{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T22:42:58Z","timestamp":1782945778332,"version":"3.54.5"},"reference-count":58,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2024,2,7]],"date-time":"2024-02-07T00:00:00Z","timestamp":1707264000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000780","name":"European Commission","doi-asserted-by":"publisher","award":["101096573"],"award-info":[{"award-number":["101096573"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Italian National Recovery and Resilience Plan (NRRP) of NextGenerationEU","award":["101096573"],"award-info":[{"award-number":["101096573"]}]},{"name":"Swiss State Secretariat for Education, Research and Innovation (SERI)","award":["101096573"],"award-info":[{"award-number":["101096573"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JSAN"],"abstract":"<jats:p>In the rapidly evolving landscape of next-generation 6G systems, the integration of AI functions to orchestrate network resources and meet stringent user requirements is a key focus. Distributed Learning (DL), a promising set of techniques that shape the future of 6G communication systems, plays a pivotal role. Vehicular applications, representing various services, are likely to benefit significantly from the advances of 6G technologies, enabling dynamic management infused with inherent intelligence. However, the deployment of various DL methods in traditional vehicular settings with specific demands and resource constraints poses challenges. The emergence of distributed computing and communication resources, such as the edge-cloud continuum and integrated terrestrial and non-terrestrial networks (T\/NTN), provides a solution. Efficiently harnessing these resources and simultaneously implementing diverse DL methods becomes crucial, and Network Slicing (NS) emerges as a valuable tool. This study delves into the analysis of DL methods suitable for vehicular environments alongside NS. Subsequently, we present a framework to facilitate DL-as-a-Service (DLaaS) on a distributed networking platform, empowering the proactive deployment of DL algorithms. This approach allows for the effective management of heterogeneous services with varying requirements. The proposed framework is exemplified through a detailed case study in a vehicular integrated T\/NTN with diverse service demands from specific regions. Performance analysis highlights the advantages of the DLaaS approach, focusing on flexibility, performance enhancement, added intelligence, and increased user satisfaction in the considered T\/NTN vehicular scenario.<\/jats:p>","DOI":"10.3390\/jsan13010014","type":"journal-article","created":{"date-parts":[[2024,2,7]],"date-time":"2024-02-07T03:47:09Z","timestamp":1707277629000},"page":"14","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Network Sliced Distributed Learning-as-a-Service for Internet of Vehicles Applications in 6G Non-Terrestrial Network Scenarios"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-0767-7622","authenticated-orcid":false,"given":"David","family":"Naseh","sequence":"first","affiliation":[{"name":"Department of Electrical, Electronic and Information Engineering \u201cGuglielmo Marconi\u201d, University of Bologna, 40126 Bologna, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2716-6441","authenticated-orcid":false,"given":"Swapnil Sadashiv","family":"Shinde","sequence":"additional","affiliation":[{"name":"Department of Electrical, Electronic and Information Engineering \u201cGuglielmo Marconi\u201d, University of Bologna, 40126 Bologna, Italy"},{"name":"CNIT\u2014University of Bologna Research Unit, 40126 Bologna, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7338-1957","authenticated-orcid":false,"given":"Daniele","family":"Tarchi","sequence":"additional","affiliation":[{"name":"Department of Electrical, Electronic and Information Engineering \u201cGuglielmo Marconi\u201d, University of Bologna, 40126 Bologna, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2027","DOI":"10.1109\/COMST.2021.3089688","article-title":"Comprehensive Survey on Machine Learning in Vehicular Network: Technology, Applications and Challenges","volume":"23","author":"Tang","year":"2021","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1109\/MNET.011.2000430","article-title":"Federated Learning in Vehicular Networks: Opportunities and Solutions","volume":"35","author":"Posner","year":"2021","journal-title":"IEEE Netw."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3579","DOI":"10.1109\/JSAC.2021.3118346","article-title":"Distributed Learning in Wireless Networks: Recent Progress and Future Challenges","volume":"39","author":"Chen","year":"2021","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"591","DOI":"10.1109\/COMST.2022.3218527","article-title":"Distributed Artificial Intelligence Empowered by End-Edge-Cloud Computing: A Survey","volume":"25","author":"Duan","year":"2023","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2429","DOI":"10.1109\/COMST.2018.2815638","article-title":"Network Slicing and Softwarization: A Survey on Principles, Enabling Technologies, and Solutions","volume":"20","author":"Afolabi","year":"2018","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1109\/MNET.010.2100025","article-title":"Intelligent Task Offloading and Energy Allocation in the UAV-Aided Mobile Edge-Cloud Continuum","volume":"35","author":"Cheng","year":"2021","journal-title":"IEEE Netw."