{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T16:04:24Z","timestamp":1776441864202,"version":"3.51.2"},"reference-count":64,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2020,11,6]],"date-time":"2020-11-06T00:00:00Z","timestamp":1604620800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Live sheep export has become a public concern. This study aimed to test a non-contact biometric system based on artificial intelligence to assess heat stress of sheep to be potentially used as automated animal welfare assessment in farms and while in transport. Skin temperature (\u00b0C) from head features were extracted from infrared thermal videos (IRTV) using automated tracking algorithms. Two parameter engineering procedures from RGB videos were performed to assess Heart Rate (HR) in beats per minute (BPM) and respiration rate (RR) in breaths per minute (BrPM): (i) using changes in luminosity of the green (G) channel and (ii) changes in the green to red (a) from the CIELAB color scale. A supervised machine learning (ML) classification model was developed using raw RR parameters as inputs to classify cutoff frequencies for low, medium, and high respiration rate (Model 1). A supervised ML regression model was developed using raw HR and RR parameters from Model 1 (Model 2). Results showed that Models 1 and 2 were highly accurate in the estimation of RR frequency level with 96% overall accuracy (Model 1), and HR and RR with R = 0.94 and slope = 0.76 (Model 2) without statistical signs of overfitting<\/jats:p>","DOI":"10.3390\/s20216334","type":"journal-article","created":{"date-parts":[[2020,11,6]],"date-time":"2020-11-06T09:09:30Z","timestamp":1604653770000},"page":"6334","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["Non-Invasive Sheep Biometrics Obtained by Computer Vision Algorithms and Machine Learning Modeling Using Integrated Visible\/Infrared Thermal Cameras"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0377-5085","authenticated-orcid":false,"given":"Sigfredo","family":"Fuentes","sequence":"first","affiliation":[{"name":"Digital Agriculture, Food and Wine Sciences Group, School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Parkville, VIC 3010, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9207-9307","authenticated-orcid":false,"given":"Claudia","family":"Gonzalez Viejo","sequence":"additional","affiliation":[{"name":"Digital Agriculture, Food and Wine Sciences Group, School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Parkville, VIC 3010, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1150-379X","authenticated-orcid":false,"given":"Surinder S.","family":"Chauhan","sequence":"additional","affiliation":[{"name":"Animal Nutrition and Physiology, Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Parkville 3010, Australia"}]},{"given":"Aleena","family":"Joy","sequence":"additional","affiliation":[{"name":"Animal Nutrition and Physiology, Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Parkville 3010, Australia"}]},{"given":"Eden","family":"Tongson","sequence":"additional","affiliation":[{"name":"Digital Agriculture, Food and Wine Sciences Group, School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Parkville, VIC 3010, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3998-1240","authenticated-orcid":false,"given":"Frank R.","family":"Dunshea","sequence":"additional","affiliation":[{"name":"Animal Nutrition and Physiology, Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Parkville 3010, Australia"},{"name":"Faculty of Biological Sciences, The University of Leeds, Leeds LS2 9JT, UK"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Rice, M., Hemsworth, L.M., Hemsworth, P.H., and Coleman, G.J. (2020). The Impact of a Negative Media Event on Public Attitudes towards Animal Welfare in the Red Meat Industry. Animals, 10.","DOI":"10.3390\/ani10040619"},{"key":"ref_2","unstructured":"Norman, G. (2020, July 11). Available online: https:\/\/www.mla.com.au\/research-and-development\/search-rd-reports\/final-report-details\/National-livestock-export-industry-sheep-cattle-and-goat-transportperformance-report2017\/3852#:~:text=National%20livestock%20export%20industry%20sheep%2C%20cattle%20and%20goat%20transport%20performance%20report%202017&text=The%20overall%20mortality%20rate%20for,of%200.80%25%20observed%20in%202016."