{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,3]],"date-time":"2026-05-03T05:16:07Z","timestamp":1777785367425,"version":"3.51.4"},"reference-count":54,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2022,5,19]],"date-time":"2022-05-19T00:00:00Z","timestamp":1652918400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Coordena\u0107\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior-Brasil (CAPES)","award":["001"],"award-info":[{"award-number":["001"]}]},{"name":"Coordena\u0107\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior-Brasil (CAPES)","award":["435683\/2018-7"],"award-info":[{"award-number":["435683\/2018-7"]}]},{"name":"Coordena\u0107\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior-Brasil (CAPES)","award":["UIDB\/50022\/2020"],"award-info":[{"award-number":["UIDB\/50022\/2020"]}]},{"DOI":"10.13039\/501100002322","name":"Brazilian fostering agency CNPq (National Council for Scientific and Technological Development)","doi-asserted-by":"publisher","award":["001"],"award-info":[{"award-number":["001"]}],"id":[{"id":"10.13039\/501100002322","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002322","name":"Brazilian fostering agency CNPq (National Council for Scientific and Technological Development)","doi-asserted-by":"publisher","award":["435683\/2018-7"],"award-info":[{"award-number":["435683\/2018-7"]}],"id":[{"id":"10.13039\/501100002322","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002322","name":"Brazilian fostering agency CNPq (National Council for Scientific and Technological Development)","doi-asserted-by":"publisher","award":["UIDB\/50022\/2020"],"award-info":[{"award-number":["UIDB\/50022\/2020"]}],"id":[{"id":"10.13039\/501100002322","id-type":"DOI","asserted-by":"publisher"}]},{"name":"INEGI-LAETA","award":["001"],"award-info":[{"award-number":["001"]}]},{"name":"INEGI-LAETA","award":["435683\/2018-7"],"award-info":[{"award-number":["435683\/2018-7"]}]},{"name":"INEGI-LAETA","award":["UIDB\/50022\/2020"],"award-info":[{"award-number":["UIDB\/50022\/2020"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Vehicles are the major source of air pollution in modern cities, emitting excessive levels of CO2 and other noxious gases. Exploiting the OBD-II interface available on most vehicles, the continuous emission of such pollutants can be indirectly measured over time, although accuracy has been an important design issue when performing this task due the nature of the retrieved data. In this scenario, soft-sensor approaches can be adopted to process engine combustion data such as fuel injection and mass air flow, processing them to estimate pollution and transmitting the results for further analyses. Therefore, this article proposes a soft-sensor solution based on an embedded system designed to retrieve data from vehicles through their OBD-II interface, processing different inputs to provide estimated values of CO2 emissions over time. According to the type of data provided by the vehicle, two different algorithms are defined, and each follows a comprehensive mathematical formulation. Moreover, an unsupervised TinyML approach is also derived to remove outliers data when processing the computed data stream, improving the accuracy of the soft sensor as a whole while not requiring any interaction with cloud-based servers to operate. Initial results for an embedded implementation on the Freematics ONE+ board have shown the proposal\u2019s feasibility with an acquisition frequency equal to 1Hz and emission granularity measure of gCO2\/km.<\/jats:p>","DOI":"10.3390\/s22103838","type":"journal-article","created":{"date-parts":[[2022,5,20]],"date-time":"2022-05-20T00:18:11Z","timestamp":1653005891000},"page":"3838","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":64,"title":["A TinyML Soft-Sensor Approach for Low-Cost Detection and Monitoring of Vehicular Emissions"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7729-9085","authenticated-orcid":false,"given":"Pedro","family":"Andrade","sequence":"first","affiliation":[{"name":"Postgraduate Program in Electrical and Computer Engineering, Federal University of Rio Grande do Norte, Natal 59078-970, Rio Grande do