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1109\/MNET.011.2000567","article-title":"Computing over Space-Air-Ground Integrated Networks: Challenges and Opportunities","volume":"35","author":"Shang","year":"2021","journal-title":"IEEE Netw."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"6345","DOI":"10.1109\/TCOMM.2021.3088898","article-title":"Secrecy-Energy Efficient Hybrid Beamforming for Satellite-Terrestrial Integrated Networks","volume":"69","author":"Lin","year":"2021","journal-title":"IEEE Trans. Commun."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Lin, Z., Niu, H., An, K., Hu, Y., Li, D., Wang, J., and Al-Dhahir, N. (IEEE Internet Things J., 2023). Pain without Gain: Destructive Beamforming from a Malicious RIS Perspective in IoT Networks, IEEE Internet Things J., early access.","DOI":"10.1109\/JIOT.2023.3316830"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3717","DOI":"10.1109\/TAES.2022.3155711","article-title":"Refracting RIS-Aided Hybrid Satellite-Terrestrial Relay Networks: Joint Beamforming Design and Optimization","volume":"58","author":"Lin","year":"2022","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"4189","DOI":"10.1109\/TCOMM.2022.3170632","article-title":"Energy-Efficient Hybrid Beamforming for Multilayer RIS-Assisted Secure Integrated Terrestrial-Aerial Networks","volume":"70","author":"Sun","year":"2022","journal-title":"IEEE Trans. Commun."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Thakur, P., and Singh, G. (2021). Spectrum Sharing in Cognitive Radio Networks: Towards Highly Connected Environments, Wiley Telecom.","DOI":"10.1002\/9781119665458"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Thakur, P., and Singh, G. (2021). Spectrum Sharing in Cognitive Radio Networks: Towards Highly Connected Environments, Wiley Telecom.","DOI":"10.1002\/9781119665458"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Thakur, P., and Singh, G. (2021). Spectrum Sharing in Cognitive Radio Networks: Towards Highly Connected Environments, Wiley Telecom.","DOI":"10.1002\/9781119665458"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Mishra, P., and Singh, G. (2023). Sustainable Smart Cities: Enabling Technologies, Energy Trends and Potential Applications, Springer International Publishing.","DOI":"10.1007\/978-3-031-33354-5"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"7457","DOI":"10.1109\/JIOT.2020.2984887","article-title":"Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence","volume":"7","author":"Deng","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1109\/IOTM.001.2300035","article-title":"Slice-Level Performance Metric Forecasting in Intelligent Transportation Systems and the Internet of Vehicles","volume":"6","author":"Manias","year":"2023","journal-title":"IEEE Internet Things Mag."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1175","DOI":"10.1109\/COMST.2022.3158270","article-title":"A Survey of Intelligent Network Slicing Management for Industrial IoT: Integrated Approaches for Smart Transportation, Smart Energy, and Smart Factory","volume":"24","author":"Wu","year":"2022","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.comcom.2021.09.003","article-title":"Edge and fog computing for IoT: A survey on current research activities & future directions","volume":"180","author":"Laroui","year":"2021","journal-title":"Comput. Commun."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1717","DOI":"10.1109\/JPROC.2019.2917084","article-title":"Wireless Edge Computing with Latency and Reliability Guarantees","volume":"107","author":"Elbamby","year":"2019","journal-title":"Proc. IEEE"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1109\/MWC.006.2100699","article-title":"Actions at the Edge: Jointly Optimizing the Resources in Multi-Access Edge Computing","volume":"29","author":"Deng","year":"2022","journal-title":"IEEE Wirel. Commun."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1109\/MNET.2021.9606835","article-title":"Guest Editorial: In-Network Computing: Emerging Trends for the Edge-Cloud Continuum","volume":"35","author":"Zeng","year":"2021","journal-title":"IEEE Netw."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1109\/MNET.001.1800528","article-title":"Intelligent Network Slicing for V2X Services Toward 5G","volume":"33","author":"Mei","year":"2019","journal-title":"IEEE Netw."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Muscinelli, E., Shinde, S.S., and Tarchi, D. (2022). Overview of Distributed Machine Learning Techniques for 6G Networks. Algorithms, 15.","DOI":"10.3390\/a15060210"},{"key":"ref_25","first-page":"30","article-title":"A Survey on Distributed Machine Learning","volume":"53","author":"Verbraeken","year":"2020","journal-title":"ACM Comput. Surv."