},{"key":"ref_3","unstructured":"Davey, A., and Fisher, R. (2020, July 11). Available online: https:\/\/www.agriculture.gov.au\/sites\/default\/files\/documents\/draft-ris-animals-australia-attachment-pegasus-report.pdf."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"260","DOI":"10.14202\/vetworld.2016.260-268","article-title":"Impact of heat stress on health and performance of dairy animals: A review","volume":"9","author":"Das","year":"2016","journal-title":"Vet. World"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.crm.2017.02.001","article-title":"Climate change and livestock: Impacts, adaptation, and mitigation","volume":"16","author":"Nejadhashemi","year":"2017","journal-title":"Clim. Risk Manag."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1093\/af\/vfy030","article-title":"Impact of climate change on animal health and welfare","volume":"9","author":"Lacetera","year":"2019","journal-title":"Anim. Front."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"108025","DOI":"10.1016\/j.meatsci.2019.108025","article-title":"Effects of heat stress on animal physiology, metabolism, and meat quality: A review","volume":"162","author":"Chauhan","year":"2020","journal-title":"Meat Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"92.4","DOI":"10.4049\/jimmunol.204.Supp.92.4","article-title":"Resilience of High Immune Response (HIR) Genetics in the Context of Climate Change: Effects of Heat Stress on Cattle with Diverse Immune Response Genotypes","volume":"204","author":"Mallard","year":"2020","journal-title":"J. Immunol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1093\/af\/vfy035","article-title":"Heat stress adaptations in pigs","volume":"9","author":"Mayorga","year":"2019","journal-title":"Anim. Front."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Joy, A., Dunshea, F.R., Leury, B.J., Clarke, I.J., DiGiacomo, K., and Chauhan, S.S. (2020). Resilience of Small Ruminants to Climate Change and Increased Environmental Temperature: A Review. Animals, 10.","DOI":"10.3390\/ani10050867"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Osei-Amponsah, R., Chauhan, S.S., Leury, B.J., Cheng, L., Cullen, B., Clarke, I.J., and Dunshea, F.R. (2019). Genetic selection for thermotolerance in ruminants. Animals, 9.","DOI":"10.3390\/ani9110948"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Rashamol, V.P., Sejian, V., Bagath, M., Krishnan, G., Archana, P.R., and Bhatta, R. (2018). Physiological adaptability of livestock to heat stress: An updated review. J. Anim. Behav. Biometeorol., 6.","DOI":"10.31893\/2318-1265jabb.v6n3p62-71"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"s445","DOI":"10.1017\/S1751731118001301","article-title":"Adaptation of ruminant livestock production systems to climate changes","volume":"12","author":"Henry","year":"2018","journal-title":"Animal"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1093\/af\/vfy031","article-title":"Heat stress: Physiology of acclimation and adaptation","volume":"9","author":"Collier","year":"2019","journal-title":"Anim. Front."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1453","DOI":"10.1007\/s00484-016-1136-9","article-title":"A comparison of THI indices leads to a sensible heat-based heat stress index for shaded cattle that aligns temperature and humidity stress","volume":"60","author":"Berman","year":"2016","journal-title":"Int. J. Biometeorol."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Ekine-Dzivenu, C., Mrode, R.A., Ojango, J.M., and Okeyo Mwai, A. (2019). Evaluating the impact of heat stress as measured by temperature-humidity index (THI) on test-day milk yield of dairy cattle in Tanzania. Livestock Sci., 104314.","DOI":"10.1016\/j.livsci.2020.104314"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"10367","DOI":"10.3168\/jds.2017-13676","article-title":"A 100-Year Review: Stress physiology including heat stress","volume":"100","author":"Collier","year":"2017","journal-title":"J. Dairy Sci."