{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T15:56:20Z","timestamp":1774454180453,"version":"3.50.1"},"reference-count":53,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2025,1,29]],"date-time":"2025-01-29T00:00:00Z","timestamp":1738108800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"EU project NGI Trustchain","award":["101093274"],"award-info":[{"award-number":["101093274"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JSAN"],"abstract":"<jats:p>In an era of increasing reliance on digital health solutions, safeguarding user privacy has emerged as a paramount concern. Health applications often need to balance advanced AI functionalities with sufficient privacy measures to ensure user engagement. This paper presents the architecture of FLORA, a privacy-first ovulation-tracking application that leverages federated learning (FL), privacy-enhancing technologies (PETs), and blockchain to protect user data while delivering accurate and personalized health insights. Unlike conventional centralized systems, FLORA ensures that sensitive information remains on users\u2019 devices, with predictive algorithms powered by local computations. Blockchain technology provides immutable consent tracking and model update transparency, further improving user trust. In addition, FLORA\u2019s design incentivizes participation through a token-based reward system, fostering collaborative data contributions. This work illustrates how the integration of cutting-edge technologies creates a secure, scalable, and user-centric health application, setting a new standard for privacy-preserving digital health platforms.<\/jats:p>","DOI":"10.3390\/jsan14010011","type":"journal-article","created":{"date-parts":[[2025,1,29]],"date-time":"2025-01-29T03:26:10Z","timestamp":1738121170000},"page":"11","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Federated Learning for privacy-Friendly Health Apps: A Case Study on Ovulation Tracking"],"prefix":"10.3390","volume":"14","author":[{"given":"Nikolaos","family":"Pavlidis","sequence":"first","affiliation":[{"name":"Institute for Language and Speech Processing, Athena Research Center, 67100 Xanthi, Greece"},{"name":"Department of Electrical and Computer Engineering, Democritus University of Thrace, Kimmeria, 67100 Xanthi, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6337-4320","authenticated-orcid":false,"given":"Andreas","family":"Sendros","sequence":"additional","affiliation":[{"name":"Institute for Language and Speech Processing, Athena Research Center, 67100 Xanthi, Greece"},{"name":"Department of Electrical and Computer Engineering, Democritus University of Thrace, Kimmeria, 67100 Xanthi, Greece"}]},{"given":"Theodoros","family":"Tsiolakis","sequence":"additional","affiliation":[{"name":"Institute for Language and Speech Processing, Athena Research Center, 67100 Xanthi, Greece"},{"name":"Department of Electrical and Computer Engineering, Democritus University of Thrace, Kimmeria, 67100 Xanthi, Greece"}]},{"given":"Periklis","family":"Kostamis","sequence":"additional","affiliation":[{"name":"Institute for Language and Speech Processing, Athena Research Center, 67100 Xanthi, Greece"},{"name":"Department of Electrical and Computer Engineering, Democritus University of Thrace, Kimmeria, 67100 Xanthi, Greece"}]},{"given":"Christos","family":"Karasoulas","sequence":"additional","affiliation":[{"name":"Institute for Language and Speech Processing, Athena Research Center, 67100 Xanthi, Greece"},{"name":"Department of Electrical and Computer Engineering, Democritus University of Thrace, Kimmeria, 67100 Xanthi, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0275-2009","authenticated-orcid":false,"given":"Eleni","family":"Briola","sequence":"additional","affiliation":[{"name":"Institute for Language and Speech Processing, Athena Research Center, 67100 Xanthi, Greece"},{"name":"Department of Electrical and Computer Engineering, Democritus University of Thrace, Kimmeria, 67100 Xanthi, Greece"}]},{"given":"Christos Chrysanthos","family":"Nikolaidis","sequence":"additional","affiliation":[{"name":"Institute for Language and Speech Processing, Athena Research Center, 67100 Xanthi, Greece"},{"name":"Department of Electrical and Computer Engineering, Democritus University of Thrace, Kimmeria, 67100 