Norte, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0116-6489","authenticated-orcid":false,"given":"Ivanovitch","family":"Silva","sequence":"additional","affiliation":[{"name":"Postgraduate Program in Electrical and Computer Engineering, Federal University of Rio Grande do Norte, Natal 59078-970, Rio Grande do Norte, Brazil"},{"name":"Department of Computer Engineering and Automation, Federal University of Rio Grande do Norte, Natal 59078-970, Rio Grande do Norte, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7678-9007","authenticated-orcid":false,"given":"Marianne","family":"Silva","sequence":"additional","affiliation":[{"name":"Postgraduate Program in Electrical and Computer Engineering, Federal University of Rio Grande do Norte, Natal 59078-970, Rio Grande do Norte, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2808-8529","authenticated-orcid":false,"given":"Thommas","family":"Flores","sequence":"additional","affiliation":[{"name":"Postgraduate Program in Electrical and Computer Engineering, Federal University of Rio Grande do Norte, Natal 59078-970, Rio Grande do Norte, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1691-3288","authenticated-orcid":false,"given":"Jord\u00e3o","family":"Cassiano","sequence":"additional","affiliation":[{"name":"Postgraduate Program in Electrical and Computer Engineering, Federal University of Rio Grande do Norte, Natal 59078-970, Rio Grande do Norte, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3988-8476","authenticated-orcid":false,"given":"Daniel G.","family":"Costa","sequence":"additional","affiliation":[{"name":"INEGI, Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Shakhov, V., and Sokolova, O. (2019, January 26\u201330). Towards Air Pollution Detection with Internet of Vehicles. Proceedings of the 2019 15th International Asian School-Seminar Optimization Problems of Complex Systems (OPCS), Novosibirsk, Russia.","DOI":"10.1109\/OPCS.2019.8880264"},{"key":"ref_2","unstructured":"(2020, April 10). World Health Organization\u2014Air Pollution. Available online: https:\/\/www.who.int\/health-topics\/air-pollution#tab=tab_1."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Korunoski, M., Stojkoska, B.R., and Trivodaliev, K. (2019, January 1\u20134). Internet of Things Solution for Intelligent Air Pollution Prediction and Visualization. Proceedings of the IEEE EUROCON 2019\u201418th International Conference on Smart Technologies, Novi Sad, Serbia.","DOI":"10.1109\/EUROCON.2019.8861609"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.cities.2018.04.003","article-title":"Understanding \u2018smart cities\u2019: Intertwining development drivers with desired outcomes in a multidimensional framework","volume":"81","author":"Yigitcanlar","year":"2018","journal-title":"Cities"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1016\/j.jes.2021.12.042","article-title":"Variability of fuel consumption and CO2 emissions of a gasoline passenger car under multiple in-laboratory and on-road testing conditions","volume":"125","author":"Zhou","year":"2023","journal-title":"J. Environ. Sci. (China)"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"135237","DOI":"10.1016\/j.scitotenv.2019.135237","article-title":"Connected & autonomous vehicles\u2014Environmental impacts\u2014A review","volume":"712","author":"Kopelias","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Khot, R., and Chitre, V. (2017, January 17\u201318). Survey on air pollution monitoring systems. Proceedings of the 2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), Coimbatore, India.","DOI":"10.1109\/ICIIECS.2017.8275846"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"4025","DOI":"10.1109\/JSEN.2019.2952447","article-title":"Visual Sensors Hardware Platforms: A Review","volume":"20","author":"Costa","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_9","first-page":"155","article-title":"An overview of IoT hardware development platforms","volume":"11","author":"Singh","year":"2020","journal-title":"Int. J. Emerg. Technol."