{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T19:50:13Z","timestamp":1768074613333,"version":"3.49.0"},"reference-count":60,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2025,12,6]],"date-time":"2025-12-06T00:00:00Z","timestamp":1764979200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Water"],"abstract":"<jats:p>Hydrochar is a carbon-rich material produced through the hydrothermal carbonization (HTC) of wet biomass such as sewage sludge. Its nitrogen content is a critical quality parameter, influencing its suitability for use as a soil amendment and its potential environmental impacts. This study develops a high-accuracy ensemble machine learning framework to predict the nitrogen content of hydrochar derived from sewage sludge based on feedstock compositions and HTC process conditions. Four ensemble algorithms\u2014Gradient Boosting Regression Trees (GBRTs), AdaBoost, Light Gradient Boosting Machine (LightGBM), and eXtreme Gradient Boosting (XGBoost)\u2014were trained using an 80\/20 train\u2013test split and evaluated through standard statistical metrics. GBRT and XGBoost provided the best performance, achieving R2 values of 0.993 and 0.989 and RMSE values of 0.169 and 0.213 during training, while maintaining strong predictive capabilities on the test dataset. SHAP analyses identified nitrogen content, ash content, and heating temperature as the most influential predictors of hydrochar nitrogen levels. Predicting nitrogen behaviour during HTC is environmentally relevant, as the improper management of nitrogen-rich hydrochar residues can contribute to nitrogen leaching, eutrophication, and disruption of aquatic biogeochemical cycles. The proposed ensemble-based modelling approach therefore offers a reliable tool for optimizing HTC operations, supporting sustainable sludge valorisation, and reducing environmental risks associated with nitrogen emissions.<\/jats:p>","DOI":"10.3390\/w17243468","type":"journal-article","created":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T09:17:36Z","timestamp":1765185456000},"page":"3468","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Robust Ensemble-Based Model and Web Application for Nitrogen Content Prediction in Hydrochar from Sewage Sludge"],"prefix":"10.3390","volume":"17","author":[{"given":"Esraa Q.","family":"Shehab","sequence":"first","affiliation":[{"name":"Department of Civil Engineering, College of Engineering, University of Diyala, Baqubah 32001, Iraq"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0997-6398","authenticated-orcid":false,"given":"Nadia Moneem","family":"Al-Abdaly","sequence":"additional","affiliation":[{"name":"Construction and Building Engineering Technologies Department, Najaf Engineering Technical Colleges, Al-Furat-Al-Awsat Technical University, Najaf 54003, Iraq"}]},{"given":"Mohammed E.","family":"Seno","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Al-Maarif University College (AUC), Ramadi 31001, Iraq"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0680-8540","authenticated-orcid":false,"given":"Hamza","family":"Imran","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, College of Engineering, Al-Karkh University, Baghdad 10081, Iraq"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7099-0685","authenticated-orcid":false,"given":"Antonio","family":"Albuquerque","sequence":"additional","affiliation":[{"name":"GeoBioTec, Department of Civil Engineering and Architecture, University of Beira Interior, 6201-001 Covilh\u00e3, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"110283","DOI":"10.1016\/j.rser.2020.110283","article-title":"Towards hydrogen production from waste activated sludge: Principles, challenges and perspectives","volume":"135","author":"Fu","year":"2021","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.jaap.2017.04.018","article-title":"Co-pyrolysis of sewage sludge and sawdust\/rice straw for the production of biochar","volume":"125","author":"Huang","year":"2017","journal-title":"J. Anal. Appl. Pyrolysis"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"109643","DOI":"10.1016\/j.jenvman.2019.109643","article-title":"Life cycle environmental impacts of sewage sludge treatment methods for resource recovery considering ecotoxicity of heavy metals and pharmaceutical and personal care products","volume":"260","author":"Tarpani","year":"2020","journal-title":"J. Environ. Manag."