{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T03:54:24Z","timestamp":1768881264586,"version":"3.49.0"},"reference-count":70,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T00:00:00Z","timestamp":1768780800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Science and Technology of the Republic of Colombia"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Informatics"],"abstract":"<jats:p>Agricultural marketing increasingly integrates Agriculture 4.0 technologies\u2014Blockchain, AI\/ML, IoT, and recommendation systems\u2014yet systematic evaluations of computational maturity and deployment readiness remain limited. This Systematic Literature Review (SLR) examined 99 peer-reviewed studies (2019\u20132025) from Scopus, Web of Science, and IEEE Xplore following PRISMA protocols to assess algorithmic performance, evaluation methods, and Technology Readiness Levels (TRLs) for agricultural marketing applications. Hybrid recommendation systems dominate current research (28.3%), achieving accuracies of 80\u201392%, while blockchain implementations (15.2%) show fast transaction times (&lt;2 s) but limited real-world adoption. Machine learning models using Random Forest, Gradient Boosting, and CNNs reach 85\u201395% predictive accuracy, and IoT systems report &gt;95% data transmission reliability. However, 77.8% of technologies remain at validation stages (TRL \u2264 5), and only 3% demonstrate operational deployment beyond one year. The findings reveal an \u201cefficiency paradox\u201d: strong technical performance (75\u201397\/100) contrasts with weak economic validation (\u226420% include cost\u2013benefit analysis). Most studies overlook temporal, geographic, and economic generalization, prioritizing computational metrics over implementation viability. This review highlights the persistent validation\u2013deployment gap in digital agriculture, urging a shift toward multi-tier evaluation frameworks that include contextual, adoption, and impact validation under real deployment conditions.<\/jats:p>","DOI":"10.3390\/informatics13010014","type":"journal-article","created":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T14:58:54Z","timestamp":1768834734000},"page":"14","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["The Validation\u2013Deployment Gap in Agricultural Information Systems: A Systematic Technology Readiness Assessment"],"prefix":"10.3390","volume":"13","author":[{"given":"Mary Elsy","family":"Arzuaga-Ochoa","sequence":"first","affiliation":[{"name":"Department of Computer Science and Electronics, Universidad de la Costa CUC, Barranquilla 080001, Colombia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5433-0414","authenticated-orcid":false,"given":"Melisa","family":"Acosta-Coll","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Electronics, Universidad de la Costa CUC, Barranquilla 080001, Colombia"}]},{"given":"Mauricio","family":"Barrios Barrios","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Electronics, Universidad de la Costa CUC, Barranquilla 080001, Colombia"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,19]]},"reference":[{"key":"ref_1","unstructured":"FAO (2019). The State of Food and Agriculture 2019: Moving Forward on Food Loss and Waste Reduction, FAO."},{"key":"ref_2","unstructured":"Gustavsson, J., Cederberg, C., Sonesson, U., Van Otterdijk, R., and Meybeck, A. (2011). Global Food Losses and Food Waste: Extent, Causes and Prevention, FAO."},{"key":"ref_3","first-page":"1","article-title":"Agricultural markets and development","volume":"Volume 13","author":"Barrett","year":"2021","journal-title":"Annual Review of Resource Economics"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.agsy.2018.01.022","article-title":"Rapid transformation of food systems in developing regions: Highlighting the role of agricultural research & innovations","volume":"172","author":"Reardon","year":"2019","journal-title":"Agric. Syst."},{"key":"ref_5","unstructured":"Shepherd, A.W. (2019). Understanding Agricultural Marketing. Food and Agriculture, Organization of the United Nations (FAO)."},{"key":"ref_6","unstructured":"Vorley, B. (2018). Food, Inc. Corporate Concentration from Farm to Consumer, International Institute for Environment and Development (IIED)."