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Osei-Amponsah, R., Dunshea, F.R., Leury, B.J., Cheng, L., Cullen, B., Joy, A., Abhijith, A., Zhang, M.H., and Chauhan, S.S. (2020). Heat Stress Impacts on Lactating Cows Grazing Australian Summer Pastures on an Automatic Robotic Dairy. Animals, 10.","DOI":"10.3390\/ani10050869"},{"key":"ref_19","unstructured":"Peters, R. (2019). Sensor Based Measurements of Maximum Day Temperature Effects on Eating-, Ruminating-, Lying-, Inactive Time and Number of Steps in 6 Dutch Dairy Farms. [Master\u2019s Thesis, Utrecht University]."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1111\/jpn.12379","article-title":"Heat stress effects on livestock: Molecular, cellular and metabolic aspects, a review","volume":"100","author":"Najar","year":"2016","journal-title":"J. Anim. Physiol. Anim. Nutr."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"s431","DOI":"10.1017\/S1751731118001945","article-title":"Adaptation of animals to heat stress","volume":"12","author":"Sejian","year":"2018","journal-title":"Animal"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"825","DOI":"10.1111\/jpn.12892","article-title":"Comparative assessment of growth performance of three different indigenous goat breeds exposed to summer heat stress","volume":"102","author":"Pragna","year":"2018","journal-title":"J. Anim. Physiol. Anim. Nutr."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.meatsci.2019.05.007","article-title":"Computer vision and remote sensing to assess physiological responses of cattle to pre-slaughter stress, and its impact on beef quality: A review","volume":"156","author":"Fuentes","year":"2019","journal-title":"Meat Sci."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Fuentes, S., Gonzalez Viejo, C., Cullen, B., Tongson, E., Chauhan, S.S., and Dunshea, F.R. (2020). Artificial Intelligence Applied to a Robotic Dairy Farm to Model Milk Productivity and Quality based on Cow Data and Daily Environmental Parameters. Sensors, 20.","DOI":"10.3390\/s20102975"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1016\/j.compag.2018.06.028","article-title":"Machine learning algorithms to predict core, skin, and hair-coat temperatures of piglets","volume":"151","author":"Gorczyca","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_26","first-page":"489","article-title":"A Review on Application of Deep Learning in Thermography","volume":"7","author":"Ramesh","year":"2017","journal-title":"Int. J. Eng. Manag. Res."},{"key":"ref_27","unstructured":"Lalo\u00eb, D. (2020, July 13). Available online: http:\/\/dataia.eu\/sites\/default\/files\/Outils%20com\/Livestock%20and%20AI%20-%20Denis%20Laloe.pdf."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Hoffmann, G., Herbut, P., Pinto, S., Heinicke, J., Kuhla, B., and Amon, T. (2019). Animal-related, non-invasive indicators for determining heat stress in dairy cows. Biosyst. Eng.","DOI":"10.1016\/j.biosystemseng.2019.10.017"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Barbosa Pereira, C., Dohmeier, H., Kunczik, J., Hochhausen, N., Tolba, R., and Czaplik, M. (2019). Contactless monitoring of heart and respiratory rate in anesthetized pigs using infrared thermography. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0224747"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Jorquera-Chavez, M., Fuentes, S., Dunshea, F.R., Warner, R.D., Poblete, T., Morrison, R.S., and Jongman, E.C. (2020). Remotely Sensed Imagery for Early Detection of Respiratory Disease in Pigs: A Pilot Study. Animals, 10.","DOI":"10.3390\/ani10030451"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Jorquera-Chavez, M., Fuentes, S., Dunshea, F.R., Warner, R.D., Poblete, T., and Jongman, E.C. (2019). Modelling and Validation of Computer Vision Techniques to Assess Heart Rate, Eye Temperature, Ear-Base Temperature and Respiration Rate in Cattle. Animals, 9.","DOI":"10.3390\/ani9121089"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Subramanian, N., Chaudhuri, A., and Kayikci, Y. (2020). Blockchain Applications in Food Supply Chain. Blockchain and Supply Chain Logistics, Palgrave Pivot.","DOI":"10.1007\/978-3-030-47531-4"},{"key":"ref_33","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_34","unstructured":"National Research Council (2007). Nutrient Requirements of Small Ruminants: Sheep, Goats, Cervids, and New World Camelids, The National Academies Press."