Xanthi, Greece"}]},{"given":"Vasilis","family":"Perifanis","sequence":"additional","affiliation":[{"name":"Institute for Language and Speech Processing, Athena Research Center, 67100 Xanthi, Greece"},{"name":"Department of Electrical and Computer Engineering, Democritus University of Thrace, Kimmeria, 67100 Xanthi, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8130-5775","authenticated-orcid":false,"given":"George","family":"Drosatos","sequence":"additional","affiliation":[{"name":"Institute for Language and Speech Processing, Athena Research Center, 67100 Xanthi, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6215-2574","authenticated-orcid":false,"given":"Eleftheria","family":"Katsiri","sequence":"additional","affiliation":[{"name":"Institute for Language and Speech Processing, Athena Research Center, 67100 Xanthi, Greece"},{"name":"Department of Electrical and Computer Engineering, Democritus University of Thrace, Kimmeria, 67100 Xanthi, Greece"}]},{"given":"Despoina Elisavet","family":"Filippidou","sequence":"additional","affiliation":[{"name":"OPSIS Research, Strada Corbita 30, Parter, Sector 5, 51083 Bucharest, Romania"}]},{"given":"Anastasios","family":"Manos","sequence":"additional","affiliation":[{"name":"OPSIS Research, Strada Corbita 30, Parter, Sector 5, 51083 Bucharest, Romania"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3749-0165","authenticated-orcid":false,"given":"Pavlos S.","family":"Efraimidis","sequence":"additional","affiliation":[{"name":"Institute for Language and Speech Processing, Athena Research Center, 67100 Xanthi, Greece"},{"name":"Department of Electrical and Computer Engineering, Democritus University of Thrace, Kimmeria, 67100 Xanthi, Greece"}]}],"member":"1968","published-online":{"date-parts":[[2025,1,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e12286","DOI":"10.2196\/12286","article-title":"Applications of machine learning in real-life digital health interventions: Review of the literature","volume":"21","author":"Triantafyllidis","year":"2019","journal-title":"J. Med. Internet Res."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1587","DOI":"10.1080\/03007995.2018.1475348","article-title":"Can apps and calendar methods predict ovulation with accuracy?","volume":"34","author":"Johnson","year":"2018","journal-title":"Curr. Med. Res. Opin."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"491","DOI":"10.2478\/popets-2020-0083","article-title":"How private is your period?: A systematic analysis of menstrual app privacy policies","volume":"4","author":"Shipp","year":"2020","journal-title":"Proc. Priv. Enhancing Technol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/j.ijlp.2019.04.002","article-title":"How private is your mental health app data? An empirical study of mental health app privacy policies and practices","volume":"64","author":"Parker","year":"2019","journal-title":"Int. J. Law Psychiatry"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Cao, J., Laabadli, H., Mathis, C.H., Stern, R.D., and Emami-Naeini, P. (2024, January 11\u201316). \u201cI Deleted It After the Overturn of Roe v. Wade\u201d: Understanding Women\u2019s Privacy Concerns Toward Period-Tracking Apps in the Post Roe v. Wade Era. Proceedings of the CHI Conference on Human Factors in Computing Systems, Honolulu, HI, USA.","DOI":"10.1145\/3613904.3642042"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Malki, L.M., Kaleva, I., Patel, D., Warner, M., and Abu-Salma, R. (2024, January 11\u201316). Exploring Privacy Practices of Female mHealth Apps in a Post-Roe World. Proceedings of the CHI Conference on Human Factors in Computing Systems, Honolulu, HI, USA.","DOI":"10.1145\/3613904.3642521"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1136\/bmjsrh-2019-200488","article-title":"Use of menstruation and fertility app trackers: A scoping review of the evidence","volume":"47","author":"Earle","year":"2021","journal-title":"BMJ Sex. Reprod. Health"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Song, Q., Hernandez, R.H., Kou, Y., and Gui, X. (2024, January 11\u201316). \u201cOur Users\u2019 Privacy is Paramount to Us\u201d: A Discourse Analysis of How Period and Fertility Tracking App Companies Address the Roe v Wade Overturn. Proceedings of the CHI Conference on Human Factors in Computing Systems, Honolulu, HI, USA.","DOI":"10.1145\/3613904.3642384"},{"key":"ref_9","unstructured":"McMahan, B., Moore, E., Ramage, D., Hampson, S., and y Arcas, B.A. (2017, January 20\u201322). Communication-efficient learning of deep networks from decentralized data. Proceedings of the Artificial Intelligence and Statistics, PMLR, Ft., Lauderdale, FL, USA."