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1413","DOI":"10.1016\/j.worlddev.2009.06.009","article-title":"The future of small farms: New directions for services, institutions, and intermediation","volume":"38","author":"Poulton","year":"2010","journal-title":"World Dev."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"104933","DOI":"10.1016\/j.landusepol.2020.104933","article-title":"Agriculture 4.0: Making it work for people, production, and the planet","volume":"100","author":"Rose","year":"2021","journal-title":"Land Use Policy"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.agsy.2017.01.023","article-title":"Big Data in Smart Farming\u2014A review","volume":"153","author":"Wolfert","year":"2017","journal-title":"Agric. Syst."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Saiz-Rubio, V., and Rovira-M\u00e1s, F. (2020). From Smart Farming towards Agriculture 5.0: A Review on Crop Data Management. Agronomy, 10.","DOI":"10.3390\/agronomy10020207"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"100009","DOI":"10.1016\/j.array.2019.100009","article-title":"The digitisation of agriculture: A survey of research activities on smart farming","volume":"3","author":"Bacco","year":"2019","journal-title":"Array"},{"key":"ref_12","first-page":"100315","article-title":"A review of social science on digital agriculture, smart farming and agriculture 4.0: New contributions and a future research agenda","volume":"90","author":"Klerkx","year":"2019","journal-title":"NJAS Wagening. J. Life Sci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"640","DOI":"10.1016\/j.tifs.2019.07.034","article-title":"The rise of blockchain technology in agriculture and food supply chains","volume":"91","author":"Kamilaris","year":"2019","journal-title":"Trends Food Sci. Technol."},{"key":"ref_14","unstructured":"Tian, F. (2017, January 16\u201318). A supply chain traceability system for food safety based on HACCP, blockchain & Internet of Things. Proceedings of the 2017 International Conference on Service Systems and Service Management, Dalian, China."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"103046","DOI":"10.1016\/j.agsy.2020.103046","article-title":"Digital twins in smart farming","volume":"189","author":"Verdouw","year":"2021","journal-title":"Agric. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"153616","DOI":"10.1109\/ACCESS.2021.3127201","article-title":"Application of Intelligent Recommendation for Agricultural Information: A Systematic Literature Review","volume":"9","author":"Song","year":"2021","journal-title":"IEEE Access"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1111\/wre.12255","article-title":"Big Data for weed control and crop protection","volume":"57","author":"Fountas","year":"2017","journal-title":"Weed Res."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.compag.2018.08.001","article-title":"Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review","volume":"153","author":"Rieder","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"102763","DOI":"10.1016\/j.agsy.2019.102763","article-title":"Digitalisation of agricultural knowledge and advice networks: A state-of-the-art review","volume":"180","author":"Fielke","year":"2020","journal-title":"Agric. Syst."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Liakos, K.G., Busato, P., Moshou, D., Pearson, S., and Bochtis, D. (2018). Machine learning in agriculture: A review. Sensors, 18.","DOI":"10.3390\/s18082674"},{"key":"ref_21","first-page":"1","article-title":"How big data and machine learning impact agriculture","volume":"8","author":"Caraka","year":"2021","journal-title":"J. Big Data"},{"key":"ref_22","unstructured":"C. A. S. P. (CASP) (2025, November 08). CASP Qualitative Checklist. Available, 2018. Available online: https:\/\/casp-uk.net\/casp-tools-checklists\/."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1162\/qss_a_00019","article-title":"Scopus as a curated, high-quality bibliometric data source for academic research in quantitative science studies","volume":"1","author":"Baas","year":"2020","journal-title":"Quant. Sci. Stud."