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.smallrumres.2006.10.003","article-title":"Physiological traits as affected by heat stress in sheep\u2014A review","volume":"71","author":"Marai","year":"2007","journal-title":"Small Rumin. Res."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Aubakir, B., Nurimbetov, B., Tursynbek, I., and Varol, H.A. (2016, January 16\u201320). Vital sign monitoring utilizing Eulerian video magnification and thermography. Proceedings of the 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA.","DOI":"10.1109\/EMBC.2016.7591489"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"4711","DOI":"10.1109\/ACCESS.2017.2678521","article-title":"Heart rate variability extraction from videos signals: ICA vs. EVM comparison","volume":"5","author":"Alghoul","year":"2017","journal-title":"IEEE Access"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Gonzalez Viejo, C., Fuentes, S., Torrico, D.D., and Dunshea, F.R. (2018). Non-contact heart rate and blood pressure estimations from video analysis and machine learning modelling applied to food sensory responses: A case study for chocolate. Sensors, 18.","DOI":"10.3390\/s18061802"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Gonzalez Viejo, C., Torrico, D., Dunshea, F., and Fuentes, S. (2019). Development of Artificial Neural Network Models to Assess Beer Acceptability Based on Sensory Properties Using a Robotic Pourer: A Comparative Model Approach to Achieve an Artificial Intelligence System. Beverages, 5.","DOI":"10.3390\/beverages5020033"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1111\/j.1365-2389.2005.0698.x","article-title":"Neural network models to predict cation exchange capacity in arid regions of Iran","volume":"56","author":"Amini","year":"2005","journal-title":"Eur. J. Soil Sci."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1007\/s40092-016-0146-x","article-title":"On the use of back propagation and radial basis function neural networks in surface roughness prediction","volume":"12","author":"Markopoulos","year":"2016","journal-title":"J. Ind. Eng. Int."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Deep, K., Jain, M., and Salhi, S. (2019). Logistics, Supply Chain and Financial Predictive Analytics: Theory and Practices, Springer.","DOI":"10.1007\/978-981-13-0872-7"},{"key":"ref_43","first-page":"311","article-title":"Studies of the vascular arrangements of the nose","volume":"87","author":"Dawes","year":"1953","journal-title":"J. Anat."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1002\/(SICI)1097-4687(199809)237:3<275::AID-JMOR5>3.0.CO;2-Y","article-title":"Histological studies of the dorsal nasal, angularis oculi, and facial veins of sheep (Ovis aries)","volume":"237","author":"Mitchell","year":"1998","journal-title":"J. Morphol."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/S0921-4488(99)00123-6","article-title":"Comparative electrocardiographic studies, and differing effects of pentazocine on ECG, heart and respiratory rates in young sheep and goats","volume":"37","author":"Mir","year":"2000","journal-title":"Small Rumin. Res."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Konold, T., and Bone, G.E. (2011). Heart rate variability analysis in sheep affected by transmissible spongiform encephalopathies. BMC Res. Notes, 4.","DOI":"10.1186\/1756-0500-4-539"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"973","DOI":"10.1007\/s00484-019-01711-3","article-title":"A panting score index for sheep","volume":"63","author":"Lees","year":"2019","journal-title":"Int. J. Biometeorol."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Sejian, V., Bhatta, R., Gaughan, J., Malik, P.K., Naqvi, S., and Lal, R. (2017). Sheep Production Adapting to Climate Change, Springer.","DOI":"10.1007\/978-981-10-4714-5"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.smallrumres.2019.02.009","article-title":"Genes for resilience to heat stress in small ruminants: A review","volume":"173","author":"Sejian","year":"2019","journal-title":"Small Rumin. Res."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.smallrumres.2016.12.039","article-title":"Conditions to evaluate differences among individual sheep and goats in resilience to high heat load index","volume":"147","author":"Mengistu","year":"2017","journal-title":"Small Rumin. Res."