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"12806","DOI":"10.1109\/JIOT.2021.3072611","article-title":"Federated learning meets blockchain in edge computing: Opportunities and challenges","volume":"8","author":"Nguyen","year":"2021","journal-title":"IEEE Internet Things J."},{"key":"ref_11","unstructured":"Gentry, C. (2009). A Fully Homomorphic Encryption Scheme, Stanford University."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Dwork, C. (2006, January 10\u201314). Differential privacy. Proceedings of the International Colloquium on Automata, Languages, and Programming, Venice, Italy.","DOI":"10.1007\/11787006_1"},{"key":"ref_13","unstructured":"Green, M., and Ateniese, G. (2007, January 5\u20138). Identity-based proxy re-encryption. Proceedings of the Applied Cryptography and Network Security: 5th International Conference, ACNS 2007, Zhuhai, China. Proceedings 5."},{"key":"ref_14","first-page":"7232","article-title":"Evaluating gradient inversion attacks and defenses in federated learning","volume":"34","author":"Huang","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Thakur, T., Kadam, S., Patil, N., and Achrekar, C. (2023). Machine Learning in Period, Fertility and Ovulation Tracking Application. TechRxiv.","DOI":"10.36227\/techrxiv.22041683.v1"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"e42164","DOI":"10.2196\/42164","article-title":"Evaluation of Menstrual Cycle Tracking Behaviors in the Ovulation and Menstruation Health Pilot Study: Cross-Sectional Study","volume":"25","author":"Adnan","year":"2023","journal-title":"J. Med. Internet Res."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Suman, S., Mukherjee, S., Selvan, M.P., Mary, V.A., Jancy, S., and Shyry, S.P. (2023, January 19\u201320). Menstrual Cycle Tracking Using Deep Learning. Proceedings of the 2023 3rd International Conference on Pervasive Computing and Social Networking (ICPCSN), Salem, India.","DOI":"10.1109\/ICPCSN58827.2023.00030"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Srivastava, D., Gupta, S., Kudavelly, S., Suryanarayana, V.K., and Ga, R. (2021, January 1\u20135). Unsupervised deep learning based longitudinal follicular growth tracking during IVF cycle using 3D transvaginal ultrasound in assisted reproduction. Proceedings of the 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Virtual.","DOI":"10.1109\/EMBC46164.2021.9630495"},{"key":"ref_19","unstructured":"Fehring, R.J., and Schneider, M. (2024, December 01). Randomized Comparison of Two Internet-Supported Methods of Natural Family Planning. Available online: https:\/\/epublications.marquette.edu\/data_nfp\/2\/."},{"key":"ref_20","unstructured":"Odirichukwu, J., Njoku, O., Odirichukwu, S.C., Ndigwe, C., Nwachukwu, D.C., Nnoruka, J.U., Okorie, I.C., Dimoji, C., Odii, J., and John, C.N. (2023). Improving Menstrual Cycle Prediction Accuracy using Advanced Machine Learning Model Methods, QTanalytics India."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic minority over-sampling technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J. Artif. Intell. Res."},{"key":"ref_22","unstructured":"He, H., Bai, Y., Garcia, E.A., and Li, S. (2008, January 1\u20138). ADASYN: Adaptive synthetic sampling approach for imbalanced learning. Proceedings of the 2008 IEEE International joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), Hong Kong, China."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"90792","DOI":"10.1109\/ACCESS.2022.3201876","article-title":"Adoption of federated learning for healthcare informatics: Emerging applications and future directions","volume":"10","author":"Patel","year":"2022","journal-title":"IEEE Access"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"8017489","DOI":"10.1155\/2023\/8017489","article-title":"Secure and efficient smart healthcare system based on federated learning","volume":"2023","author":"Liu","year":"2023","journal-title":"Int. J. Intell. Syst."