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1162\/qss_a_00018","article-title":"Web of Science as a data source for research on scientific and scholarly activity","volume":"1","author":"Birkle","year":"2020","journal-title":"Quant. Sci. Stud."},{"key":"ref_25","unstructured":"IEEE (2025, November 08). IEEE Xplore Digital Library 2024. Available online: https:\/\/ieeexplore.ieee.org\/."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"276","DOI":"10.11613\/BM.2012.031","article-title":"Interrater reliability: The kappa statistic","volume":"22","author":"McHugh","year":"2012","journal-title":"Biochem. Medica"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"18131","DOI":"10.48084\/etasr.8908","article-title":"A Blockchain Semantic-based Approach for Secure and Traceable Agri-Food Supply Chain","volume":"14","author":"Annane","year":"2024","journal-title":"Eng. Technol. Appl. Sci. Res."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Bhatia, S., and Albarrak, A.S. (2023). A Blockchain-Driven Food Supply Chain Management Using QR Code and XAI-Faster RCNN Architecture. Sustainability, 15.","DOI":"10.3390\/su15032579"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Thilakarathne, N.N., Bakar, M.S.A., Abas, P.E., and Yassin, H. (2022). A Cloud Enabled Crop Recommendation Platform for Machine Learning-Driven Precision Farming. Sensors, 22.","DOI":"10.3390\/s22166299"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Shinde, A.V., Patil, D.D., and Tripathi, K.K. (2024). A Comprehensive Survey on Recommender Systems Techniques and Challenges in Big Data Analytics with IOT Applications. Rev. Gest\u00e3o Soc. Ambient., 18.","DOI":"10.24857\/rgsa.v18n2-097"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"746","DOI":"10.11591\/ijece.v13i1.pp746-755","article-title":"AgroSupportAnalytics: Big data recommender system for agricultural farmer complaints in Egypt","volume":"13","author":"Rslan","year":"2023","journal-title":"Int. J. Electr. Comput. Eng."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"724","DOI":"10.1016\/j.procs.2020.01.023","article-title":"Improvement of Crop Production Using Recommender System by Weather Forecasts","volume":"165","author":"Kamatchi","year":"2019","journal-title":"Procedia Comput. Sci."},{"key":"ref_33","first-page":"4111","article-title":"ML-CSFR: A Unified Crop Selection and Fertilizer Recommendation Framework based on Machine Learning","volume":"25","author":"Bhola","year":"2024","journal-title":"Scalable Comput. Pr. Exp."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Kiruthika, S., and Karthika, D. (2023). IOT-BASED professional crop recommendation system using a weight-based long-term memory approach. Meas. Sens., 27.","DOI":"10.1016\/j.measen.2023.100722"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"825","DOI":"10.1016\/j.procs.2022.07.124","article-title":"Towards an Efficient Recommender Systems in Smart Agriculture: A deep reinforcement learning approach","volume":"203","author":"Bouni","year":"2022","journal-title":"Procedia Comput. Sci."},{"key":"ref_36","first-page":"34","article-title":"Random forest algorithm use for crop recommendation","volume":"9","author":"Paithane","year":"2023","journal-title":"J. Eng. Technol. Ind. Appl."},{"key":"ref_37","first-page":"7330078","article-title":"Recommendation of Business Models for Agriculture-Related Platforms Based on Deep Learning","volume":"2022","author":"Zhou","year":"2022","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Fayyaz, Z., Ebrahimian, M., Nawara, D., Ibrahim, A., and Kashef, R. (2020). Recommendation systems: Algorithms, challenges, metrics, and business opportunities. Appl. Sci., 10.","DOI":"10.3390\/app10217748"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"24","DOI":"10.37934\/araset.34.1.2437","article-title":"A Framework for Blockchain and Internet of Things Integration in Improving Food Security in the Food Supply Chain","volume":"34","author":"Guixia","year":"2024","journal-title":"J. Adv. Res. Appl. Sci. Eng. Technol."