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Psota, E.T., Mittek, M., P\u00e9rez, L.C., Schmidt, T., and Mote, B. (2019). Multi-pig part detection and association with a fully-convolutional network. Sensors, 19.","DOI":"10.3390\/s19040852"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Nguyen, H., Maclagan, S.J., Nguyen, T.D., Nguyen, T., Flemons, P., Andrews, K., Ritchie, E.G., and Phung, D. (2017, January 19\u201321). Animal recognition and identification with deep convolutional neural networks for automated wildlife monitoring. Proceedings of the IEEE international conference on data science and advanced Analytics (DSAA), Tokyo, Japan.","DOI":"10.1109\/DSAA.2017.31"},{"key":"ref_53","unstructured":"Zhuang, P., Xing, L., Liu, Y., Guo, S., and Qiao, Y. (2017, January 11\u201314). Marine Animal Detection and Recognition with Advanced Deep Learning Models. Proceedings of the Working Notes of CLEF 2017\u2014Conference and Labs of the Evaluation Forum, Dublin, Ireland."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"eaaw0736","DOI":"10.1126\/sciadv.aaw0736","article-title":"Chimpanzee face recognition from videos in the wild using deep learning","volume":"5","author":"Schofield","year":"2019","journal-title":"Sci. Adv."},{"key":"ref_55","unstructured":"Tyd\u00e9n, A., and Olsson, S. (2020). Edge Machine Learning for Animal Detection, Classification, and Tracking. [Master\u2019s Thesis, Department of Electrical Engineering, Link\u00f6ping University]. Automatic Control."},{"key":"ref_56","first-page":"218","article-title":"A new face recognition method using PCA, LDA and neural network","volume":"2","author":"Hossein","year":"2008","journal-title":"Int. J. Comput. Sci. Eng."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"313","DOI":"10.13031\/2013.22395","article-title":"A preliminary investigation on face recognition as a biometric identifier of sheep","volume":"50","author":"Corkery","year":"2007","journal-title":"Trans. Asabe"},{"key":"ref_58","first-page":"1945","article-title":"Biometric recognition for pet animal","volume":"2014","author":"Kumar","year":"2014","journal-title":"J. Softw. Eng. Appl."},{"key":"ref_59","unstructured":"Noviyanto, A., and Arymurthy, A.M. (2007, January 13\u201317). Automatic cattle identification based on muzzle photo using speed-up robust features approach. Proceedings of the 3rd European Conference of Computer Science, Heraklion, Crete, Greece."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.compag.2006.12.003","article-title":"Virtual fencing applications: Implementing and testing an automated cattle control system","volume":"56","author":"Swain","year":"2007","journal-title":"Comput. Electron. Agric."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Campbell, D.L., Ouzman, J., Mowat, D., Lea, J.M., Lee, C., and Llewellyn, R.S. (2020). Virtual Fencing Technology Excludes Beef Cattle from an Environmentally Sensitive Area. Animals, 10.","DOI":"10.3390\/ani10061069"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Marini, D., Meuleman, M.D., Belson, S., Rodenburg, T.B., Llewellyn, R., and Lee, C. (2018). Developing an ethically acceptable virtual fencing system for sheep. Animals, 8.","DOI":"10.3390\/ani8030033"},{"key":"ref_63","first-page":"1","article-title":"The ability of ewes with lambs to learn a virtual fencing system","volume":"2017","author":"Brunberg","year":"2017","journal-title":"Animal"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"187","DOI":"10.3389\/fvets.2018.00187","article-title":"A framework to assess the impact of new animal management technologies on welfare: A case study of virtual fencing","volume":"5","author":"Lee","year":"2018","journal-title":"Front. Vet. Sci."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/21\/6334\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:30:04Z","timestamp":1760178604000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/21\/6334"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,6]]},"references-count":64,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2020,11]]}},"alternative-id":["s20216334"],"URL":"https:\/\/doi.org\/10.3390\/s20216334","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,11,6]]}}}