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Signoretti, G., Silva, M., Araujo, J., Guedes, L.A., Silva, I., Sisinni, E., and Ferrari, P. (2020, January 3\u20135). Performance Evaluation of an evolving data compression algorithm embedded into an OBD-II edge device. Proceedings of the 2020 IEEE International Workshop on Metrology for Industry 4.0 IoT, Roma, Italy.","DOI":"10.1109\/MetroInd4.0IoT48571.2020.9138270"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.conengprac.2015.12.002","article-title":"Modeling, diagnosis and estimation of actuator faults in vehicle suspensions","volume":"49","year":"2016","journal-title":"Control Eng. Pract."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Silva, D.R.C., Oliveira, G.M.B., Silva, I., Ferrari, P., and Sisinni, E. (2018, January 25\u201328). Latency evaluation for MQTT and WebSocket Protocols: An Industry 4.0 perspective. Proceedings of the 2018 IEEE Symposium on Computers and Communications (ISCC), Natal, Brazil.","DOI":"10.1109\/ISCC.2018.8538692"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Silva, M., Vieira, E., Signoretti, G., Silva, I., Silva, D., and Ferrari, P. (2018). A Customer Feedback Platform for Vehicle Manufacturing Compliant with Industry 4.0 Vision. Sensors, 18.","DOI":"10.3390\/s18103298"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Andrade, P., Silva, I., Signoretti, G., Silva, M., Dias, J., Marques, L., and Costa, D.G. (2021, January 7\u20139). An Unsupervised TinyML Approach Applied for Pavement Anomalies Detection Under the Internet of Intelligent Vehicles. Proceedings of the 2021 IEEE International Workshop on Metrology for Industry 4.0 IoT (MetroInd4.0 IoT), Rome, Italy.","DOI":"10.1109\/MetroInd4.0IoT51437.2021.9488546"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Wang, X., Magno, M., Cavigelli, L., and Benini, L. (2019). FANN-on-MCU: An Open-Source Toolkit for Energy-Efficient Neural Network Inference at the Edge of the Internet of Things. arXiv.","DOI":"10.1109\/JIOT.2020.2976702"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1275","DOI":"10.1109\/JIOT.2018.2805263","article-title":"Edge Computing for the Internet of Things: A Case Study","volume":"5","author":"Premsankar","year":"2018","journal-title":"IEEE Internet Things J."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Maitra, S., Richards, D., Abdelgawad, A., and Yelamarthi, K. (2019, January 11\u201313). Performance Evaluation of IoT Encryption Algorithms: Memory, Timing, and Energy. Proceedings of the 2019 IEEE Sensors Applications Symposium (SAS), Sophia Antipolis, France.","DOI":"10.1109\/SAS.2019.8706017"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1109\/MCAS.2020.3005467","article-title":"TinyML-Enabled Frugal Smart Objects: Challenges and Opportunities","volume":"20","author":"Skarmeta","year":"2020","journal-title":"IEEE Circuits Syst. Mag."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"100461","DOI":"10.1016\/j.iot.2021.100461","article-title":"TinyML Meets IoT: A Comprehensive Survey","volume":"16","author":"Dutta","year":"2021","journal-title":"Internet Things"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Sanchez-Iborra, R. (2021). Lpwan and embedded machine learning as enablers for the next generation of wearable devices. Sensors, 21.","DOI":"10.3390\/s21155218"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Signoretti, G., Silva, M., Andrade, P., Silva, I., Sisinni, E., and Ferrari, P. (2021). An Evolving TinyML Compression Algorithm for IoT Environments Based on Data Eccentricity. Sensors, 21.","DOI":"10.3390\/s21124153"},{"key":"ref_22","first-page":"1595","article-title":"A review on TinyML: State-of-the-art and prospects","volume":"34","author":"Ray","year":"2021","journal-title":"J. King Saud Univ.-Comput. Inf. Sci."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Ajani, T., Imoize, A., and Atayero, A. (2021). An overview of machine learning within embedded and mobile devices-optimizations and applications. Sensors, 21.","DOI":"10.3390\/s21134412"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"111168","DOI":"10.1016\/j.ecoenv.2020.111168","article-title":"A novel soft sensor based warning system for hazardous ground-level ozone using advanced damped least squares neural network","volume":"205","author":"Balram","year":"2020","journal-title":"Ecotoxicol. Environ. Saf."