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Kechagias, E.P., Gayialis, S.P., Papadopoulos, G.A., and Papoutsis, G. (2023). An Ethereum-Based Distributed Application for Enhancing Food Supply Chain Traceability. Foods, 12.","DOI":"10.3390\/foods12061220"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Zou, Y., Gao, Q., Wu, H., and Liu, N. (2024). Carbon-Efficient Scheduling in Fresh Food Supply Chains with a Time-Window-Constrained Deep Reinforcement Learning Model. Sensors, 24.","DOI":"10.3390\/s24237461"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Saban, M., Bekkour, M., Amdaouch, I., El Gueri, J., Ahmed, B.A., Chaari, M.Z., Ruiz-Alzola, J., Rosado-Mu\u00f1oz, A., and Aghzout, O. (2023). A Smart Agricultural System Based on PLC and a Cloud Computing Web Application Using LoRa and LoRaWan. Sensors, 23.","DOI":"10.3390\/s23052725"},{"key":"ref_43","first-page":"321","article-title":"Blockchain technology in the food supply chain: Influences on supplier relationships and outcomes","volume":"24.","author":"Schilhabel","year":"2023","journal-title":"Issues Inf. Syst."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"27834","DOI":"10.1038\/s41598-024-79177-6","article-title":"Assessing the adoption barriers for AI in food supply chain finance applying a hybrid interval-valued Fermatean fuzzy CRITIC-ARAS model","volume":"14","author":"Wang","year":"2024","journal-title":"Sci. Rep."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Umami, I., and Rahmawati, L. (2021). Comparing Epsilon Greedy and Thompson Sampling model for Multi-Armed Bandit algorithm on Marketing Dataset. J. Appl. Data Sci., 2.","DOI":"10.47738\/jads.v2i2.28"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Ramanathan, R., Duan, Y., Ajmal, T., Pelc, K., Gillespie, J., Ahmadzadeh, S., Condell, J., Hermens, I., and Ramanathan, U. (2023). Motivations and Challenges for Food Companies in Using IoT Sensors for Reducing Food Waste: Some Insights and a Road Map for the Future. Sustainability, 15.","DOI":"10.3390\/su15021665"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"012186","DOI":"10.1088\/1742-6596\/1693\/1\/012186","article-title":"Research on Contextual Recommendation System of Agricultural Science and Technology Resource Based on User Portrait","volume":"1693","author":"Zhang","year":"2020","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Chandan, A., John, M., and Potdar, V. (2023). Achieving UN SDGs in Food Supply Chain Using Blockchain Technology. Sustainability, 15.","DOI":"10.3390\/su15032109"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"596","DOI":"10.1007\/s12393-022-09322-2","article-title":"Advances in Machine Learning and Hyperspectral Imaging in the Food Supply Chain","volume":"14","author":"Kang","year":"2022","journal-title":"Food Eng. Rev."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"134","DOI":"10.24138\/jcomss-2020-0011","article-title":"Efficient Behavior Prediction Based on User Events","volume":"17","author":"Szabo","year":"2021","journal-title":"J. Commun. Softw. Syst."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Osmond, A.B., Hidayat, F., and Supangkat, S.H. (2021). Electronic Commerce Product Recommendation using Enhanced Conjoint Analysis. Int. J. Adv. Comput. Sci. Appl. (IJACSA), 12.","DOI":"10.14569\/IJACSA.2021.0121176"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Toader, D.-C., R\u0103dulescu, C.M., and Toader, C. (2024). Investigating the Adoption of Blockchain Technology in Agri-Food Supply Chains: Analysis of an Extended UTAUT Model. Agriculture, 14.","DOI":"10.3390\/agriculture14040614"},{"key":"ref_53","first-page":"17","article-title":"Crop Insurance Premium Recommendation System Using Artificial Intelligence Techniques","volume":"8","author":"Prakash","year":"2023","journal-title":"Int. J. Prof. Bus. Rev."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1007\/s40032-023-00941-0","article-title":"Modeling of Critical Food Supply Chain Drivers Using DEMATEL Method and Blockchain Technology","volume":"104","author":"Patidar","year":"2023","journal-title":"J. Inst. Eng. (India) Ser. C"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Mbadlisa, G., and Jokonya, O. (2024). Factors Affecting the Adoption of Blockchain Technologies in the Food Supply Chain. Front. Sustain. Food Syst., 8.","DOI":"10.3389\/fsufs.2024.1497599"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"43","DOI":"10.17762\/ijritcc.v11i1s.5992","article-title":"Machine Learning Approach for Prediction of the Online User Intention for a Product PurchaseMachine Learning Approach for Prediction of the Online User Intention for a Product Purchase","volume":"11","author":"Sharma","year":"2023","journal-title":"Int. J. Recent Innov. Trends Comput. Commun."