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Nikolaidis, C.C., Perifanis, V., Pavlidis, N., and Efraimidis, P.S. (2023, January 18\u201320). Federated Learning for Early Dropout Prediction on Healthy Ageing Applications. Proceedings of the 2023 Eighth International Conference on Fog and Mobile Edge Computing (FMEC), Tartu, Estonia.","DOI":"10.1109\/FMEC59375.2023.10306129"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1007\/s10586-024-04850-4","article-title":"Advancing elderly social care dropout prediction with federated learning: Client selection and imbalanced data management","volume":"28","author":"Nikolaidis","year":"2024","journal-title":"Clust. Comput."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"108441","DOI":"10.1016\/j.knosys.2022.108441","article-title":"Federated neural collaborative filtering","volume":"242","author":"Perifanis","year":"2022","journal-title":"Knowl.-Based Syst."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"5880","DOI":"10.1002\/int.22818","article-title":"Privacy-preserving federated learning based on multi-key homomorphic encryption","volume":"37","author":"Ma","year":"2022","journal-title":"Int. J. Intell. Syst."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"767","DOI":"10.1016\/j.ins.2022.12.024","article-title":"FedPOIRec: Privacy-preserving federated poi recommendation with social influence","volume":"623","author":"Perifanis","year":"2023","journal-title":"Inf. Sci."},{"key":"ref_30","unstructured":"Cheon, J.H., Kim, A., Kim, M., and Song, Y. (2017, January 3\u20137). Homomorphic encryption for arithmetic of approximate numbers. Proceedings of the Advances in Cryptology\u2013ASIACRYPT 2017: 23rd International Conference on the Theory and Applications of Cryptology and Information Security, Hong Kong, China. Proceedings, Part I 23."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"4289","DOI":"10.1109\/JIOT.2023.3302065","article-title":"Secure federated learning with fully homomorphic encryption for iot communications","volume":"11","author":"Hijazi","year":"2023","journal-title":"IEEE Internet Things J."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Fereidooni, H., Marchal, S., Miettinen, M., Mirhoseini, A., M\u00f6llering, H., Nguyen, T.D., Rieger, P., Sadeghi, A.R., Schneider, T., and Yalame, H. (2021, January 27). SAFELearn: Secure aggregation for private federated learning. Proceedings of the 2021 IEEE Security and Privacy Workshops (SPW), San Francisco, CA, USA.","DOI":"10.1109\/SPW53761.2021.00017"},{"key":"ref_33","unstructured":"Zhang, C., Li, S., Xia, J., Wang, W., Yan, F., and Liu, Y. (2020, January 15\u201317). {BatchCrypt}: Efficient homomorphic encryption for {Cross-Silo} federated learning. Proceedings of the 2020 USENIX annual technical conference (USENIX ATC 20), Online."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Chen, H., Laine, K., and Player, R. (2017, January 7). Simple encrypted arithmetic library-SEAL v2. 1 . Proceedings of the Financial Cryptography and Data Security: FC 2017 International Workshops, WAHC, BITCOIN, VOTING, WTSC, and TA, Sliema, Malta. Revised Selected Papers 21.","DOI":"10.1007\/978-3-319-70278-0_1"},{"key":"ref_35","first-page":"103201","article-title":"CS-MIA: Membership inference attack based on prediction confidence series in federated learning","volume":"67","author":"Gu","year":"2022","journal-title":"J. Inf. Secur. Appl."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"22359","DOI":"10.1109\/ACCESS.2022.3151670","article-title":"Differential privacy for deep and federated learning: A survey","volume":"10","author":"Abdelhadi","year":"2022","journal-title":"IEEE Access"},{"key":"ref_37","first-page":"72181","article-title":"Dynamic personalized federated learning with adaptive differential privacy","volume":"36","author":"Yang","year":"2023","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"4488","DOI":"10.1109\/TIFS.2023.3293417","article-title":"Personalized federated learning with differential privacy and convergence guarantee","volume":"18","author":"Wei","year":"2023","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Hidayat, M.A., Nakamura, Y., Dawton, B., and Arakawa, Y. (2023, January 3\u20136). Agc-dp: Differential privacy with adaptive gaussian clipping for federated learning. Proceedings of the 2023 24th IEEE International Conference on Mobile Data Management (MDM), Singapore.","DOI":"10.1109\/MDM58254.2023.00042"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"102048","DOI":"10.1016\/j.phycom.2023.102048","article-title":"Blockchain aware proxy re-encryption algorithm-based data sharing scheme","volume":"58","author":"Keshta","year":"2023","journal-title":"Phys. Commun."