{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T10:29:06Z","timestamp":1786616946268,"version":"build-2736575974"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T00:00:00Z","timestamp":1783296000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T00:00:00Z","timestamp":1786579200000},"content-version":"vor","delay-in-days":38,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100004242","name":"Princess Nourah Bint Abdulrahman University","doi-asserted-by":"publisher","award":["PNURSP2026R235"],"award-info":[{"award-number":["PNURSP2026R235"]}],"id":[{"id":"10.13039\/501100004242","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Cyber deception in Industrial Internet of Things (IIoT) environments faces a fundamental challenge, insufficient deception investment fails to trap attackers, while excessive deployment causes rational adversaries to accumulate skepticism, progressively neutralizing deceptive mechanisms. To the best of our knowledge, no existing reinforcement learning (RL) framework has jointly characterized the optimal deception investment level under these dynamics and trained against a co-evolutionary adversary that explicitly models skepticism accumulation. This paper introduces Co-ADAM, a Co-evolutionary Adaptive Deception-Aware Multi-mechanism defense framework that formalizes IIoT cyber deception as a constrained Markov decision process (CMDP) with an embedded signaling game. We establish that the co-evolutionary training will reach some equilibrium of skepticism (Lemma 1, Propositions 1, 2) and characterize the resulting joint policy as an\n                    <jats:italic>\u03b5<\/jats:italic>\n                    -Nash deception equilibrium with exploitability\n                    <jats:italic>\u03b5<\/jats:italic>\n                    \u2009=\u20092.4523 (4.43% normalized). A deep Q-network (DQN) defender is trained against an adaptive attacker pool of size\n                    <jats:italic>K<\/jats:italic>\n                    \u2009=\u200910 across 5000 episodes, with skepticism dynamics, attacker momentum, and budget constraints embedded directly in the environment. Compared to seven baselines over three IIoT datasets, CIC-IIoT 2025, WUSTL-IIoT 2021 and Edge-IIoTset, Co-ADAM attains an Attack Mitigation Rate (AMR) of 0.8363 under random and 0.8033 under adaptive attackers, with cumulative rewards of 147.91 and 102.52 respectively, all differences are statistically significant at\n                    <jats:italic>p<\/jats:italic>\n                    \u2009&lt;\u20090.001. The learned policy transfers across heterogeneous IIoT environments with 99.61% mean AMR retention, scales to 1000 devices with less than 0.26% AMR degradation, and converges empirically at episode 1377. The most significant element of the architecture confirmed in ablation as adversarial skepticism (Cohen\u2019s\n                    <jats:italic>d<\/jats:italic>\n                    \u2009=\u20093.57), forms the foundation of principled skepticism modeling as a critical component of deception-based IIoT defense. Physical validation on a 10-day IIoT testbed comprising a Centerm D660 honeypot, three ESP32 sensors, and a Kali Linux attack machine confirms simulation findings with testbed AMR of 0.9975 exceeding simulation AMR of 0.8363.\n                  <\/jats:p>","DOI":"10.1038\/s41598-026-60920-0","type":"journal-article","created":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T05:15:28Z","timestamp":1783314928000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Co-ADAM: a co-evolutionary signaling game framework for equilibrium cyber deception in industrial IoT"],"prefix":"10.1038","volume":"16","author":[{"given":"Usman","family":"Wushishi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nasir","family":"Hussain","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Altaf","family":"Hussain","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amal","family":"Al-Rasheed","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zahid","family":"Ullah","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,6]]},"reference":[{"key":"60920_CR1","doi-asserted-by":"publisher","DOI":"10.1145\/3635030","author":"VR Kebande","year":"2024","unstructured":"Kebande, V. R. & Awad, A. I. Industrial internet of things ecosystems security and digital forensics: Achievements, open challenges, and future directions. ACM Comput. Surv. https:\/\/doi.org\/10.1145\/3635030 (2024).","journal-title":"ACM Comput. Surv."