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Wu, X., Ge, H., Jiang, Y., Sun, Z., Ji, X., Jia, Z., and Cui, G. (2023). A Blockchain-Based Traceability Model for Grain and Oil Food Supply Chain. Foods, 12.","DOI":"10.3390\/foods12173235"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Xu, J., Han, J., Qi, Z., Jiang, Z., Xu, K., Zheng, M., and Zhang, X. (2022). A Reliable Traceability Model for Grain and Oil Quality Safety Based on Blockchain and Industrial Internet. Sustainability, 14.","DOI":"10.3390\/su142215144"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"George, W., and Al-Ansari, T. (2023). GM-Ledger: Blockchain-Based Certificate Authentication for International Food Trade. Foods, 12.","DOI":"10.3390\/foods12213914"},{"key":"ref_60","first-page":"47","article-title":"Huang, Integration and Analysis of Data in Grain Quality and Safety Traceability Using Blockchain Technology","volume":"10","author":"Shao","year":"2023","journal-title":"J. Logist. Inform. Serv. Sci."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"2246739","DOI":"10.1080\/23311975.2023.2246739","article-title":"How might blockchain technology be used in the food supply chain? A systematic literature review","volume":"10","author":"Astuti","year":"2023","journal-title":"Cogent Bus. Manag."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Schmidt, D., Casagranda, L.F., Butturi, M.A., and Sellitto, M.A. (2024). Digital Technologies, Sustainability, and Efficiency in Grain Post-Harvest Activities: A Bibliometric Analysis. Sustainability, 16.","DOI":"10.3390\/su16031244"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1108\/BFJ-06-2023-0517","article-title":"Discovering the conceptual building blocks of blockchain technology applications in the agri-food supply chain: A review and re-search agenda","volume":"126","year":"2024","journal-title":"Br. Food J."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"98","DOI":"10.4018\/IJBIR.20210101.oa6","article-title":"Experiential Retailing Leveraged by Data Analytics","volume":"12","author":"Dastidar","year":"2021","journal-title":"Int. J. Bus. Intell. Res."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Mohammed, A., Potdar, V., and Quaddus, M. (2023). Exploring Factors and Impact of Blockchain Technology in the Food Supply Chains: An Exploratory Study. Foods, 12.","DOI":"10.3390\/foods12102052"},{"key":"ref_66","first-page":"1","article-title":"Industry 4.0, Circular Economy and Sustainability in the Food Industry: A Literature Review","volume":"6","year":"2023","journal-title":"Int. J. Ind. Eng. Oper. Manag."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Miller, T., Mikiciuk, G., Durlik, I., Mikiciuk, M., \u0141obodzi\u0144ska, A., and \u015anieg, M. (2025). The IoT and AI in Agriculture: The Time Is Now\u2014A Systematic Review of Smart Sensing Technologies. Sensors, 25.","DOI":"10.3390\/s25123583"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Demestichas, K., Peppes, N., Alexakis, T., and Adamopoulou, E. (2020). Blockchain in Agriculture Traceability Systems: A Review. Appl. Sci., 10.","DOI":"10.3390\/app10124113"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Sendros, A., Drosatos, G., Efraimidis, P.S., and Tsirliganis, N.C. (2022). Blockchain Applications in Agriculture: A Scoping Review. Appl. Sci., 12.","DOI":"10.3390\/app12168061"},{"key":"ref_70","unstructured":"Rogers, E.M. (2003). Diffusion of Innovations, Free Press. [5th ed.]."}],"container-title":["Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2227-9709\/13\/1\/14\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T15:17:07Z","timestamp":1768835827000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2227-9709\/13\/1\/14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,19]]},"references-count":70,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,1]]}},"alternative-id":["informatics13010014"],"URL":"https:\/\/doi.org\/10.3390\/informatics13010014","relation":{},"ISSN":["2227-9709"],"issn-type":[{"value":"2227-9709","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,19]]}}}