{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T03:02:42Z","timestamp":1763348562067,"version":"build-2065373602"},"reference-count":67,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2023,7,18]],"date-time":"2023-07-18T00:00:00Z","timestamp":1689638400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61702323","G20220602","2022YBR014"],"award-info":[{"award-number":["61702323","G20220602","2022YBR014"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002338","name":"National Training Program of Innovation and Entrepreneurship for Undergraduates","doi-asserted-by":"publisher","award":["61702323","G20220602","2022YBR014"],"award-info":[{"award-number":["61702323","G20220602","2022YBR014"]}],"id":[{"id":"10.13039\/501100002338","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010264","name":"Shanghai Maritime University\u2019s Top Innovative Talent Training Program for Graduate Students","doi-asserted-by":"publisher","award":["61702323","G20220602","2022YBR014"],"award-info":[{"award-number":["61702323","G20220602","2022YBR014"]}],"id":[{"id":"10.13039\/501100010264","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Model evaluation is critical in deep learning. However, the traditional model evaluation approach is susceptible to issues of untrustworthiness, including insecure data and model sharing, insecure model training, incorrect model evaluation, centralized model evaluation, and evaluation results that can be tampered easily. To minimize these untrustworthiness issues, this paper proposes a blockchain-based model evaluation framework. The framework consists of an access control layer, a storage layer, a model training layer, and a model evaluation layer. The access control layer facilitates secure resource sharing. To achieve fine-grained and flexible access control, an attribute-based access control model combining the idea of a role-based access control model is adopted. A smart contract is designed to manage the access control policies stored in the blockchain ledger. The storage layer ensures efficient and secure storage of resources. Resource files are stored in the IPFS, with the encrypted results of their index addresses recorded in the blockchain ledger. Another smart contract is designed to achieve decentralized and efficient management of resource records. The model training layer performs training on users\u2019 servers, and, to ensure security, the training data must have records in the blockchain. The model evaluation layer utilizes the recorded data to evaluate the recorded models. A method in the smart contract of the storage layer is designed to enable evaluation, with scores automatically uploaded as a resource attribute. The proposed framework is applied to deep learning-based motion object segmentation, demonstrating its key functionalities. Furthermore, we validated the storage strategy adopted by the framework, and the trustworthiness of the framework is also analyzed.<\/jats:p>","DOI":"10.3390\/s23146492","type":"journal-article","created":{"date-parts":[[2023,7,19]],"date-time":"2023-07-19T01:02:23Z","timestamp":1689728543000},"page":"6492","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A Blockchain-Based Trustworthy Model Evaluation Framework for Deep Learning and Its Application in Moving Object Segmentation"],"prefix":"10.3390","volume":"23","author":[{"given":"Rui","family":"Jiang","sequence":"first","affiliation":[{"name":"College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-5165-5001","authenticated-orcid":false,"given":"Jiatao","family":"Li","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weifeng","family":"Bu","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiang","family":"Shen","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"103597","DOI":"10.1016\/j.csi.2021.103597","article-title":"MOOCsChain: A blockchain-based secure storage and sharing scheme for MOOCs learning","volume":"81","author":"Li","year":"2022","journal-title":"Comput. Stand. Interfaces"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1109\/TDSC.2020.2977646","article-title":"A traceable and revocable ciphertext-policy attribute-based encryption scheme based on privacy protection","volume":"19","author":"Han","year":"2022","journal-title":"IEEE Trans. Dependable Secur. Comput."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Liu, H., Han, D., Cui, M., Li, K.C., Souri, A., and Shojafar, M. (2023). IdenMultiSig: Identity-based decentralized multi-signature in internet of things. IEEE Trans. Comput. Soc. Syst., 1\u201311.","DOI":"10.1109\/TCSS.2022.3232173"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3530","DOI":"10.1109\/TII.2021.3114621","article-title":"A blockchain-based auditable access control system for private data in service-centric iot environments","volume":"18","author":"Han","year":"2022","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"4704","DOI":"10.1109\/JIOT.2021.3107846","article-title":"A privacy-preserving storage scheme for logistics data with assistance of blockchain","volume":"9","author":"Li","year":"2022","journal-title":"IEEE Internet Things J."