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"4298","DOI":"10.1109\/TVT.2020.2973651","article-title":"Blockchain Empowered Asynchronous Federated Learning for Secure Data Sharing in Internet of Vehicles","volume":"69","author":"Lu","year":"2020","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Ridolfi, L., Naseh, D., Shinde, S.S., and Tarchi, D. (2023). Implementation and Evaluation of a Federated Learning Framework on Raspberry PI Platforms for IoT 6G Applications. Future Internet, 15.","DOI":"10.3390\/fi15110358"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1518","DOI":"10.1109\/TCAD.2022.3205551","article-title":"Optimizing Training Efficiency and Cost of Hierarchical Federated Learning in Heterogeneous Mobile-Edge Cloud Computing","volume":"42","author":"Cui","year":"2023","journal-title":"IEEE Trans. Comput. Aided Des. Integr. Circuits Syst."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Zhang, W., Pu, L., Lin, T., and Yan, J. (IEEE Internet Things J., 2023). To Distill or Not To Distill: Towards Fast, Accurate and Communication Efficient Federated Distillation Learning, IEEE Internet Things J., early access.","DOI":"10.1109\/JIOT.2023.3324666"},{"key":"ref_30","unstructured":"Naseh, D., Shinde, S.S., and Tarchi, D. (2023, January 2\u20134). Enabling Intelligent Vehicular Networks Through Distributed Learning in the Non-Terrestrial Networks 6G Vision. Proceedings of the 28th European Wireless Conference (EW2023), Rome, Italy."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1109\/MCOM.001.2000397","article-title":"Wireless Communications for Collaborative Federated Learning","volume":"58","author":"Chen","year":"2020","journal-title":"IEEE Commun. Mag."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"384","DOI":"10.1016\/j.future.2023.03.038","article-title":"Vehicular intelligent collaborative intersection driving decision algorithm in Internet of Vehicles","volume":"145","author":"Shao","year":"2023","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Song, R., Liu, D., Chen, D.Z., Festag, A., Trinitis, C., Schulz, M., and Knoll, A. (2023, January 18\u201323). Federated Learning via Decentralized Dataset Distillation in Resource-Constrained Edge Environments. Proceedings of the 2023 International Joint Conference on Neural Networks (IJCNN), Gold Coast, Australia.","DOI":"10.1109\/IJCNN54540.2023.10191879"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1109\/TCOMM.2020.3026398","article-title":"Q-GADMM: Quantized Group ADMM for Communication Efficient Decentralized Machine Learning","volume":"69","author":"Elgabli","year":"2021","journal-title":"IEEE Trans. Commun."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Han, S., Chen, Y., Du, L., and Lv, J. (2022, January 8\u201312). ADMM-based Energy-Efficient Resource Allocation Method for Internet of Vehicles. Proceedings of the 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), Macau, China.","DOI":"10.1109\/ITSC55140.2022.9922478"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"13677","DOI":"10.1007\/s10489-022-04105-y","article-title":"A review of cooperative multi-agent deep reinforcement learning","volume":"53","author":"Oroojlooy","year":"2023","journal-title":"Appl. Intell."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Jiang, K., Zhou, H., Zeng, D., and Wu, J. (2020, January 10\u201313). Multi-Agent Reinforcement Learning for Cooperative Edge Caching in Internet of Vehicles. Proceedings of the 2020 IEEE 17th International Conference on Mobile Ad Hoc and Sensor Systems (MASS), Delhi, India.","DOI":"10.1109\/MASS50613.2020.00062"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1007\/s43684-022-00023-5","article-title":"Multi-agent reinforcement learning for cooperative lane changing of connected and autonomous vehicles in mixed traffic","volume":"2","author":"Zhou","year":"2022","journal-title":"Auton. Intell. Syst."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1109\/TCSS.2021.3074038","article-title":"Collaborative Edge Computing for Social Internet of Vehicles to Alleviate Traffic Congestion","volume":"9","author":"Wang","year":"2022","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Girelli Consolaro, N., Shinde, S.S., Naseh, D., and Tarchi, D. (2023). Analysis and Performance Evaluation of Transfer Learning Algorithms for 6G Wireless Networks. Electronics, 12.","DOI":"10.3390\/electronics12153327"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"790","DOI":"10.1109\/TR.2021.3062045","article-title":"Transfer Learning Promotes 6G Wireless Communications: Recent Advances and Future Challenges","volume":"70","author":"Wang","year":"2021","journal-title":"IEEE Trans. Reliab."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"13849","DOI":"10.1109\/JIOT.2021.3088875","article-title":"A Survey of Recent Advances in Edge-Computing-Powered Artificial Intelligence of Things","volume":"8","author":"Chang","year":"2021","journal-title":"IEEE Internet Things J."