},{"key":"60920_CR2","doi-asserted-by":"publisher","first-page":"625","DOI":"10.1016\/j.procs.2025.10.258","volume":"272","author":"A Amirova","year":"2025","unstructured":"Amirova, A., Aldasheva, L. & Abdiraman, A. Securing the industrial internet of things: Threats, vulnerabilities, and recommendations. Procedia Comput. Sci. 272, 625\u2013630. https:\/\/doi.org\/10.1016\/j.procs.2025.10.258 (2025).","journal-title":"Procedia Comput. Sci."},{"key":"60920_CR3","doi-asserted-by":"publisher","DOI":"10.1145\/3757734","author":"S Khan","year":"2025","unstructured":"Khan, S., Lopes Martins, P. A. F., Sousa, B. & Pereira, V. A comprehensive review on lightweight cryptographic mechanisms for industrial internet of things systems. ACM Comput. Surv. https:\/\/doi.org\/10.1145\/3757734 (2025).","journal-title":"ACM Comput. Surv."},{"key":"60920_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2025.104458","volume":"154","author":"A Sezgin","year":"2025","unstructured":"Sezgin, A. & Boyac\u0131, A. DecoyPot: A large language model-driven web API honeypot for realistic attacker engagement. Comput. Secur. 154, 104458. https:\/\/doi.org\/10.1016\/j.cose.2025.104458 (2025).","journal-title":"Comput. Secur."},{"key":"60920_CR5","doi-asserted-by":"publisher","unstructured":"Sayed, M. A., Rahman, M., Khan, M. A. I., & Tosh, D. A survey of network requirements for enabling effective cyber deception. arXiv preprint arXiv:2309.00184 (2024). https:\/\/doi.org\/10.48550\/arXiv.2309.00184.","DOI":"10.48550\/arXiv.2309.00184"},{"key":"60920_CR6","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1007\/978-3-031-50670-3_10","volume-title":"Decision and Game Theory for Security","author":"MA Sayed","year":"2023","unstructured":"Sayed, M. A., Anwar, A. H., Kiekintveld, C. & Kamhoua, C. Honeypot allocation for cyber deception in dynamic tactical networks: A game theoretic approach. In Decision and Game Theory for Security (eds Fu, J. et al.) 195\u2013214 (Springer Nature Switzerland, 2023). https:\/\/doi.org\/10.1007\/978-3-031-50670-3_10."},{"issue":"4","key":"60920_CR7","doi-asserted-by":"publisher","first-page":"2420","DOI":"10.1109\/COMST.2024.3484491","volume":"27","author":"H Kheddar","year":"2025","unstructured":"Kheddar, H., Dawoud, D. W., Awad, A. I., Himeur, Y. & Khan, M. K. Reinforcement-learning-based intrusion detection in communication networks: A review. IEEE Commun. Surv. Tutor. 27(4), 2420\u20132469. https:\/\/doi.org\/10.1109\/COMST.2024.3484491 (2025).","journal-title":"IEEE Commun. Surv. Tutor."},{"issue":"1","key":"60920_CR8","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-025-33426-4","volume":"16","author":"U Wushishi","year":"2025","unstructured":"Wushishi, U. et al. D3O-IIoT: Deep reinforcement learning-driven dynamic deception orchestration for industrial IoT security. Sci. Rep. 16(1), 2389. https:\/\/doi.org\/10.1038\/s41598-025-33426-4 (2025).","journal-title":"Sci. Rep."},{"issue":"2","key":"60920_CR9","doi-asserted-by":"publisher","first-page":"856","DOI":"10.1109\/TCST.2022.3207671","volume":"31","author":"P Shukla","year":"2023","unstructured":"Shukla, P., An, L., Chakrabortty, A. & Duel-Hallen, A. A robust Stackelberg game for cyber-security investment in networked control systems. IEEE Trans. Control Syst. Technol. 31(2), 856\u2013871. https:\/\/doi.org\/10.1109\/TCST.2022.3207671 (2023).","journal-title":"IEEE Trans. Control Syst. Technol."},{"key":"60920_CR10","doi-asserted-by":"publisher","DOI":"10.1007\/s44443-025-00043-0","author":"D Park","year":"2025","unstructured":"Park, D. & Dagher, G. G. A non-cooperative non-zero-sum game theory model for cyber-attack prevention. Discov. Comput. https:\/\/doi.org\/10.1007\/s44443-025-00043-0 (2025).","journal-title":"Discov. Comput."