},{"key":"ref_4","unstructured":"Ministry of Housing and Urban-Rural Development (2020). Statistical Yearbook of Urban and Rural Construction in China, Ministry of Housing and Urban-Rural Development."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"991","DOI":"10.1016\/j.biortech.2015.10.099","article-title":"The migration and transformation behaviors of heavy metals during the hydrothermal treatment of sewage sludge","volume":"200","author":"Huang","year":"2016","journal-title":"Bioresour. Technol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"110824","DOI":"10.1016\/j.jenvman.2020.110824","article-title":"Effects of rice straw\/wood sawdust addition on the transport\/conversion behaviors of heavy metals during the liquefaction of sewage sludge","volume":"270","author":"Xiao","year":"2020","journal-title":"J. Environ. Manag."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"139660","DOI":"10.1016\/j.jclepro.2023.139660","article-title":"Analysis of fuel properties of hydrochar derived from food waste and biomass: Evaluating varied mixing techniques pre\/post-hydrothermal carbonization","volume":"430","author":"Wu","year":"2023","journal-title":"J. Clean. Prod."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"142049","DOI":"10.1016\/j.chemosphere.2024.142049","article-title":"Feasibility study of Aesculus turbinata fruit shell-derived biochar for ammonia removal in wastewater and its subsequent use as nitrogen fertilizer","volume":"357","author":"Lee","year":"2024","journal-title":"Chemosphere"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Merzari, F., Goldfarb, J., Andreottola, G., Mimmo, T., Volpe, M., and Fiori, L. (2020). Hydrothermal carbonization as a strategy for sewage sludge management: Influence of process withdrawal point on hydrochar properties. Energies, 13.","DOI":"10.3390\/en13112890"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"118472","DOI":"10.1016\/j.fuel.2020.118472","article-title":"Hydrothermal carbonization of organic wastes to carbonaceous solid fuel\u2013A review of mechanisms and process parameters","volume":"279","author":"Pauline","year":"2020","journal-title":"Fuel"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Petrovi\u0107, J., Ercegovi\u0107, M., Simi\u0107, M., Koprivica, M., Dimitrijevi\u0107, J., Jovanovi\u0107, A., and Jankovi\u0107 Panti\u0107, J. (2024). Hydrothermal carbonization of waste biomass: A review of hydrochar preparation and environmental application. Processes, 12.","DOI":"10.3390\/pr12010207"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1016\/j.cej.2019.03.102","article-title":"A facile one-pot hydrothermal synthesis of hydroxyapatite\/biochar nanocomposites: Adsorption behavior and mechanisms for the removal of copper (II) from aqueous media","volume":"369","author":"Jung","year":"2019","journal-title":"Chem. Eng. J."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Tsarpali, M., Kuhn, J.N., and Philippidis, G.P. (2022). Hydrothermal carbonization of residual algal biomass for production of hydrochar as a biobased metal adsorbent. Sustainability, 14.","DOI":"10.3390\/su14010455"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Xue, Y., Wang, Z., Wu, Y., Wu, R., and Zhao, F. (2023). Migration and conversion of phosphorus in hydrothermal carbonization of municipal sludge with hydrochloric acid. Sustainability, 15.","DOI":"10.21203\/rs.3.rs-2520146\/v1"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Kapetanakis, T.N., Vardiambasis, I.O., Nikolopoulos, C.D., Konstantaras, A.I., Trang, T.K., Khuong, D.A., Tsubota, T., Keyikoglu, R., Khataee, A., and Kalderis, D. (2021). Towards engineered hydrochars: Application of artificial neural networks in the hydrothermal carbonization of sewage sludge. Energies, 14.","DOI":"10.3390\/en14113000"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Devnath, B., Khanal, S., Shah, A., and Reza, T. (2024). Influence of Hydrothermal Carbonization (HTC) Temperature on Hydrochar and Process Liquid for Poultry, Swine, and Dairy Manure. Environments, 11.","DOI":"10.3390\/environments11070150"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Langone, M., and Basso, D. (2020). Process waters from hydrothermal carbonization of sludge: Characteristics and possible valorization pathways. Int. J. Environ. Res. Public Health, 17.","DOI":"10.3390\/ijerph17186618"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ho, T.T.T., Nadeem, A., and