{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T11:19:32Z","timestamp":1784805572572,"version":"3.55.0"},"reference-count":64,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2020,5,25]],"date-time":"2020-05-25T00:00:00Z","timestamp":1590364800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100010418","name":"Institute for Information and Communications Technology Promotion","doi-asserted-by":"publisher","award":["2019-0-00203"],"award-info":[{"award-number":["2019-0-00203"]}],"id":[{"id":"10.13039\/501100010418","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF-2019R1I1A3A01062789"],"award-info":[{"award-number":["NRF-2019R1I1A3A01062789"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Agriculture and livestock play a vital role in social and economic stability. Food safety and transparency in the food supply chain are a significant concern for many people. Internet of Things (IoT) and blockchain are gaining attention due to their success in versatile applications. They generate a large amount of data that can be optimized and used efficiently by advanced deep learning (ADL) techniques. The importance of such innovations from the viewpoint of supply chain management is significant in different processes such as for broadened visibility, provenance, digitalization, disintermediation, and smart contracts. This article takes the secure IoT\u2013blockchain data of Industry 4.0 in the food sector as a research object. Using ADL techniques, we propose a hybrid model based on recurrent neural networks (RNN). Therefore, we used long short-term memory (LSTM) and gated recurrent units (GRU) as a prediction model and genetic algorithm (GA) optimization jointly to optimize the parameters of the hybrid model. We select the optimal training parameters by GA and finally cascade LSTM with GRU. We evaluated the performance of the proposed system for a different number of users. This paper aims to help supply chain practitioners to take advantage of the state-of-the-art technologies; it will also help the industry to make policies according to the predictions of ADL.<\/jats:p>","DOI":"10.3390\/s20102990","type":"journal-article","created":{"date-parts":[[2020,5,25]],"date-time":"2020-05-25T11:42:02Z","timestamp":1590406922000},"page":"2990","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":264,"title":["IoT-Blockchain Enabled Optimized Provenance System for Food Industry 4.0 Using Advanced Deep Learning"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2561-4389","authenticated-orcid":false,"given":"Prince Waqas","family":"Khan","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Jeju National University, Jeju City 63243, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1107-9941","authenticated-orcid":false,"given":"Yung-Cheol","family":"Byun","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Jeju National University, Jeju City 63243, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4434-8933","authenticated-orcid":false,"given":"Namje","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Computer Education, Teachers College, Jeju National University, Jeju City 63243, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,5,25]]},"reference":[{"key":"ref_1","unstructured":"WHO (2020, May 12). Factsheet: Child Maltreatment. Available online: https:\/\/www.afro.who.int\/health-topics\/child-health."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.tifs.2014.03.010","article-title":"Statistical Process Control (SPC) in the food industry\u2014A systematic review and future research agenda","volume":"37","author":"Lim","year":"2014","journal-title":"Trends Food Sci. Technol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1080\/02508060408691768","article-title":"Improving wheat productivity in Pakistan: Econometric analysis using panel data from Chaj in the upper Indus Basin","volume":"29","author":"Hussain","year":"2004","journal-title":"Water Int."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"435","DOI":"10.1007\/s13213-011-0259-9","article-title":"Assessing the in vitro zinc solubilization potential and improving sugarcane growth by inoculating Gluconacetobacter diazotrophicus","volume":"62","author":"Natheer","year":"2012","journal-title":"Ann. Microbiol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1016\/j.meatsci.2017.04.019","article-title":"Animal breeding strategies can improve meat quality attributes within entire populations","volume":"132","author":"Berry","year":"2017","journal-title":"Meat Sci."},{"key":"ref_6","unstructured":"Chivenge, P., and Sharma, S. (2019, January 6\u20138). Precision agriculture in food production: Nutrient management. Proceedings of the International Workshop on ICTs for Precision Agriculture, Selangor, Malaysia."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"38442","DOI":"10.1109\/ACCESS.2019.2906033","article-title":"UAV\u2019s Agricultural Image Segmentation Predicated by Clifford Geometric Algebra","volume":"7","author":"Khan","year":"2019","journal-title":"IEEE Access"},{"key":"ref_8","first-page":"86","article-title":"Soil moisture measurement to support production of high-quality oranges for information and communication technology (ICT) application in production orchards","volume":"20","author":"Fujita","year":"2011","journal-title":"Agric. Inf. Res."