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"108527","DOI":"10.1016\/j.cie.2022.108527","article-title":"Modeling and analysis of port supply chain system based on Fabric blockchain","volume":"172","author":"Gao","year":"2022","journal-title":"Comput. Ind. Eng."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"18207","DOI":"10.1109\/ACCESS.2020.2968492","article-title":"Fabric-iot: A blockchain-based access control system in iot","volume":"8","author":"Liu","year":"2020","journal-title":"IEEE Access"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1007\/s11227-022-04655-5","article-title":"A blockchain-based secure storage and access control scheme for supply chain finance","volume":"79","author":"Li","year":"2023","journal-title":"J. Supercomput."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/j.future.2022.12.037","article-title":"A novel system for medical equipment supply chain traceability based on alliance chain and attribute and role access control","volume":"142","author":"Li","year":"2023","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"5038851","DOI":"10.1155\/2022\/5038851","article-title":"Blockchain-based deep learning to process iot data acquisition in cognitive data","volume":"2022","author":"Hannah","year":"2022","journal-title":"BioMed Res. Int."},{"key":"ref_11","first-page":"529","article-title":"Healthcare security using blockchain for pharmacogenomics","volume":"46","author":"Abraham","year":"2019","journal-title":"J. Int. Pharm. Res."},{"key":"ref_12","unstructured":"Kuo, T.T., and Ohno-Machado, L. (2018). Modelchain: Decentralized privacy-preserving healthcare predictive modeling framework on private blockchain networks. arXiv."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1109\/TITS.2015.2465141","article-title":"A novel approach for improved vehicular positioning using cooperative map matching and dynamic base station DGPS concept","volume":"17","author":"Rohani","year":"2015","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Goel, A., Agarwal, A., Vatsa, M., Singh, R., and Ratha, N. (2019, January 16\u201317). DeepRing: Protecting deep neural network with blockchain. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, Long Beach, CA, USA.","DOI":"10.1109\/CVPRW.2019.00341"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Wang, T., Du, M., Wu, X., and He, T. (2020, January 14\u201319). An analytical framework for trusted machine learning and computer vision running with blockchain. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, Seattle, WA, USA.","DOI":"10.1109\/CVPRW50498.2020.00011"},{"key":"ref_16","first-page":"2438","article-title":"DeepChain: Auditable and privacy-preserving deep learning with blockchain-based incentive","volume":"18","author":"Weng","year":"2021","journal-title":"IEEE Trans. Dependable Secur. Comput."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1007\/s11633-022-1378-4","article-title":"Deep learning-based moving object segmentation: Recent progress and research prospects","volume":"20","author":"Jiang","year":"2023","journal-title":"Mach. Intell. Res."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"Imagenet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Li, F.-F. (2009, January 20\u201325). Imagenet: A large-scale hierarchical image database. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_21","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_25","unstructured":"Lipton, Z.C., Berkowitz, J., and Elkan, C. (2015). A critical review of recurrent neural networks for sequence learning. arXiv."},{"key":"ref_26","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_27","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., and Polosukhin, I. (2017, January 4\u20139). Attention is all you need. Proceedings of the 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"484","DOI":"10.1038\/nature16961","article-title":"Mastering the game of Go with deep neural networks and tree search","volume":"529","author":"Silver","year":"2016","journal-title":"Nature"},{"key":"ref_29","first-page":"3371","article-title":"Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion","volume":"11","author":"Vincent","year":"2010","journal-title":"J. Mach. Learn. Res."},{"key":"ref_30","unstructured":"Kingma, D.P., and Welling, M. (2013). Auto-encoding variational bayes. arXiv."