org\/10.3969\/j.issn.1002-0829.2014.02.009. Accessed 2 Jul 2024.","DOI":"10.3969\/j.issn.1002-0829.2014.02.009"},{"key":"507_CR47","unstructured":"Brown G. A new perspective for information theoretic feature selection. In: Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, Proceedings of Machine Learning Research, 2009; 5: 49\u201356."},{"key":"507_CR48","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2020\/8856707","volume":"2020","author":"H Fang","year":"2020","unstructured":"Fang H, Tang P, Si H. Feature selections using minimal redundancy maximal relevance algorithm for human activity recognition in smart home environments. J Healthc Eng. 2020;2020:1\u201313. https:\/\/doi.org\/10.1155\/2020\/8856707.","journal-title":"J Healthc Eng"},{"key":"507_CR49","doi-asserted-by":"publisher","unstructured":"Zhao Z, Anand R, Wang M. Maximum relevance and minimum redundancy feature selection methods for a marketing machine learning platform. In: 2019 IEEE International Conference on Data Science and Advanced Analytics (DSAA), 2019; pp 59\u201365. https:\/\/doi.org\/10.1109\/DSAA.2019.00059","DOI":"10.1109\/DSAA.2019.00059"},{"key":"507_CR50","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2024","author":"A Houkan","year":"2024","unstructured":"Houkan A, et al. Enhancing security in industrial IoT networks: machine learning solutions for feature selection and reduction. IEEE Access. 2024. https:\/\/doi.org\/10.1109\/ACCESS.2024.","journal-title":"IEEE Access"},{"key":"507_CR51","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1186\/s12911-019-1004-8","volume":"19","author":"S Uddin","year":"2019","unstructured":"Uddin S, Khan A, Hossain ME, Moni MA. Comparing different supervised machine learning algorithms for disease prediction. BMC Med Inform Decis Mak. 2019;19:281. https:\/\/doi.org\/10.1186\/s12911-019-1004-8.","journal-title":"BMC Med Inform Decis Mak"},{"key":"507_CR52","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1016\/j.aej.2024.05.111","volume":"103","author":"P Jain","year":"2024","unstructured":"Jain P, Islam MT, Alshammari AS. Comparative analysis of machine learning techniques for metamaterial absorber performance in terahertz applications. Alexandria Eng J. 2024;103:51\u20139. https:\/\/doi.org\/10.1016\/j.aej.2024.05.111.","journal-title":"Alexandria Eng J"},{"key":"507_CR53","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2024.3447716","author":"T Pattnaik","year":"2024","unstructured":"Pattnaik T, et al. An efficient low complex-functional link artificial neural network based framework for uneven light image thresholding. IEEE Access. 2024. https:\/\/doi.org\/10.1109\/ACCESS.2024.3447716.","journal-title":"IEEE Access"},{"key":"507_CR54","doi-asserted-by":"publisher","DOI":"10.1007\/s13042-024-02142-2","author":"P Jain","year":"2024","unstructured":"Jain P, Yedukondalu J, Chhabra H, Chauhan U, Sharma LD. EEG-based detection of cognitive load using VMD and LightGBM classifier. Int J Mach Learn Cybern. 2024. https:\/\/doi.org\/10.1007\/s13042-024-02142-2.","journal-title":"Int J Mach Learn Cybern"}],"container-title":["Discover Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44163-025-00507-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44163-025-00507-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44163-025-00507-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,4]],"date-time":"2025-11-04T10:40:40Z","timestamp":1762252840000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44163-025-00507-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,4]]},"references-count":54,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["507"],"URL":"https:\/\/doi.org\/10.1007\/s44163-025-00507-2","relation":{},"ISSN":["2731-0809"],"issn-type":[{"value":"2731-0809","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,4]]},"assertion":[{"value":"18 June 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 August 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 November 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"The authors give their consent for publication.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"307"}}