},{"key":"ref_9","unstructured":"Nakamoto, S., and Bitcoin, A. (2020, May 11). A Peer-to-Peer Electronic Cash System. Available online: https:\/\/bitcoin.org\/bitcoin.pdf."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Tijan, E., Aksentijevi\u0107, S., Ivani\u0107, K., and Jardas, M. (2019). Blockchain technology implementation in logistics. Sustainability, 11.","DOI":"10.3390\/su11041185"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Ko, T., Lee, J., and Ryu, D. (2018). Blockchain technology and manufacturing industry: Real-time transparency and cost savings. Sustainability, 10.","DOI":"10.3390\/su10114274"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.jmsy.2018.01.003","article-title":"Deep learning for smart manufacturing: Methods and applications","volume":"48","author":"Wang","year":"2018","journal-title":"J. Manuf. Syste."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3559","DOI":"10.1109\/TII.2019.2897805","article-title":"Performance optimization for blockchain-enabled industrial internet of things (iiot) systems: A deep reinforcement learning approach","volume":"15","author":"Liu","year":"2019","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Khan, P.W., and Byun, Y. (2020). A Blockchain-Based Secure Image Encryption Scheme for the Industrial Internet of Things. Entropy, 22.","DOI":"10.3390\/e22020175"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1109\/MIS.2013.39","article-title":"Artificial intelligence and big data","volume":"28","year":"2013","journal-title":"IEEE Intell. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Fern\u00e1ndez-Caram\u00e9s, T.M., Blanco-Novoa, O., Froiz-M\u00edguez, I., and Fraga-Lamas, P. (2019). Towards an Autonomous Industry 4.0 Warehouse: A UAV and Blockchain-Based System for Inventory and Traceability Applications in Big Data-Driven Supply Chain Management. Sensors, 19.","DOI":"10.3390\/s19102394"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"15542","DOI":"10.3390\/s121115542","article-title":"Meat and fish freshness inspection system based on odor sensing","volume":"12","author":"Hasan","year":"2012","journal-title":"Sensors"},{"key":"ref_18","unstructured":"Shams, S. (2020, May 12). Meat Scandal Highlights China\u2019s Negligence. Available online: https:\/\/www.dw.com\/en\/meat-scandal-highlights-chinas-negligence\/a-17806930."},{"key":"ref_19","unstructured":"News, B. (2020, May 12). Q & A: Horsemeat Scandal. Available online: https:\/\/www.bbc.com\/news\/uk-21335872."},{"key":"ref_20","unstructured":"Gillespie, P., Darlington, S., and Brocchetto, M. (2020, May 12). Brazil\u2019s Spoiled Meat Scandal Widens Worldwide. Available online: https:\/\/money.cnn.com\/2017\/03\/22\/news\/economy\/brazil-meat-scandal\/."},{"key":"ref_21","unstructured":"Tribune, T.E. (2020, May 12). Meat Matters: Feasting on Dogs and Donkeys. Available online: https:\/\/tribune.com.pk\/story\/211439\/meat-matters-feasting-on-dogs-and-donkeys\/."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1108\/BFJ-07-2017-0365","article-title":"The acceptance of blockchain technology in meat traceability and transparency","volume":"120","author":"Sander","year":"2018","journal-title":"Br. Food J."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"101967","DOI":"10.1016\/j.ijinfomgt.2019.05.023","article-title":"Modeling the blockchain enabled traceability in agriculture supply chain","volume":"52","author":"Kamble","year":"2020","journal-title":"Int. J. Inf. Manag."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Accorsi, R., Ferrari, E., Gamberi, M., Manzini, R., and Regattieri, A. (2016). A closed-loop traceability system to improve logistics decisions in food supply chains: A case study on dairy products. Advances in Food Traceability Techniques and Technologies, Elsevier.","DOI":"10.1016\/B978-0-08-100310-7.00018-1"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1016\/j.foodcont.2017.04.013","article-title":"An improved traceability system for food quality assurance and evaluation based on fuzzy classification and neural network","volume":"79","author":"Wang","year":"2017","journal-title":"Food Control"},{"key":"ref_26","unstructured":"Ribeiro, F.D.S., Caliva, F., Swainson, M., Gudmundsson, K., Leontidis, G., and Kollias, S. (2018, January 25\u201327). An adaptable deep learning system for optical character verification in retail food packaging. Proceedings of the 2018 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS), Rhodes, Greece."