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1145\/3422622","article-title":"Generative adversarial networks","volume":"63","author":"Goodfellow","year":"2020","journal-title":"Commun. ACM"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.aiopen.2021.01.001","article-title":"Graph neural networks: A review of methods and applications","volume":"1","author":"Zhou","year":"2020","journal-title":"AI Open"},{"key":"ref_33","unstructured":"Gao, Y., Doan, B.G., Zhang, Z., Ma, S., Zhang, J., Fu, A., Nepal, S., and Kim, H. (2020). Backdoor attacks and countermeasures on deep learning: A comprehensive review. arXiv."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"47230","DOI":"10.1109\/ACCESS.2019.2909068","article-title":"Badnets: Evaluating backdooring attacks on deep neural networks","volume":"7","author":"Gu","year":"2019","journal-title":"IEEE Access"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Li, Y., Jiang, Y., Li, Z., and Xia, S.T. (2022). Backdoor learning: A survey. IEEE Trans. Neural Netw. Learn. Syst.","DOI":"10.1109\/TNNLS.2022.3182979"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Tang, R., Du, M., Liu, N., Yang, F., and Hu, X. (2020, January 6\u201310). An embarrassingly simple approach for trojan attack in deep neural networks. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Virtual.","DOI":"10.1145\/3394486.3403064"},{"key":"ref_37","unstructured":"Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A. (2017). Towards deep learning models resistant to adversarial attacks. arXiv."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Narodytska, N., and Kasiviswanathan, S.P. (2017, January 21\u201326). Simple Black-Box Adversarial Attacks on Deep Neural Networks. Proceedings of the CVPR Workshops, Honolulu, HI, USA.","DOI":"10.1109\/CVPRW.2017.172"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"6101","DOI":"10.1109\/TITS.2021.3077883","article-title":"An empirical review of deep learning frameworks for change detection: Model design, experimental frameworks, challenges and research needs","volume":"23","author":"Mandal","year":"2021","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.neunet.2019.04.024","article-title":"Deep neural network concepts for background subtraction: A systematic review and comparative evaluation","volume":"117","author":"Bouwmans","year":"2019","journal-title":"Neural Netw."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Wang, Y., Jodoin, P.M., Porikli, F., Konrad, J., Benezeth, Y., and Ishwar, P. (2014, January 23\u201328). CDnet 2014: An expanded change detection benchmark dataset. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Columbus, OH, USA.","DOI":"10.1109\/CVPRW.2014.126"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Minematsu, T., Shimada, A., and Taniguchi, R.i. (2019, January 18\u201321). Simple background subtraction constraint for weakly supervised background subtraction network. Proceedings of the IEEE International Conference on Advanced Video and Signal-based Surveillance (AVSS), Taipei, Taiwan.","DOI":"10.1109\/AVSS.2019.8909896"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"59143","DOI":"10.1109\/ACCESS.2019.2914961","article-title":"A comprehensive survey of video datasets for background subtraction","volume":"7","author":"Kalsotra","year":"2019","journal-title":"IEEE Access"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1016\/j.patrec.2018.08.002","article-title":"Foreground segmentation using convolutional neural networks for multiscale feature encoding","volume":"112","author":"Lim","year":"2018","journal-title":"Pattern Recognit. Lett."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1369","DOI":"10.1007\/s10044-019-00845-9","article-title":"Learning multi-scale features for foreground segmentation","volume":"23","author":"Lim","year":"2020","journal-title":"Pattern Anal. Appl."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Tezcan, O., Ishwar, P., and Konrad, J. (2020, January 1\u20135). BSUV-Net: A fully-convolutional neural network for background subtraction of unseen videos. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV), Snowmass Village, CO, USA.","DOI":"10.1109\/WACV45572.2020.9093464"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"53849","DOI":"10.1109\/ACCESS.2021.3071163","article-title":"BSUV-Net 2.0: Spatio-temporal data augmentations for video-agnostic supervised background subtraction","volume":"9","author":"Tezcan","year":"2021","journal-title":"IEEE Access"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Liang, D., Wei, Z., Sun, H., and Zhou, H. (2021, January 5\u20139). Robust cross-Scene foreground segmentation in surveillance video. Proceedings of the IEEE International Conference on Multimedia & Expo (ICME), Shenzhen, China.","DOI":"10.1109\/ICME51207.2021.9428086"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Rahmon, G., Bunyak, F., Seetharaman, G., and Palaniappan, K. (2021, January 10\u201315). Motion U-Net: Multi-cue encoder-decoder network for motion segmentation. Proceedings of the 2020 25th International Conference on Pattern Recognition (ICPR), Milan, Italy.","DOI":"10.1109\/ICPR48806.2021.9413211"},{"key":"ref_50","first-page":"2485","article-title":"Graph moving object segmentation","volume":"44","author":"Giraldo","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1016\/j.cviu.2013.12.005","article-title":"A comprehensive review of background subtraction algorithms evaluated with synthetic and real videos","volume":"122","author":"Sobral","year":"2014","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_52","unstructured":"Nakamoto, S. (2008). Bitcoin: A peer-to-peer electronic cash system. Decentralized Bus. Rev., 21260."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1109\/35.312842","article-title":"Access control: Principle and practice","volume":"32","author":"Sandhu","year":"1994","journal-title":"IEEE Commun. Mag."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"4423","DOI":"10.1007\/s00500-021-06496-5","article-title":"Blockchain for federated learning toward secure distributed machine learning systems: A systemic survey","volume":"26","author":"Li","year":"2022","journal-title":"Soft Comput."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"102572","DOI":"10.1016\/j.ipm.2021.102572","article-title":"A blockchain empowered and privacy preserving digital contact tracing platform","volume":"58","author":"Bandara","year":"2021","journal-title":"Inf. Process. Manag."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"107669","DOI":"10.1016\/j.cie.2021.107669","article-title":"Blockchain-based smart tracking and tracing platform for drug supply chain","volume":"161","author":"Liu","year":"2021","journal-title":"Comput. Ind. Eng."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"100308","DOI":"10.1109\/ACCESS.2020.2998159","article-title":"Blockchain-based solution for the traceability of spare parts in manufacturing","volume":"8","author":"Hasan","year":"2020","journal-title":"IEEE Access"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Szabo, N. (1997). Formalizing and securing relationships on public networks. First Monday, 2.","DOI":"10.5210\/fm.v2i9.548"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1109\/2.485845","article-title":"Role-based access control models","volume":"29","author":"Sandhu","year":"1996","journal-title":"Computer"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1109\/MC.2015.33","article-title":"Attribute-based access control","volume":"48","author":"Hu","year":"2015","journal-title":"Computer"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1007\/s10586-022-03582-7","article-title":"Blockchain for deep learning: Review and open challenges","volume":"26","author":"Shafay","year":"2023","journal-title":"Cluster Comput."},{"key":"ref_62","unstructured":"Penard, W., and Van Werkhoven, T. (2008). On the secure hash algorithm family. Cryptogr. Context, 1\u201318."},{"key":"ref_63","unstructured":"Benet, J. (2014). IPFS-content addressed, versioned, p2p file system. arXiv."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Androulaki, E., Barger, A., Bortnikov, V., Cachin, C., Christidis, K., De Caro, A., Enyeart, D., Ferris, C., Laventman, G., and Manevich, Y. (2018, January 23\u201326). Hyperledger fabric: A distributed operating system for permissioned blockchains. Proceedings of the Thirteenth EuroSys Conference, Porto, Portugal.","DOI":"10.1145\/3190508.3190538"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"360","DOI":"10.1007\/s12204-023-2603-1","article-title":"Foreground segmentation network with enhanced attention","volume":"28","author":"Jiang","year":"2023","journal-title":"J. Shanghai Jiaotong Univ. (Sci.)"},{"key":"ref_66","unstructured":"Chollet, F. (2023, May 01). Keras. Available online: https:\/\/keras.io."},{"key":"ref_67","unstructured":"Hinton, G. (2023, May 01). Neural Networks for Machine Learning Lecture 6. Available online: http:\/\/www.cs.toronto.edu\/~hinton\/coursera\/lecture6\/lec6.pdf."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/14\/6492\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:14:07Z","timestamp":1760127247000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/14\/6492"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,18]]},"references-count":67,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2023,7]]}},"alternative-id":["s23146492"],"URL":"https:\/\/doi.org\/10.3390\/s23146492","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2023,7,18]]}}}