{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T01:00:21Z","timestamp":1775869221983,"version":"3.50.1"},"reference-count":53,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2024,9,21]],"date-time":"2024-09-21T00:00:00Z","timestamp":1726876800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"French Ministry for Europe and Foreign Affairs","award":["47060PG"],"award-info":[{"award-number":["47060PG"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Crop monitoring is a fundamental practice in seaweed aquaculture. Seaweeds are vulnerable to several threats such as ice-ice disease (IID) causing a whitening of the thallus due to depigmentation. Crop condition assessment is important for minimizing yield losses and improving the biosecurity of seaweed farms. The recent influence of modern technology has resulted in the development of precision aquaculture. The present study focuses on the exploitation of spectral reflectance in the visible and near-infrared regions for characterizing the crop condition of two of the most cultivated Eucheumatoids species: Kappaphycus alvareezi and Eucheuma denticulatum. In particular, the influence of spectral resolution is examined towards discriminating: (a) species and morphotypes, (b) different levels of seaweed health (i.e., from healthy to completely depigmented) and (c) depigmented from silted specimens (thallus covered by a thin layer of sediment). Two spectral libraries were built at different spectral resolutions (5 and 45 spectral bands) using in situ data. In addition, proximal multispectral imagery using a drone-based sensor was utilised. At each experimental scenario, the spectral data were classified using a Random Forest algorithm for crop condition identification. The results showed good discrimination (83\u201399% overall accuracy) for crop conditions and morphotypes regardless of spectral resolution. According to the importance scores of the hyperspectral data, useful wavelengths were identified for discriminating healthy seaweeds from seaweeds with varying symptoms of IID (i.e., thalli whitening). These wavelengths assisted in selecting a set of vegetation indices for testing their ability to improve crop condition characterisation. Specifically, five vegetation indices (the RBNDVI, GLI, Hue, Green\u2013Red ratio and NGRDI) were found to improve classification accuracy, making them recommended for seaweed health monitoring. Image-based classification demonstrated that multispectral library data can be extended to photomosaics to assess seaweed conditions on a broad scale. The results of this study suggest that proximal sensing is a first step towards effective seaweed crop monitoring, enhancing yield and contributing to aquaculture biosecurity.<\/jats:p>","DOI":"10.3390\/rs16183502","type":"journal-article","created":{"date-parts":[[2024,9,23]],"date-time":"2024-09-23T09:15:07Z","timestamp":1727082907000},"page":"3502","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Proximal Sensing for Characterising Seaweed Aquaculture Crop Conditions: Optical Detection of Ice-Ice Disease"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7276-8666","authenticated-orcid":false,"given":"Evangelos","family":"Alevizos","sequence":"first","affiliation":[{"name":"Institut des Substances et Organismes de la Mer (ISOMer), Nantes Universit\u00e9, UR 2160, F-44000 Nantes, France"}]},{"given":"Nurjannah","family":"Nurdin","sequence":"additional","affiliation":[{"name":"Marine Science Department, Marine Science and Fisheries Faculty, Hasanuddin University, Makassar 90245, Indonesia"},{"name":"Research and Development Center for Marine, Coast, and Small Island, Hasanuddin University, Jl. Perintis Kemerdekaan Km.10, Makassar 90245, Indonesia"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-2879-030X","authenticated-orcid":false,"given":"Agus","family":"Aris","sequence":"additional","affiliation":[{"name":"Research and Development Center for Marine, Coast, and Small Island, Hasanuddin University, Jl. Perintis Kemerdekaan Km.10, Makassar 90245, Indonesia"},{"name":"Department of Remote Sensing and Geographic Information Systems, Vocational Faculty, Hasanuddin University, Jl. Perintis Kemerdekaan Km.10, Makassar 90245, Indonesia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5138-2684","authenticated-orcid":false,"given":"Laurent","family":"Barill\u00e9","sequence":"additional","affiliation":[{"name":"Institut des Substances et Organismes de la Mer (ISOMer), Nantes Universit\u00e9, UR 2160, F-44000 Nantes, France"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,21]]},"reference":[{"key":"ref_1","unstructured":"Cai, J. (2021). Seaweeds and Microalgae: An Overview for Unlocking Their Potential in Global Aquaculture Development, FAO. FAO Fisheries and Aquaculture Circular."