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"14892","DOI":"10.1109\/JSEN.2020.3010134","article-title":"Development of Chemical Oxygen on Demand (COD) Soft Sensor Using Edge Intelligence","volume":"20","author":"Pattanayak","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Augello, A., Maniscalco, U., Pilato, G., and Vella, F. (2016, January 15\u201317). Disaster prevention virtual advisors through soft sensor paradigm. Proceedings of the Intelligent Interactive Multimedia Systems and Services 2016, Puerto de la Cruz, Spain.","DOI":"10.1007\/978-3-319-39345-2_55"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1109\/MCOM.2018.1700304","article-title":"Soft Sensing in Smart Cities: Handling 3Vs Using Recommender Systems, Machine Intelligence, and Data Analytics","volume":"56","author":"Habibzadeh","year":"2018","journal-title":"IEEE Commun. Mag."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Oliveira, J., Lemos, J., Vieira, E., Silva, I., Abrantes, J., Barros, D., and Costa, D. (2017, January 6\u201311). CO2 Catcher: A Platform for Monitoring of Vehicular Pollution in Smart Cities. Proceedings of the 2017 IEEE First Summer School on Smart Cities (S3C), Natal, Brazil.","DOI":"10.1109\/S3C.2017.8501380"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Silva, M., Signoretti, G., Oliveira, J., Silva, I., and Costa, D. (2019). A Crowdsensing Platform for Monitoring of Vehicular Emissions: A Smart City Perspective. Future Internet, 11.","DOI":"10.3390\/fi11010013"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Kangralkar, S., and Khanai, R. (2021, January 2\u20134). Machine Learning Application for Automotive Emission Prediction. Proceedings of the 2021 6th International Conference for Convergence in Technology (I2CT), Maharashtra, India.","DOI":"10.1109\/I2CT51068.2021.9418152"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Rivera, N., Molina Campoverde, P., Bermeo, A., Bermeo, O., and Figueroa, J. (2022). Driving Style Analysis by Studying PID\u2019s Signals for Determination of Its Influence on Pollutant Emissions. Communication, Smart Technologies and Innovation for Society, Springer.","DOI":"10.1007\/978-981-16-4126-8_30"},{"key":"ref_32","first-page":"100415","article-title":"Fuzzy inference system design for promoting an eco-friendly driving style in IoV domain","volume":"34","author":"Tropea","year":"2022","journal-title":"Veh. Commun."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Shahnavaz, F., and Akhavian, R. (2022). Automated Estimation of Construction Equipment Emission Using Inertial Sensors and Machine Learning Models. Sustainability, 14.","DOI":"10.3390\/su14052750"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Gao, Y., Dong, W., Guo, K., Liu, X., Chen, Y., Liu, X., Bu, J., and Chen, C. (2016, January 10\u201314). Mosaic: A low-cost mobile sensing system for urban air quality monitoring. Proceedings of the IEEE INFOCOM 2016\u2014The 35th Annual IEEE International Conference on Computer Communications, San Francisco, CA, USA.","DOI":"10.1109\/INFOCOM.2016.7524478"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"957","DOI":"10.1038\/s41467-020-14783-2","article-title":"Air pollution control strategies directly limiting national health damages in the US","volume":"11","author":"Ou","year":"2020","journal-title":"Nat. Commun."},{"key":"ref_36","unstructured":"Miralavy, S.P., Atani, R.E., and Khoshrouz, N. (2019). A Wireless Sensor Network based approach to monitor and control air Pollution in large urban areas. arXiv."},{"key":"ref_37","unstructured":"Zhong, H., Yin, C., Wu, X., Luo, J., and He, J. (2020). AirRL: A Reinforcement Learning Approach to Urban Air Quality Inference. arXiv."},{"key":"ref_38","unstructured":"Zhang, Q., Lam, J.C., Li, V.O., and Han, Y. (2020). Deep-AIR: A Hybrid CNN-LSTM Framework for Fine-Grained Air Pollution Forecast. arXiv."},{"key":"ref_39","unstructured":"Solehudin, A., and Heryana, N. (2019). Mapping and Monitoring Pollution Levels of Carbon Monoxide (CO) using Arduino and Location-Based Service. arXiv."},{"key":"ref_40","unstructured":"Firouzimagham, D., Sabouri, M., and Adhami, F. (2020). An IoT-Based System: Big Urban Traffic Data Mining Through Airborne Pollutant Gases Analysis. arXiv."