},{"key":"60920_CR11","doi-asserted-by":"publisher","first-page":"171559","DOI":"10.1109\/ACCESS.2024.3478808","volume":"12","author":"PC Kamdem","year":"2024","unstructured":"Kamdem, P. C., Zemkoho, A. B., Njilla, L., Nkenlifack, M. & Kamhoua, C. A. Two-layer deception model based on signaling games against cyber attacks on cyber-physical systems. IEEE Access 12, 171559\u2013171570. https:\/\/doi.org\/10.1109\/ACCESS.2024.3478808 (2024).","journal-title":"IEEE Access"},{"key":"60920_CR12","doi-asserted-by":"publisher","DOI":"10.1145\/3785665","author":"Z Huma","year":"2026","unstructured":"Huma, Z., Jan, S. U., Ahmad, J., Buchanan, W. & Pitropakis, N. Adversarial machine learning in IoT security: A comprehensive survey. ACM Comput. Surv. https:\/\/doi.org\/10.1145\/3785665 (2026).","journal-title":"ACM Comput. Surv."},{"key":"60920_CR13","doi-asserted-by":"publisher","unstructured":"Bashir, A., Shamszaman, Z. U., Song, Z. & Han, T. A. Co-evolutionary dynamics of attack and defence in cybersecurity, arXiv preprint arXiv:2505.19338 (2025). https:\/\/doi.org\/10.48550\/arXiv.2505.19338.","DOI":"10.48550\/arXiv.2505.19338"},{"key":"60920_CR14","doi-asserted-by":"publisher","first-page":"89363","DOI":"10.1109\/ACCESS.2024.3419068","volume":"12","author":"M Lyu","year":"2024","unstructured":"Lyu, M., Habibi Gharakheili, H. & Sivaraman, V. A survey on enterprise network security: Asset behavioral monitoring and distributed attack detection. IEEE Access 12, 89363\u201389383. https:\/\/doi.org\/10.1109\/ACCESS.2024.3419068 (2024).","journal-title":"IEEE Access"},{"key":"60920_CR15","doi-asserted-by":"publisher","first-page":"455","DOI":"10.1109\/OAJPE.2025.3577012","volume":"12","author":"Y Chen","year":"2025","unstructured":"Chen, Y., Konstantinou, C., Mashima, D., Srivastava, A. K. & Lakshminarayana, S. Survey of moving target defense in power grids: Design principles, tradeoffs, and future directions. IEEE Open Access J. Power Energy 12, 455\u2013469. https:\/\/doi.org\/10.1109\/OAJPE.2025.3577012 (2025).","journal-title":"IEEE Open Access J. Power Energy"},{"key":"60920_CR16","doi-asserted-by":"publisher","first-page":"32252","DOI":"10.1109\/ACCESS.2026.3668537","volume":"14","author":"L Souto","year":"2026","unstructured":"Souto, L. et al. Moving target defense in software-defined networks: A systematic review. IEEE Access 14, 32252\u201332272. https:\/\/doi.org\/10.1109\/ACCESS.2026.3668537 (2026).","journal-title":"IEEE Access"},{"key":"60920_CR17","doi-asserted-by":"publisher","unstructured":"Huang, R. & Ma, R. Research on IIoT honeypot technology based on improved Conpot. In: Proceedings of the 2024 3rd International Conference on Artificial Intelligence and Education, ICAIE \u201924, Association for Computing Machinery, New York, NY, USA, 2025, pp. 927\u2013931. https:\/\/doi.org\/10.1145\/3722237.3722396.","DOI":"10.1145\/3722237.3722396"},{"key":"60920_CR18","doi-asserted-by":"publisher","DOI":"10.1145\/3785138","author":"H-T Yang","year":"2025","unstructured":"Yang, H.-T. et al. Patrol security game: Defending against adversary with freedom in attack timing, location, and duration. ACM Trans. Cyber-Phys. Syst. https:\/\/doi.org\/10.1145\/3785138 (2025).","journal-title":"ACM Trans. Cyber-Phys. Syst."},{"issue":"13","key":"60920_CR19","doi-asserted-by":"publisher","first-page":"25457","DOI":"10.1109\/JIOT.2025.3559080","volume":"12","author":"W Cheng","year":"2025","unstructured":"Cheng, W., Wei, W., Liu, X. & Huan, X. Joint deep reinforcement learning and Stackelberg game with intervention forheterogeneous edge computing in industrial internet of things. IEEE Internet Things J. 12(13), 25457\u201325471. https:\/\/doi.org\/10.1109\/JIOT.2025.3559080 (2025).","journal-title":"IEEE Internet Things J."},{"key":"60920_CR20","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1016\/B978-0-12-214850-7.50025-5","volume-title":"Uncertainty in Economics","author":"M Spence","year":"1978","unstructured":"Spence, M. Job market signaling. In Uncertainty in Economics (eds Diamond, P. & Rothschild, M.) 281\u2013306 (Academic Press, 1978). https:\/\/doi.org\/10.1016\/B978-0-12-214850-7.50025-5."