Choe, K. (2024). A review of upscaling hydrothermal carbonization. Energies, 17.","DOI":"10.3390\/en17081918"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Chang, M.Y., and Huang, W.J. (2020). A Practical Case Report on the Node Point of a Butterfly Model Circular Economy: Synthesis of a New Hybrid Mineral\u2013Hydrothermal Fertilizer for Rice Cropping. Sustainability, 12.","DOI":"10.3390\/su12031245"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1016\/j.jclepro.2017.12.117","article-title":"Application of Mg\u2013Al-modified biochar for simultaneous removal of ammonium, nitrate, and phosphate from eutrophic water","volume":"176","author":"Yin","year":"2018","journal-title":"J. Clean. Prod."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.biortech.2013.03.057","article-title":"Engineered carbon (biochar) prepared by direct pyrolysis of Mg-accumulated tomato tissues: Characterization and phosphate removal potential","volume":"138","author":"Yao","year":"2013","journal-title":"Bioresour. Technol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"576","DOI":"10.1016\/j.chemosphere.2015.06.084","article-title":"Reduced nitrification and abundance of ammonia-oxidizing bacteria in acidic soil amended with biochar","volume":"138","author":"Wang","year":"2015","journal-title":"Chemosphere"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.jclepro.2017.01.069","article-title":"Simultaneous capture removal of phosphate, ammonium and organic substances by MgO impregnated biochar and its potential use in swine wastewater treatment","volume":"147","author":"Li","year":"2017","journal-title":"J. Clean. Prod."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Xiong, J., Chen, S., Wang, J., Wang, Y., Fang, X., and Huang, H. (2021). Speciation of main nutrients (N\/P\/K) in hydrochars produced from the hydrothermal carbonization of swine manure under different reaction temperatures. Materials, 14.","DOI":"10.3390\/ma14154114"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Roslan, S.Z., Zainudin, S.F., Mohd Aris, A., Chin, K.B., Musa, M., Mohamad Daud, A.R., and Syed Hassan, S.S.A. (2023). Hydrothermal carbonization of sewage sludge into solid biofuel: Influences of process conditions on the energetic properties of hydrochar. Energies, 16.","DOI":"10.3390\/en16052483"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Wang, X., Duo, J., Jin, Z., Yang, F., Lai, T., and Collins, E. (2025). Effects of Hydrothermal Carbonization Conditions on the Characteristics of Hydrochar and Its Application as a Soil Amendment: A Review. Agronomy, 15.","DOI":"10.3390\/agronomy15020327"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"143679","DOI":"10.1016\/j.scitotenv.2020.143679","article-title":"A review on nitrogen transformation in hydrochar during hydrothermal carbonization of biomass containing nitrogen","volume":"756","author":"Leng","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"864","DOI":"10.1002\/open.202000148","article-title":"Nitrogen-Containing Hydrochar: The Influence of Nitrogen-Containing Compounds on the Hydrochar Formation","volume":"9","author":"Alhnidi","year":"2020","journal-title":"ChemistryOpen"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"123295","DOI":"10.1016\/j.energy.2022.123295","article-title":"Random forest-based modeling for insights on phosphorus content in hydrochar produced from hydrothermal carbonization of sewage sludge","volume":"245","author":"Djandja","year":"2022","journal-title":"Energy"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"113538","DOI":"10.1016\/j.jece.2024.113538","article-title":"Machine learning and experiments on hydrothermal liquefaction of sewage sludge: Insight into migration and transformation mechanisms of phosphorus","volume":"12","author":"Zheng","year":"2024","journal-title":"J. Environ. Chem. Eng."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"123928","DOI":"10.1016\/j.jclepro.2020.123928","article-title":"Multi-task prediction and optimization of hydrochar properties from high-moisture municipal solid waste: Application of machine learning on waste-to-resource","volume":"278","author":"Li","year":"2021","journal-title":"J. Clean. Prod."