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"123911","DOI":"10.1016\/j.fuel.2022.123911","article-title":"Quantitative study of vehicle CO2 emission at various temperatures and road loads","volume":"320","author":"Wang","year":"2022","journal-title":"Fuel"},{"key":"ref_42","unstructured":"Arsie, I., Leo, R.D., Pianese, C., and De Cesare, M. (2014, January 24\u201329). Estimation of in-cylinder mass and AFR by cylinder pressure measurement in automotive Diesel engines. Proceedings of the 19th IFAC World Congress, Cape Town, South Africa."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"88","DOI":"10.21014\/acta_imeko.v9i4.719","article-title":"Performance evaluation of a vehicular edge device for customer feedback in Industry 4.0","volume":"9","author":"Silva","year":"2020","journal-title":"ACTA IMEKO"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Hamm, A., Willner, A., and Schieferdecker, I. (2019). Edge Computing: A Comprehensive Survey of Current Initiatives and a Roadmap for a Sustainable Edge Computing Development. arXiv.","DOI":"10.30844\/wi_2020_g1-hamm"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Silva, I.M.D., Guedes, L.A., and Vasques, F. (2008, January 15\u201318). Performance evaluation of a compression algorithm for wireless sensor networks in monitoring applications. Proceedings of the 2008 IEEE International Conference on Emerging Technologies and Factory Automation, Hamburg, Germany.","DOI":"10.1109\/ETFA.2008.4638468"},{"key":"ref_46","unstructured":"Ruan, L., Guo, S., Qiu, X., and Buyya, R. (2020). Fog Computing for Smart Grids: Challenges and Solutions. arXiv."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Angelov, P. (2014, January 9\u201312). Anomaly detection based on eccentricity analysis. Proceedings of the 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), Orlando, FL, USA.","DOI":"10.1109\/EALS.2014.7009497"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1080\/00031305.1984.10483182","article-title":"Chebyshev Inequality with Estimated Mean and Variance","volume":"38","author":"Saw","year":"1984","journal-title":"Am. Stat."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"894","DOI":"10.1109\/19.536707","article-title":"On-line fault detection and diagnosis obtained by implementing neural algorithms on a digital signal processor","volume":"45","author":"Bernieri","year":"1996","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Kangin, D., and Angelov, P. (2015, January 12\u201317). Evolving clustering, classification and regression with TEDA. Proceedings of the 2015 International Joint Conference on Neural Networks (IJCNN), Killarney, Ireland.","DOI":"10.1109\/IJCNN.2015.7280528"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1016\/j.asoc.2017.12.032","article-title":"Ensemble of Evolving Data Clouds and Fuzzy Models for Weather Time Series Prediction","volume":"64","author":"Soares","year":"2018","journal-title":"Appl. Soft Comput."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"625","DOI":"10.1016\/j.applthermaleng.2017.05.087","article-title":"Volumetric efficiency modelling of internal combustion engines based on a novel adaptive learning algorithm of artificial neural networks","volume":"123","author":"Climent","year":"2017","journal-title":"Appl. Therm. Eng."},{"key":"ref_53","unstructured":"(2018). Monitoring CO2 Emissions from New Passenger Cars and Vans in 2016, European Environment Agency (EEA). Eea Report no 19\/2017."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Hien, N.L.H., and Kor, A.L. (2022). Analysis and Prediction Model of Fuel Consumption and Carbon Dioxide Emissions of Light-Duty Vehicles. Appl. Sci., 12.","DOI":"10.3390\/app12020803"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/10\/3838\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:14:30Z","timestamp":1760138070000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/10\/3838"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,19]]},"references-count":54,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2022,5]]}},"alternative-id":["s22103838"],"URL":"https:\/\/doi.org\/10.3390\/s22103838","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,19]]}}}