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Liu, L., Yao, Y., Wang, R., Wu, B., and Shi, W. (2020, January 27\u201328). Equinox: A Road-Side Edge Computing Experimental Platform for CAVs. Proceedings of the 2020 International Conference on Connected and Autonomous Driving (MetroCAD), Detroit, MI, USA.","DOI":"10.1109\/MetroCAD48866.2020.00014"},{"key":"ref_44","unstructured":"Wang, Y., Liu, L., Zhang, X., and Shi, W. (2019, January 9). HydraOne: An Indoor Experimental Research and Education Platform for CAVs. Proceedings of the 2nd USENIX Workshop on Hot Topics in Edge Computing (HotEdge 19), Renton, WA, USA."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Wu, T., Wang, Y., Shi, W., and Lu, J. (2020, January 27\u201328). HydraMini: An FPGA-based Affordable Research and Education Platform for Autonomous Driving. Proceedings of the 2020 International Conference on Connected and Autonomous Driving (MetroCAD), Detroit, MI, USA.","DOI":"10.1109\/MetroCAD48866.2020.00016"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Maheshwari, S., Zhang, W., Seskar, I., Zhang, Y., and Raychaudhuri, D. (May, January 29). EdgeDrive: Supporting Advanced Driver Assistance Systems using Mobile Edge Clouds Networks. Proceedings of the IEEE INFOCOM 2019\u2014IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), Paris, France.","DOI":"10.1109\/INFCOMW.2019.8845256"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Villanueva, A., Benemerito, R.L.L., Cabug-Os, M.J.M., Chua, R.B., Rebeca, C.K.D., and Miranda, M. (2019, January 24\u201325). Somnolence Detection System Utilizing Deep Neural Network. Proceedings of the 2019 International Conference on Information and Communications Technology (ICOIACT), Yogyakarta, Indonesia.","DOI":"10.1109\/ICOIACT46704.2019.8938460"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"127453","DOI":"10.1109\/ACCESS.2019.2939532","article-title":"An Automatic Car Accident Detection Method Based on Cooperative Vehicle Infrastructure Systems","volume":"7","author":"Tian","year":"2019","journal-title":"IEEE Access"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"6142","DOI":"10.1109\/TITS.2021.3083927","article-title":"A Survey of Driving Safety with Sensing, Vehicular Communications, and Artificial Intelligence-Based Collision Avoidance","volume":"23","author":"Fu","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Tufail, A., Namoun, A., Abi Sen, A.A., Kim, K.H., Alrehaili, A., and Ali, A. (2021). Moisture Computing-Based Internet of Vehicles (IoV) Architecture for Smart Cities. Sensors, 21.","DOI":"10.3390\/s21113785"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1109\/TGCN.2021.3127923","article-title":"Green Internet of Vehicles (IoV) in the 6G Era: Toward Sustainable Vehicular Communications and Networking","volume":"6","author":"Wang","year":"2022","journal-title":"IEEE Trans. Green Commun. Netw."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1109\/JPROC.2019.2947490","article-title":"Mobile Edge Intelligence and Computing for the Internet of Vehicles","volume":"108","author":"Zhang","year":"2020","journal-title":"Proc. IEEE"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1761","DOI":"10.1109\/COMST.2020.2997475","article-title":"Complementing IoT Services Through Software Defined Networking and Edge Computing: A Comprehensive Survey","volume":"22","author":"Rafique","year":"2020","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1109\/MNET.001.1900659","article-title":"Emerging Technologies for 5G-IoV Networks: Applications, Trends and Opportunities","volume":"34","author":"Duan","year":"2020","journal-title":"IEEE Netw."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1280","DOI":"10.1109\/COMST.2022.3149714","article-title":"A Survey of Collaborative Machine Learning Using 5G Vehicular Communications","volume":"24","author":"Balkus","year":"2022","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_56","first-page":"1464","article-title":"Edge Intelligence for Adaptive Multimedia Streaming in Heterogeneous Internet of Vehicles","volume":"22","author":"Dai","year":"2023","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"6792","DOI":"10.1109\/ACCESS.2020.2964697","article-title":"5G Vehicular Network Resource Management for Improving Radio Access through Machine Learning","volume":"8","author":"Khattak","year":"2020","journal-title":"IEEE Access"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"2041","DOI":"10.1109\/TVT.2021.3135332","article-title":"On the Design of Federated Learning in Latency and Energy Constrained Computation Offloading Operations in Vehicular Edge Computing Systems","volume":"71","author":"Shinde","year":"2022","journal-title":"IEEE Trans. Veh. Technol."}],"container-title":["Journal of Sensor and Actuator Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2224-2708\/13\/1\/14\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T13:56:17Z","timestamp":1760104577000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2224-2708\/13\/1\/14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,7]]},"references-count":58,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,2]]}},"alternative-id":["jsan13010014"],"URL":"https:\/\/doi.org\/10.3390\/jsan13010014","relation":{},"ISSN":["2224-2708"],"issn-type":[{"value":"2224-2708","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,7]]}}}