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Li, J., Zhu, X., Li, Y., Tong, Y., and Wang, X. (2019, January 16\u201318). Multi-task prediction of fuel properties of hydrochar derived from wet municipal wastes with random forest. Proceedings of the Applied Energy Symposium, Xiamen, China.","DOI":"10.46855\/energy-proceedings-3218"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1883","DOI":"10.1016\/j.renene.2019.07.103","article-title":"Predictions of energy recovery from hydrochar generated from the hydrothermal carbonization of organic wastes","volume":"145","author":"Li","year":"2020","journal-title":"Renew. Energy"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"11050","DOI":"10.1021\/acs.energyfuels.0c01893","article-title":"Prediction of bio-oil yield and hydrogen contents based on machine learning method: Effect of biomass compositions and pyrolysis conditions","volume":"34","author":"Tang","year":"2020","journal-title":"Energy Fuels"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Zhu, X., Li, Y., and Wang, X. (2019). Machine learning prediction of biochar yield and carbon contents in biochar based on biomass characteristics and pyrolysis conditions. Bioresour. Technol., 288.","DOI":"10.1016\/j.biortech.2019.121527"},{"key":"ref_36","first-page":"121527","article-title":"Uncertainty and sensitivity analyses of co-combustion\/pyrolysis of textile dyeing sludge and incense sticks: Regression and machine-learning models","volume":"288","author":"Wen","year":"2019","journal-title":"Renew. Energy"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"121010","DOI":"10.1016\/j.energy.2021.121010","article-title":"A novel machine learning-based approach for prediction of nitrogen content in hydrochar from hydrothermal carbonization of sewage sludge","volume":"232","author":"Djandja","year":"2021","journal-title":"Energy"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"117609","DOI":"10.1016\/j.fuel.2020.117609","article-title":"Role of feedstock properties and hydrothermal carbonization conditions on fuel properties of sewage sludge-derived hydrochar using multiple linear regression technique","volume":"271","author":"Zheng","year":"2020","journal-title":"Fuel"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1007\/s12517-022-09665-4","article-title":"A novel gradient boosting regression tree technique optimized by improved sparrow search algorithm for predicting TBM penetration rate","volume":"15","author":"Yang","year":"2022","journal-title":"Arab. J. Geosci."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Al-Taai, S.R., Azize, N.M., Thoeny, Z.A., Imran, H., Bernardo, L.F.A., and Al-Khafaji, Z. (2023). XGBoost prediction model optimized with Bayesian for the compressive strength of eco-friendly concrete containing ground granulated blast furnace slag and recycled coarse aggregate. Appl. Sci., 13.","DOI":"10.3390\/app13158889"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"4207","DOI":"10.1007\/s00521-023-09296-0","article-title":"Prediction of punching shear strength in flat slabs: Ensemble learning models and practical implementation","volume":"36","author":"Nguyen","year":"2024","journal-title":"Neural Comput. Appl."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"106132","DOI":"10.1016\/j.compgeo.2024.106132","article-title":"Towards reliable barrier systems: A constrained XGBoost model coupled with gray wolf optimization for maximum swelling pressure of bentonite","volume":"168","author":"Shehab","year":"2024","journal-title":"Comput. Geotech."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Szczepanek, R. (2022). Daily streamflow forecasting in mountainous catchment using XGBoost, LightGBM and CatBoost. Hydrology, 9.","DOI":"10.3390\/hydrology9120226"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1007\/s11250-023-03700-6","article-title":"Usage of the XGBoost and MARS algorithms for predicting body weight in Kajli sheep breed","volume":"55","author":"Faraz","year":"2023","journal-title":"Trop. Anim. Health Prod."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Gan, M., Pan, S., Chen, Y., Cheng, C., Pan, H., and Zhu, X. (2021). Application of the machine learning LightGBM model to the prediction of the water levels of the Lower Columbia River. J. Mar. Sci. Eng., 9.","DOI":"10.3390\/jmse9050496"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Wang, S., and Zhang, X. (2024). Research on Credit Default Prediction Model Based on TabNet-Stacking. Entropy, 26.","DOI":"10.3390\/e26100861"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"567","DOI":"10.1111\/itor.13514","article-title":"Service pricing and charging strategy for video platforms considering consumer preferences","volume":"33","author":"Liu","year":"2024","journal-title":"Int. Trans. Oper. Res."