},{"key":"60920_CR21","doi-asserted-by":"publisher","first-page":"5219","DOI":"10.1109\/ACCESS.2024.3525064","volume":"13","author":"IC Kombou Sihomnou","year":"2025","unstructured":"Kombou Sihomnou, I. C., Benslimane, A., Anwar, A. H., Deugoue, G. & Kamhoua, C. A. Cyber deception against battery drain DoS attacks in wireless sensor networks using signaling game. IEEE Access 13, 5219\u20135235. https:\/\/doi.org\/10.1109\/ACCESS.2024.3525064 (2025).","journal-title":"IEEE Access"},{"key":"60920_CR22","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1007\/978-3-031-53510-9_2","volume-title":"Network Security Empowered by Artificial Intelligence","author":"T Li","year":"2024","unstructured":"Li, T., Pan, Y. & Zhu, Q. Decision-dominant strategic defense against lateral movement for 5G zero-trust multi-domain networks. In Network Security Empowered by Artificial Intelligence (eds Chen, Y. et al.) 25\u201376 (Springer Nature Switzerland, 2024). https:\/\/doi.org\/10.1007\/978-3-031-53510-9_2."},{"key":"60920_CR23","doi-asserted-by":"publisher","first-page":"63381","DOI":"10.1109\/ACCESS.2024.3390722","volume":"12","author":"F Mesadieu","year":"2024","unstructured":"Mesadieu, F., Torre, D. & Chennamaneni, A. Leveraging deep reinforcement learning technique for intrusion detection in SCADA infrastructure. IEEE Access 12, 63381\u201363399. https:\/\/doi.org\/10.1109\/ACCESS.2024.3390722 (2024).","journal-title":"IEEE Access"},{"key":"60920_CR24","doi-asserted-by":"publisher","first-page":"2134","DOI":"10.1109\/ACCESS.2025.3650335","volume":"14","author":"M Sepczuk","year":"2026","unstructured":"Sepczuk, M. & Sosnowski, K. Using the reinforcement learning agent to test the correctness of rule configuration in web application firewalls. IEEE Access 14, 2134\u20132151. https:\/\/doi.org\/10.1109\/ACCESS.2025.3650335 (2026).","journal-title":"IEEE Access"},{"key":"60920_CR25","doi-asserted-by":"publisher","unstructured":"Ahmad, T., Butkovic, M. & Truscan, D. Using reinforcement learning for security testing: A systematic mapping study. In: 2025 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW), 2025, pp. 208\u2013216. https:\/\/doi.org\/10.1109\/ICSTW64639.2025.10962455.","DOI":"10.1109\/ICSTW64639.2025.10962455"},{"key":"60920_CR26","doi-asserted-by":"publisher","first-page":"151444","DOI":"10.1109\/ACCESS.2024.3477415","volume":"12","author":"F Sangoleye","year":"2024","unstructured":"Sangoleye, F., Johnson, J. & Tsiropoulou, E. E. Intrusion detection in industrial control systems based on deep reinforcement learning. IEEE Access 12, 151444\u2013151459. https:\/\/doi.org\/10.1109\/ACCESS.2024.3477415 (2024).","journal-title":"IEEE Access"},{"issue":"6","key":"60920_CR27","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1007\/s12083-025-02151-0","volume":"18","author":"J Alotaibi","year":"2025","unstructured":"Alotaibi, J. AI-driven intrusion detection and mitigation framework for software-defined IoT networks. Peer-to-Peer Netw. Appl. 18(6), 321. https:\/\/doi.org\/10.1007\/s12083-025-02151-0 (2025).","journal-title":"Peer-to-Peer Netw. Appl."},{"key":"60920_CR28","doi-asserted-by":"publisher","first-page":"99439","DOI":"10.1109\/ACCESS.2024.3430037","volume":"12","author":"A Siddiqua","year":"2024","unstructured":"Siddiqua, A., Liu, S., Nipu, A. S., Harris, A. & Liu, Y. Co-evolving multi-agent transfer reinforcement learning via scenario independent representation. IEEE Access 12, 99439\u201399451. https:\/\/doi.org\/10.1109\/ACCESS.2024.3430037 (2024).","journal-title":"IEEE Access"},{"issue":"10","key":"60920_CR29","doi-asserted-by":"publisher","first-page":"8563","DOI":"10.1109\/TPAMI.2025.3579895","volume":"47","author":"B Pi","year":"2025","unstructured":"Pi, B., Deng, L.-J., Feng, M., Perc, M. & Kurths, J. Dynamic evolution of complex networks: A reinforcement learning approach applying evolutionary games to community structure. IEEE Trans. Pattern Anal. Mach. Intell. 47(10), 8563\u20138582. https:\/\/doi.org\/10.1109\/TPAMI.2025.3579895 (2025).","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"6","key":"60920_CR30","doi-asserted-by":"publisher","first-page":"9864","DOI":"10.1109\/TVT.2025.3539382","volume":"74","author":"C Tang","year":"2025","unstructured":"Tang, C., Pan, L., Chen, J., Liu, Y. & Lai, J. A game theory-reinforcement learning approach to cooperation for UAVs. IEEE Trans. Veh. Technol. 