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Mao, D., Hao, Z., Wang, F., and Li, H. (2018). Innovative Blockchain-Based Approach for Sustainable and Credible Environment in Food Trade: A Case Study in Shandong Province, China. Sustainability, 10.","DOI":"10.3390\/su10093149"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Mao, D., Wang, F., Hao, Z., and Li, H. (2018). Credit evaluation system based on blockchain for multiple stakeholders in the food supply chain. Int. J. Environ. Res. Public Health, 15.","DOI":"10.3390\/ijerph15081627"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"20698","DOI":"10.1109\/ACCESS.2019.2897792","article-title":"Food safety traceability system based on blockchain and EPCIS","volume":"7","author":"Lin","year":"2019","journal-title":"IEEE Access"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"129000","DOI":"10.1109\/ACCESS.2019.2940227","article-title":"Blockchain-driven IoT for food traceability with an integrated consensus mechanism","volume":"7","author":"Tsang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Hao, Z., Mao, D., Zhang, B., Zuo, M., and Zhao, Z. (2020). A Novel Visual Analysis Method of Food Safety Risk Traceability Based on Blockchain. Int. J. Environ. Res. Public Health, 17.","DOI":"10.3390\/ijerph17072300"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Borah, M.D., Naik, V.B., Patgiri, R., Bhargav, A., Phukan, B., and Basani, S.G. (2020). Supply Chain Management in Agriculture Using Blockchain and IoT. Advanced Applications of Blockchain Technology, Springer.","DOI":"10.1007\/978-981-13-8775-3_11"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Casino, F., Dasaklis, T.K., and Patsakis, C. (2019, January 20\u201322). Enhanced Vendor-managed Inventory through Blockchain. Proceedings of the 2019 4th South-East Europe Design Automation, Computer Engineering, Computer\nNetworks and Social Media Conference (SEEDA-CECNSM), Piraeus, Greece.","DOI":"10.1109\/SEEDA-CECNSM.2019.8908481"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Baralla, G., Pinna, A., and Corrias, G. (2019, January 27\u201327). Ensure traceability in European food supply chain by using a blockchain system. Proceedings of the 2019 IEEE\/ACM 2nd International Workshop on Emerging Trends in Software Engineering for Blockchain (WETSEB), Montreal, QC, Canada.","DOI":"10.1109\/WETSEB.2019.00012"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Xie, C., Sun, Y., and Luo, H. (2017, January 10\u201311). Secured data storage scheme based on block chain for agricultural products tracking. Proceedings of the 2017 3rd International Conference on Big Data Computing and Communications (BIGCOM), Chengdu, China.","DOI":"10.1109\/BIGCOM.2017.43"},{"key":"ref_36","unstructured":"Tian, F. (2016, January 24\u201326). An agri-food supply chain traceability system for China based on RFID & blockchain technology. Proceedings of the 2016 13th international conference on service systems and service management (ICSSSM), Kunming, China."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Tseng, J.H., Liao, Y.C., Chong, B., and Liao, S.W. (2018). Governance on the drug supply chain via gcoin blockchain. Int. J. Environ. Res. Public Health, 15.","DOI":"10.3390\/ijerph15061055"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Hua, J., Wang, X., Kang, M., Wang, H., and Wang, F.Y. (2018, January 26\u201330). Blockchain based provenance for agricultural products: A distributed platform with duplicated and shared bookkeeping. Proceedings of the 2018 IEEE Intelligent Vehicles Symposium (IV), Changshu, China.","DOI":"10.1109\/IVS.2018.8500647"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"641","DOI":"10.1016\/j.future.2018.04.061","article-title":"Research on agricultural supply chain system with double chain architecture based on blockchain technology","volume":"86","author":"Leng","year":"2018","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Tse, D., Zhang, B., Yang, Y., Cheng, C., and Mu, H. (2017, January 10\u201313). Blockchain application in food supply information security. Proceedings of the 2017 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), Singapore.","DOI":"10.1109\/IEEM.2017.8290114"},{"key":"ref_41","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_42","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1016\/j.procs.2018.07.193","article-title":"How blockchain improves the supply chain: Case study alimentary supply chain","volume":"134","author":"Prieto","year":"2018","journal-title":"Procedia Comput. Sci."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"4146","DOI":"10.1109\/TII.2019.2948053","article-title":"BPAS: Blockchain-Assisted Privacy-Preserving Authentication System for Vehicular Ad-Hoc Networks","volume":"16","author":"Feng","year":"2019","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_44","unstructured":"Gaur, N., Desrosiers, L., Ramakrishna, V., Novotny, P., Baset, S.A., and O\u2019Dowd, A. (2018). Hands-On Blockchain with Hyperledger: Building Decentralized Applications with Hyperledger Fabric and Composer, Packt Publishing Ltd."