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"118143","DOI":"10.1016\/j.enconman.2024.118143","article-title":"Development and conceptual design of a sewage sludge-to-fuel hybrid process: Prediction and optimization under analysis of variance and response surface model","volume":"306","author":"Tian","year":"2024","journal-title":"Energy Convers. Manag."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Sharma, C., and Ojha, C.S.P. (2019). Statistical parameters of hydrometeorological variables: Standard deviation, SNR, skewness and kurtosis. Advances in Water Resources Engineering and Management: Select Proceedings of TRACE 2018, Springer.","DOI":"10.1007\/978-981-13-8181-2_5"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1353\/csd.2006.0002","article-title":"The wisdom development scale: Translating the conceptual to the concrete","volume":"47","author":"Brown","year":"2006","journal-title":"J. Coll. Stud. Dev."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1716","DOI":"10.3758\/s13428-016-0814-1","article-title":"Univariate and multivariate skewness and kurtosis for measuring nonnormality: Prevalence, influence and estimation","volume":"49","author":"Cain","year":"2017","journal-title":"Behav. Res. Methods"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/j.anbehav.2014.05.003","article-title":"Effective use of Pearson\u2019s product\u2013moment correlation coefficient","volume":"93","author":"Puth","year":"2014","journal-title":"Anim. Behav."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1016\/j.istruc.2020.02.028","article-title":"Prediction model for compressive arch action capacity of RC frame structures under column removal scenario using gene expression programming","volume":"25","author":"Azim","year":"2020","journal-title":"Structures"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1080\/14786440009463897","article-title":"On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling","volume":"50","author":"Pearson","year":"1900","journal-title":"London Edinburgh Dublin Philos. Mag. J. Sci."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"2885","DOI":"10.1016\/j.corsci.2008.07.022","article-title":"The use of linear regression methods and Pearson\u2019s correlation matrix to identify mechanical\u2013physical\u2013chemical parameters controlling the micro-electrochemical behaviour of machined copper","volume":"50","author":"Gravier","year":"2008","journal-title":"Corros. Sci."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"125634","DOI":"10.1016\/j.conbuildmat.2021.125634","article-title":"Machine learning modeling integrating experimental analysis for predicting the properties of sugarcane bagasse ash concrete","volume":"314","author":"Shah","year":"2022","journal-title":"Constr. Build. Mater."},{"key":"ref_58","first-page":"537","article-title":"Experimental and numerical analysis of seismic behaviour for recycled aggregate concrete filled circular steel tube frames","volume":"31","author":"Zhang","year":"2023","journal-title":"Comput. Concr."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Khan, M.A., Farooq, F., Javed, M.F., Zafar, A., Ostrowski, K.A., Aslam, F., Malazdrewicz, S., and Ma\u015blak, M. (2021). Simulation of depth of wear of eco-friendly concrete using machine learning based computational approaches. Materials, 15.","DOI":"10.3390\/ma15010058"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Khan, M., Ali, M., Najeh, T., and Gamil, Y. (2024). Computational prediction of workability and mechanical properties of bentonite plastic concrete using multi-expression programming. Sci. Rep., 14.","DOI":"10.1038\/s41598-024-56088-0"}],"container-title":["Water"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-4441\/17\/24\/3468\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T09:28:30Z","timestamp":1765186110000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-4441\/17\/24\/3468"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,6]]},"references-count":60,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["w17243468"],"URL":"https:\/\/doi.org\/10.3390\/w17243468","relation":{},"ISSN":["2073-4441"],"issn-type":[{"value":"2073-4441","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,6]]}}}