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Hurtado, A.Q., Critchley, A.T., and Neish, I.C. (2017). Carrageenan Industry Market Overview. Tropical Seaweed Farming Trends, Problems and Opportunities: Focus on Kappaphycus and Eucheuma of Commerce, Springer International Publishing. Developments in Applied Phycology.","DOI":"10.1007\/978-3-319-63498-2"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Bell, T.W., Nidzieko, N.J., Siegel, D.A., Miller, R.J., Cavanaugh, K.C., Nelson, N.B., Reed, D.C., Fedorov, D., Moran, C., and Snyder, J.N. (2020). The Utility of Satellites and Autonomous Remote Sensing Platforms for Monitoring Offshore Aquaculture Farms: A Case Study for Canopy Forming Kelps. Front. Mar. Sci., 7.","DOI":"10.3389\/fmars.2020.520223"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"104431","DOI":"10.1016\/j.marpol.2021.104431","article-title":"Monitoring the COVID-19-Affected Indonesian Seaweed Industry Using Remote Sensing Data","volume":"127","author":"Langford","year":"2021","journal-title":"Mar. Policy"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Meng, D., Yang, X., Wang, Z., Liu, Y., Zhang, J., Liu, X., and Liu, B. (2024). Spatial Distribution and Differentiation Analysis of Coastal Aquaculture in China Based on Remote Sensing Monitoring. Remote Sens., 16.","DOI":"10.3390\/rs16091585"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1051\/alr\/2021015","article-title":"Mapping and Estimating Harvest Potential of Seaweed Culture Using Worldview-2 Satellite Images: A Case Study in Nusa Lembongan, Bali \u2212 Indonesia","volume":"34","author":"Pratama","year":"2021","journal-title":"Aquat. Living Resour."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2307","DOI":"10.1007\/s10811-017-1200-9","article-title":"In Situ Variability of Carrageenan Content and Biomass in the Cultivated Red Macroalga Kappaphycus alvarezii with an Estimation of Its Carrageenan Stock at the Scale of the Malasoro Bay (Indonesia) Using Satellite Image Processing","volume":"29","author":"Setyawidati","year":"2017","journal-title":"J. Appl. Phycol."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Hurtado, A.Q., Critchley, A.T., and Neish, I.C. (2017). Tropical Seaweed Farming Trends, Problems and Opportunities: Focus on Kappaphycus and Eucheuma of Commerce, Springer International Publishing.","DOI":"10.1007\/978-3-319-63498-2"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Nurdin, N., Alevizos, E., Syamsuddin, R., Asis, H., Zainuddin, E.N., Aris, A., Oiry, S., Brunier, G., Komatsu, T., and Barill\u00e9, L. (2023). Precision Aquaculture Drone Mapping of the Spatial Distribution of Kappaphycus alvarezii Biomass and Carrageenan. Remote Sens., 15.","DOI":"10.3390\/rs15143674"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Ubina, N.A., and Cheng, S.-C. (2022). A Review of Unmanned System Technologies with Its Application to Aquaculture Farm Monitoring and Management. Drones, 6.","DOI":"10.3390\/drones6010012"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Kumar, Y.N., Poong, S.-W., Gachon, C., Brodie, J., Sade, A., and Lim, P.-E. (2020). Impact of Elevated Temperature on the Physiological and Biochemical Responses of Kappaphycus alvarezii (Rhodophyta). PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0239097"},{"key":"ref_12","unstructured":"Neish, I.C. (2008). Monograph No. HB2F 1008 V3 GAP, SEAPlant.net."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Hurtado, A.Q., Critchley, A.T., and Neish, I.C. (2017). Impacts of Climate Change on Eucheuma-Kappaphycus Farming. Tropical Seaweed Farming Trends, Problems and Opportunities: Focus on Kappaphycus and Eucheuma of Commerce, Springer International Publishing. Developments in Applied Phycology.","DOI":"10.1007\/978-3-319-63498-2"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1975","DOI":"10.1007\/s10811-014-0507-z","article-title":"Observations on Pests and Diseases Affecting a Eucheumatoid Farm in China","volume":"27","author":"Pang","year":"2015","journal-title":"J. Appl. Phycol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"815","DOI":"10.1111\/jwas.12649","article-title":"A Review of Reported Seaweed Diseases and Pests in Aquaculture in Asia","volume":"51","author":"Ward","year":"2020","journal-title":"J. World Aquac. Soc."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Hurtado, A.Q., Critchley, A.T., and Neish, I.C. (2017). The Cultivation of Kappaphycus and Eucheuma in Tropical and Sub-Tropical Waters. Tropical Seaweed Farming Trends, Problems and Opportunities: Focus on Kappaphycus and Eucheuma of Commerce, Springer International Publishing. Developments in Applied Phycology.","DOI":"10.1007\/978-3-319-63498-2"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Hurtado, A.Q., Critchley, A.T., and Neish, I.C. (2017). Impacts of AMPEP on Epiphytes and Diseases in Kappaphycus and Eucheuma Cultivation. Tropical Seaweed Farming Trends, Problems and Opportunities: Focus on Kappaphycus and Eucheuma of Commerce, Springer International Publishing. Developments in Applied Phycology.","DOI":"10.1007\/978-3-319-63498-2"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2147","DOI":"10.1007\/s10811-019-02020-3","article-title":"An Analysis of the Current Status and Future of Biosecurity Frameworks for the Indonesian Seaweed Industry","volume":"32","author":"Kambey","year":"2020","journal-title":"J. Appl. Phycol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"7401","DOI":"10.1038\/s41467-022-34783-8","article-title":"A New Progressive Management Pathway for Improving Seaweed Biosecurity","volume":"13","author":"Cabarubias","year":"2022","journal-title":"Nat. Commun."