74(6), 9864\u20139869. https:\/\/doi.org\/10.1109\/TVT.2025.3539382 (2025).","journal-title":"IEEE Trans. Veh. Technol."},{"key":"60920_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.apm.2025.116313","volume":"149","author":"X Li","year":"2026","unstructured":"Li, X., Pi, B., Deng, L.-J. & Li, Q. Memory-driven q-learning model for cooperation in snowdrift game with dynamic behavioral types. Appl. Math. Model. 149, 116313. https:\/\/doi.org\/10.1016\/j.apm.2025.116313 (2026).","journal-title":"Appl. Math. Model."},{"key":"60920_CR32","doi-asserted-by":"publisher","unstructured":"Chen, K., Nguyen, T., Sahu, A. & Hassanaly, M. Adversarial multi-agent reinforcement learning for proactive false data injection detection (2026). arXiv:2411.12130. https:\/\/doi.org\/10.48550\/arXiv.2411.12130.","DOI":"10.48550\/arXiv.2411.12130"},{"key":"60920_CR33","unstructured":"Ma, W., Chang, Y.-C., Shen, Y., Zhao, Z. & Lin, C.-t. Robust multi-agent reinforcement learning with diverse adversarial agent generation and contrastive policy encoding (2026)."},{"key":"60920_CR34","unstructured":"Strom, B. E., Applebaum, A., Miller, D. P., Nickels, K. C., Pennington, A. G., Thomas, C. B. MITRE ATT&CK.: Design and philosophy, Tech. rep., The MITRE Corporation, originally published July 2018; revised March 2020 (Mar. 2020). https:\/\/attack.mitre.org\/docs\/ATTACK_Design_and_Philosophy_March_2020.pdf"},{"key":"60920_CR35","volume-title":"Introductory Functional Analysis with Applications","author":"E Kreyszig","year":"1978","unstructured":"Kreyszig, E. Introductory Functional Analysis with Applications (John Wiley & Sons, 1978)."},{"issue":"7540","key":"60920_CR36","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1038\/nature14236","volume":"518","author":"V Mnih","year":"2015","unstructured":"Mnih, V. et al. Human-level control through deep reinforcement learning. Nature 518(7540), 529\u2013533. https:\/\/doi.org\/10.1038\/nature14236 (2015).","journal-title":"Nature"},{"key":"60920_CR37","unstructured":"D. P. Kingma, J. Ba, Adam: A method for stochastic optimization (2017). arXiv:1412.6980."},{"issue":"20","key":"60920_CR38","doi-asserted-by":"publisher","first-page":"4095","DOI":"10.3390\/electronics14204095","volume":"14","author":"A Firouzi","year":"2025","unstructured":"Firouzi, A., Dadkhah, S., Maret, S. A. & Ghorbani, A. A. DataSense: A real-time sensor-based benchmark dataset for attack analysis in IIoT with multi-objective feature selection. Electronics 14(20), 4095. https:\/\/doi.org\/10.3390\/electronics14204095 (2025).","journal-title":"Electronics"},{"key":"60920_CR39","unstructured":"Zolanvari, M., Teixeira, M. A., Gupta, L., Khan, K. M. & Jain, R. WUSTL-IIOT-2021 dataset for IIoT cybersecurity research. Technical Report, Washington University in St. Louis (2021)."},{"key":"60920_CR40","doi-asserted-by":"publisher","first-page":"40281","DOI":"10.1109\/ACCESS.2022.3165809","volume":"10","author":"MA Ferrag","year":"2022","unstructured":"Ferrag, M. A., Friha, O., Hamouda, D., Maglaras, L. & Janicke, H. Edge-IIoTset: A new comprehensive realistic cyber security dataset of IoT and IIoT applications for centralized and federated learning. IEEE Access 10, 40281\u201340306. https:\/\/doi.org\/10.1109\/ACCESS.2022.3165809 (2022).","journal-title":"IEEE Access"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-60920-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-60920-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-60920-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T09:41:25Z","timestamp":1786614085000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-60920-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,6]]},"references-count":40,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["60920"],"URL":"https:\/\/doi.org\/10.1038\/s41598-026-60920-0","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,6]]},"assertion":[{"value":"30 March 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 June 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 July 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"25306"}}