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"102983","DOI":"10.1016\/j.sysarc.2023.102983","article-title":"Privacy-preserving multi-party deep learning based on homomorphic proxy re-encryption","volume":"144","author":"Shen","year":"2023","journal-title":"J. Syst. Archit."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Zhang, Z., Ji, S., Wang, S., and Huang, S. (2024). Conditional Proxy Re-Encryption-Based Key Sharing Mechanism for Clustered Federated Learning. Electronics, 13.","DOI":"10.3390\/electronics13050848"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1016\/j.csbj.2018.08.002","article-title":"A blockchain-based notarization service for biomedical knowledge retrieval","volume":"16","author":"Kleinaki","year":"2018","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Kostamis, P., Sendros, A., and Efraimidis, P. (2021, January 27\u201330). Exploring ethereum\u2019s data stores: A cost and performance comparison. Proceedings of the 2021 3rd Conference on Blockchain Research & Applications for Innovative Networks and Services (BRAINS), Paris, France.","DOI":"10.1109\/BRAINS52497.2021.9569804"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/j.future.2023.11.026","article-title":"Data management in Ethereum DApps: A cost and performance analysis","volume":"153","author":"Kostamis","year":"2024","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3570953","article-title":"Blockchain-empowered federated learning: Challenges, solutions, and future directions","volume":"55","author":"Zhu","year":"2023","journal-title":"ACM Comput. Surv."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Chen, Y., Lin, F., Chen, Z., Tang, C., Jia, R., and Li, M. (2022, January 19\u201323). Blockchain-based Federated Learning with Contribution-Weighted Aggregation for Medical Data Modeling. Proceedings of the 2022 IEEE 19th International Conference on Mobile Ad Hoc and Smart Systems (MASS), Denver, CO, USA.","DOI":"10.1109\/MASS56207.2022.00090"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"4381","DOI":"10.1109\/ACCESS.2023.3235484","article-title":"FL-incentivizer: FL-NFT and FL-tokens for federated learning model trading and training","volume":"11","author":"Majeed","year":"2023","journal-title":"IEEE Access"},{"key":"ref_49","unstructured":"Pandey, S.R., Nguyen, L.D., and Popovski, P. (2022). Fedtoken: Tokenized incentives for data contribution in federated learning. arXiv."},{"key":"ref_50","unstructured":"Pandl, K.D., Leiser, F., Thiebes, S., and Sunyaev, A. (2022). Reward systems for trustworthy medical federated learning. arXiv."},{"key":"ref_51","unstructured":"Chaliasos, S., Reif, I., Torralba-Agell, A., Ernstberger, J., Kattis, A., and Livshits, B. (2024, January 23\u201325). Analyzing and Benchmarking ZK-Rollups. Proceedings of the 6th Conference on Advances in Financial Technologies (AFT 2024), Vienna, Austria."},{"key":"ref_52","unstructured":"Global, U. (2024, December 01). ConInSe. Available online: https:\/\/trustchain.ngi.eu\/is-cis\/."},{"key":"ref_53","unstructured":"Lanet, J.L., and Toma, C. (2019). ADvoCATE: A Consent Management Platform for Personal Data Processing in the IoT Using Blockchain Technology. Innovative Security Solutions for Information Technology and Communications: 11th International Conference, SecITC 2018, Springer."}],"container-title":["Journal of Sensor and Actuator Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2224-2708\/14\/1\/11\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T10:38:04Z","timestamp":1759919884000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2224-2708\/14\/1\/11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,29]]},"references-count":53,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,2]]}},"alternative-id":["jsan14010011"],"URL":"https:\/\/doi.org\/10.3390\/jsan14010011","relation":{},"ISSN":["2224-2708"],"issn-type":[{"value":"2224-2708","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,29]]}}}