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Jamil, F., Ahmad, S., Iqbal, N., and Kim, D.H. (2020). Towards a Remote Monitoring of Patient Vital Signs Based on IoT-Based Blockchain Integrity Management Platforms in Smart Hospitals. Sensors, 20.","DOI":"10.3390\/s20082195"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Hang, L., and Kim, D.H. (2020). Reliable Task Management Based on a Smart Contract for Runtime Verification of Sensing and Actuating Tasks in IoT Environments. Sensors, 20.","DOI":"10.3390\/s20041207"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Bouktif, S., Fiaz, A., Ouni, A., and Serhani, M.A. (2018). Optimal deep learning lstm model for electric load forecasting using feature selection and genetic algorithm: Comparison with machine learning approaches. Energies, 11.","DOI":"10.3390\/en11071636"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Kuo, C.C., Liu, C.H., Chang, H.C., and Lin, K.J. (2017). Implementation of a motor diagnosis system for rotor failure using genetic algorithm and fuzzy classification. Appl. Sci., 7.","DOI":"10.3390\/app7010031"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"802","DOI":"10.1109\/LMWC.2019.2950801","article-title":"Hyperparameter Optimization of Two-Hidden-Layer Neural Networks for Power Amplifiers Behavioral Modeling Using Genetic Algorithms","volume":"29","author":"Wang","year":"2019","journal-title":"IEEE Microw. Wirel. Compon. Lett."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Settles, M., Rodebaugh, B., and Soule, T. (2003). Comparison of genetic algorithm and particle swarm optimizer when evolving a recurrent neural network. Genetic and Evolutionary Computation Conference, Springer.","DOI":"10.1007\/3-540-45105-6_17"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Chambers, L.D. (2019). Practical Handbook of Genetic Algorithms: Complex Coding Systems, CRC Press.","DOI":"10.1201\/9780429128356"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Shafqat, W., and Byun, Y.C. (2019). Topic Predictions and Optimized Recommendation Mechanism Based on Integrated Topic Modeling and Deep Neural Networks in Crowdfunding Platforms. Appl. Sci., 9.","DOI":"10.3390\/app9245496"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.physa.2018.09.120","article-title":"Improved EEMD-based crude oil price forecasting using LSTM networks","volume":"516","author":"Wu","year":"2019","journal-title":"Phys. A Stat. Mech. Appl."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Poornima, S., and Pushpalatha, M. (2019). Prediction of Rainfall Using Intensified LSTM Based Recurrent Neural Network with Weighted Linear Units. Atmosphere, 10.","DOI":"10.3390\/atmos10110668"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Kamal, I.M., Bae, H., Sunghyun, S., and Yun, H. (2020). DERN: Deep Ensemble Learning Model for Short-and Long-Term Prediction of Baltic Dry Index. Appl. Sci., 10.","DOI":"10.3390\/app10041504"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Yang, G., Lee, H., and Lee, G. (2020). A Hybrid Deep Learning Model to Forecast Particulate Matter Concentration Levels in Seoul, South Korea. Atmosphere, 11.","DOI":"10.3390\/atmos11040348"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Yue, B., Fu, J., and Liang, J. (2018). Residual recurrent neural networks for learning sequential representations. Information, 9.","DOI":"10.3390\/info9030056"},{"key":"ref_59","unstructured":"Lp, X., Yu, W., Luwang, T., Zheng, J., Qiu, X., Zhao, J., Xia, L., and Li, Y. (2018, January 9\u201311). Transaction fraud detection using GRU-centered sandwich-structured model. Proceedings of the 2018 IEEE 22nd International Conference on Computer Supported Cooperative Work in Design (CSCWD), Nanjing, China."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Pan, X., Jiang, T., Sui, B., Liu, C., and Sun, W. (2020). Monthly and Quarterly Sea Surface Temperature Prediction Based on Gated Recurrent Unit Neural Network. J. Mar. Sci. Eng., 8.","DOI":"10.3390\/jmse8040249"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Yuan, J., and Tian, Y. (2019). An Intelligent Fault Diagnosis Method Using GRU Neural Network towards Sequential Data in Dynamic Processes. Processes, 7.","DOI":"10.3390\/pr7030152"},{"key":"ref_62","unstructured":"Manorama (2020, April 18). Daily Sales Data of a Grocery Store. Available online: https:\/\/www.kaggle.com\/manovirat\/groceries-sales-data."},{"key":"ref_63","unstructured":"IBM (2020, April 15). Hyperledger Caliper. Available online: https:\/\/www.hyperledger.org\/projects\/caliper."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Lin, Y.P., Petway, J.R., Anthony, J., Mukhtar, H., Liao, S.W., Chou, C.F., and Ho, Y.F. (2017). Blockchain: The evolutionary next step for ICT e-agriculture. Environments, 4.","DOI":"10.3390\/environments4030050"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/10\/2990\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:32:18Z","timestamp":1760175138000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/10\/2990"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,5,25]]},"references-count":64,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2020,5]]}},"alternative-id":["s20102990"],"URL":"https:\/\/doi.org\/10.3390\/s20102990","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,5,25]]}}}