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1186\/s13007-021-00711-y","article-title":"Biomass Estimation of Cultivated Red Algae Pyropia Using Unmanned Aerial Platform Based Multispectral Imaging","volume":"17","author":"Che","year":"2021","journal-title":"Plant Methods"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Solvang, T., Bale, E.S., Broch, O.J., Hand\u00e5, A., and Alver, M.O. (2021). Automation Concepts for Industrial-Scale Production of Seaweed. Front. Mar. Sci., 8.","DOI":"10.3389\/fmars.2021.613093"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"111279","DOI":"10.1016\/j.rse.2019.111279","article-title":"Monitoring Seaweed Aquaculture in the Yellow Sea with Multiple Sensors for Managing the Disaster of Macroalgal Blooms","volume":"231","author":"Xing","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Tait, L., Bind, J., Charan-Dixon, H., Hawes, I., Pirker, J., and Schiel, D. (2019). Unmanned Aerial Vehicles (UAVs) for Monitoring Macroalgal Biodiversity: Comparison of RGB and Multispectral Imaging Sensors for Biodiversity Assessments. Remote Sens., 11.","DOI":"10.3390\/rs11192332"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"169789","DOI":"10.1016\/j.scitotenv.2023.169789","article-title":"Innovative Spectral Characterisation of Beached Pelagic Sargassum towards Remote Estimation of Biochemical and Phenotypic Properties","volume":"914","author":"Fidai","year":"2024","journal-title":"Sci. Total Environ."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Chandler, C.J., \u00c1vila-Mosqueda, S.V., Salas-Acosta, E.R., Maga\u00f1a-Gallegos, E., Mancera, E.E., Reali, M.A.G., de la Barreda-Bautista, B., Boyd, D.S., Metcalfe, S.E., and Sjogersten, S. (2023). Spectral Characteristics of Beached Sargassum in Response to Drying and Decay over Time. Remote Sens., 15.","DOI":"10.3390\/rs15174336"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.csr.2013.01.010","article-title":"Assessment of the Hyperspectral Sensor CASI-2 for Macroalgal Discrimination on the R\u00eda de Vigo Coast (NW Spain) Using Field Spectroscopy and Modelled Spectral Libraries","volume":"55","author":"Casal","year":"2013","journal-title":"Cont. Shelf Res."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1016\/j.algal.2017.04.021","article-title":"A Comparison of Spectral Macroalgae Taxa Separability Methods Using an Extensive Spectral Library","volume":"26","year":"2017","journal-title":"Algal Res."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Douay, F., Verpoorter, C., Duong, G., Spilmont, N., and Gevaert, F. (2022). New Hyperspectral Procedure to Discriminate Intertidal Macroalgae. Remote Sens., 14.","DOI":"10.3390\/rs14020346"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Fuller, K., Martin, R.E., and Asner, G.P. (2024). Spectral Signatures of Macroalgae on Hawaiian Reefs. Remote Sens., 16.","DOI":"10.3390\/rs16071140"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Olmedo-Masat, O.M., Raffo, M.P., Rodr\u00edguez-P\u00e9rez, D., Arij\u00f3n, M., and S\u00e1nchez-Carnero, N. (2020). How Far Can We Classify Macroalgae Remotely? An Example Using a New Spectral Library of Species from the South West Atlantic (Argentine Patagonia). Remote Sens., 12.","DOI":"10.3390\/rs12233870"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"113554","DOI":"10.1016\/j.rse.2023.113554","article-title":"Multi- and Hyperspectral Classification of Soft-Bottom Intertidal Vegetation Using a Spectral Library for Coastal Biodiversity Remote Sensing","volume":"290","author":"Davies","year":"2023","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1353691","DOI":"10.1155\/2017\/1353691","article-title":"Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications","volume":"2017","author":"Xue","year":"2017","journal-title":"J. Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.01.011","article-title":"Random Forest in Remote Sensing: A Review of Applications and Future Directions","volume":"114","author":"Belgiu","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Brunier, G., Oiry, S., Gruet, Y., Dubois, S.F., and Barill\u00e9, L. (2022). Topographic Analysis of Intertidal Polychaete Reefs (Sabellaria alveolata) at a Very High Spatial Resolution. Remote Sens., 14.","DOI":"10.3390\/rs14020307"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"107184","DOI":"10.1016\/j.ecolind.2020.107184","article-title":"Using Sentinel-2 Satellite Imagery to Develop Microphytobenthos-Based Water Quality Indices in Estuaries","volume":"121","author":"Oiry","year":"2021","journal-title":"Ecol. Indic."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Stephens, D., and Diesing, M. (2014). A Comparison of Supervised Classification Methods for the Prediction of Substrate Type Using Multibeam Acoustic and Legacy Grain-Size Data. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0093950"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"4201515","DOI":"10.1109\/TGRS.2021.3071154","article-title":"A Random Forest-Based Algorithm to Distinguish Ulva Prolifera and Sargassum From Multispectral Satellite Images","volume":"60","author":"Xiao","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/j.isprsjprs.2014.06.005","article-title":"Applying Data Fusion Techniques for Benthic Habitat Mapping and Monitoring in a Coral Reef Ecosystem","volume":"104","author":"Zhang","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Strobl, C., Boulesteix, A.-L., Kneib, T., Augustin, T., and Zeileis, A. (2008). Conditional Variable Importance for Random Forests. BMC Bioinformatics, 9.","DOI":"10.1186\/1471-2105-9-307"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Hill, J., and M\u00e9gier, J. (1994). Soil Spectral Properties and Their Relationships with Environmental Parameters\u2014Examples from Arid Regions. Imaging Spectrometry\u2014A Tool for Environmental Observations, Springer.","DOI":"10.1007\/978-0-585-33173-7"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Cavanaugh, K.C., Cavanaugh, K.C., Bell, T.W., and Hockridge, E.G. (2021). An Automated Method for Mapping Giant Kelp Canopy Dynamics from UAV. Front. Environ. Sci., 8.","DOI":"10.3389\/fenvs.2020.587354"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/0034-4257(79)90013-0","article-title":"Red and Photographic Infrared Linear Combinations for Monitoring Vegetation","volume":"8","author":"Tucker","year":"1979","journal-title":"Remote Sens. Environ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1080\/10106040108542184","article-title":"Spatially Located Platform and Aerial Photography for Documentation of Grazing Impacts on Wheat","volume":"16","author":"Louhaichi","year":"2001","journal-title":"Geocarto Int."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1491","DOI":"10.1109\/36.934080","article-title":"Scaling-up and Model Inversion Methods with Narrowband Optical Indices for Chlorophyll Content Estimation in Closed Forest Canopies with Hyperspectral Data","volume":"39","author":"Miller","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/S0034-4257(02)00096-2","article-title":"Overview of the Radiometric and Biophysical Performance of the MODIS Vegetation Indices","volume":"83","author":"Huete","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"278","DOI":"10.2307\/2657019","article-title":"Estimating Near-Infrared Leaf Reflectance from Leaf Structural Characteristics","volume":"88","author":"Slaton","year":"2001","journal-title":"Am. J. Bot."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Seckbach, J., Einav, R., and Israel, A. (2010). A Review of Kappaphycus Farming: Prospects and Constraints. Seaweeds and their Role in Globally Changing Environments, Springer.","DOI":"10.1007\/978-90-481-8569-6"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1515\/botm.1992.35.3.189","article-title":"Irradiance Acclimation of the Cultured Philippine Seaweeds, Kappaphycus alvarezii and Eucheuma denticulatum","volume":"35","author":"Dawes","year":"1992","journal-title":"Bot. Mar."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"2009","DOI":"10.1007\/s10811-020-02083-7","article-title":"Analysis of Biosecurity-Related Policies Governing the Seaweed Industry of the Philippines","volume":"32","author":"Mateo","year":"2020","journal-title":"J. Appl. Phycol."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"2118","DOI":"10.1016\/j.rse.2009.05.012","article-title":"A Novel Ocean Color Index to Detect Floating Algae in the Global Oceans","volume":"113","author":"Hu","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1007\/s10661-007-9855-3","article-title":"Remote Sensing of Aquatic Vegetation: Theory and Applications","volume":"140","author":"Silva","year":"2008","journal-title":"Environ. Monit. Assess."},{"key":"ref_52","unstructured":"Selvaraj, S. (2021). Development of Novel Image Analysis Approaches for Seaweed Discrimination\u2014Species Level Study Using Field Spectroscopy and UAV Multispectral Remote Sensing. [Ph.D. Thesis, Auckland University of Technology]."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"342","DOI":"10.1016\/j.rse.2006.01.009","article-title":"Feasibility of Hyperspectral Remote Sensing for Mapping Benthic Macroalgal Cover in Turbid Coastal Waters\u2014A Baltic Sea Case Study","volume":"101","author":"Kutser","year":"2006","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/18\/3502\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:01:34Z","timestamp":1760112094000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/18\/3502"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,21]]},"references-count":53,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["rs16183502"],"URL":"https:\/\/doi.org\/10.3390\/rs16183502","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,21]]}}}