{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,26]],"date-time":"2025-12-26T06:05:37Z","timestamp":1766729137215,"version":"3.48.0"},"reference-count":53,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T00:00:00Z","timestamp":1758672000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T00:00:00Z","timestamp":1758672000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100021171","name":"Basic and Applied Basic Research Foundation of Guangdong Province","doi-asserted-by":"publisher","award":["2023A1515110764"],"award-info":[{"award-number":["2023A1515110764"]}],"id":[{"id":"10.13039\/501100021171","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Earth Sci Inform"],"published-print":{"date-parts":[[2025,12]]},"DOI":"10.1007\/s12145-025-02000-x","type":"journal-article","created":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T10:41:35Z","timestamp":1758710495000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A deep learning and knowledge graph integration framework for intelligent inspection in hydropower stations: a case study of Jiangya hydropower station"],"prefix":"10.1007","volume":"18","author":[{"given":"Jinhui","family":"Hu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Changtao","family":"Deng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ziyi","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aoxuan","family":"Pang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiuwen","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,9,24]]},"reference":[{"key":"2000_CR1","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1016\/j.heliyon.2024.e25383","volume":"10","author":"B Abu-Salih","year":"2024","unstructured":"Abu-Salih B, Alotaibi S (2024) A systematic literature review of knowledge graph construction and application in education. Heliyon 10:23","journal-title":"Heliyon"},{"key":"2000_CR2","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-023-00774-9","volume":"10","author":"B Abu-Salih","year":"2023","unstructured":"Abu-Salih B, Al-Qurishi M, Alweshah M, Al-Smadi M, Alfayez R, Saadeh H (2023) Healthcare knowledge graph construction: a systematic review of the state-of-the-art, open issues, and opportunities. J Big Data 10:32","journal-title":"J Big Data"},{"key":"2000_CR3","first-page":"147","volume":"521","author":"F Bianchini","year":"2024","unstructured":"Bianchini F, Calamo M, De Luzi F, Macr\u00ec M, Mecella M (2024) Enhancing complex linguistic tasks resolution through fine-tuning llms, RAG and knowledge graphs (short paper). Lect Notes Bus Inf P 521:147\u2013155","journal-title":"Lect Notes Bus Inf P"},{"issue":"4","key":"2000_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.14488\/BJOPM.2020.046","volume":"17","author":"E Cavaco","year":"2020","unstructured":"Cavaco E, Muniz J (2020) Knowledge-based risk management model: application in hydropower station projects. Braz J Oper Prod Man 17(4):1\u20136","journal-title":"Braz J Oper Prod Man"},{"key":"2000_CR5","doi-asserted-by":"publisher","first-page":"595","DOI":"10.1109\/TCBB.2022.3157630","volume":"20","author":"ZY Chai","year":"2023","unstructured":"Chai ZY, Jin H, Shi SH, Zhan SY, Zhuo L, Yang Y, Lian Q (2023) Noise reduction learning based on XLNet-CRF for biomedical named entity recognition. IEEE-ACM Trans Comput Biol Bioinform 20:595\u2013605","journal-title":"IEEE-ACM Trans Comput Biol Bioinform"},{"key":"2000_CR6","doi-asserted-by":"crossref","unstructured":"Chang C, Zhou JM, Li WS, Wu ZY, Gao J, Tang Y (2023) Explainable Multi-type Item Recommendation System Based on Knowledge Graph, 16th International Conference on Knowledge Science, Engineering and Management (KSEM). Springer International Publishing Ag, Guangzhou, PEOPLES R CHINA, pp. 3\u201315","DOI":"10.1007\/978-3-031-40289-0_1"},{"key":"2000_CR7","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1007\/s12559-023-10112-z","volume":"15","author":"YP Chen","year":"2023","unstructured":"Chen YP, Huang R, Pan LJ, Huang RZ, Zheng QH, Chen P (2023) A controlled attention for nested named entity recognition. Cogn Comput 15:132\u2013145","journal-title":"Cogn Comput"},{"issue":"18","key":"2000_CR8","doi-asserted-by":"publisher","first-page":"3417","DOI":"10.3390\/rs16183417","volume":"16","author":"M Cheon","year":"2024","unstructured":"Cheon M, Mun C (2024) Combining KAN with CNN: konvnext\u2019s performance in remote sensing and patent insights. Remote Sens 16(18):3417. https:\/\/doi.org\/10.3390\/rs16183417","journal-title":"Remote Sens"},{"issue":"4","key":"2000_CR9","doi-asserted-by":"publisher","first-page":"516","DOI":"10.55730\/1300-0632.4085","volume":"32","author":"O \u00c7ift\u00e7i","year":"2024","unstructured":"\u00c7ift\u00e7i O, Soygazi F, Tekir S (2024) Enrichment of Turkish question answering systems using knowledge graphs. Turk J Electr Eng Comput Sci 32(4):516\u2013533. https:\/\/doi.org\/10.55730\/1300-0632.4085","journal-title":"Turk J Electr Eng Comput Sci"},{"key":"2000_CR10","unstructured":"Converti F (2013) Measurement, representation and rural architecture exploitation: knowledge through historical maps from mills to hydropower stations. Fabbr Conoscenza, pp 999\u20131012"},{"key":"2000_CR11","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1016\/j.geoderma.2023.116452","volume":"433","author":"P Datta","year":"2023","unstructured":"Datta P, Faroughi SA (2023) A multihead LSTM technique for prognostic prediction of soil moisture. Geoderma 433:13","journal-title":"Geoderma"},{"key":"2000_CR12","doi-asserted-by":"crossref","first-page":"25","DOI":"10.3390\/ijgi10080541","volume":"10","author":"G Del Mondo","year":"2021","unstructured":"Del Mondo G, Peng P, Gensel J, Claramunt C, Lu F (2021) Leveraging Spatio-Temporal graphs and knowledge graphs: perspectives in the field of maritime transportation. ISPRS Int J Geo-Inf 10:25","journal-title":"ISPRS Int J Geo-Inf"},{"key":"2000_CR13","first-page":"4171","volume":"1","author":"J Devlin","year":"2019","unstructured":"Devlin J, Chang MW, Lee K, Toutanova K (2019) BERT: pre-training of deep bidirectional transformers for language understanding. 2019 Conf North Am Chapter Association Comput Linguistics: Hum Lang Technol (Naacl Hlt 2019) 1:4171\u20134186","journal-title":"2019 Conf North Am Chapter Association Comput Linguistics: Hum Lang Technol (Naacl Hlt 2019)"},{"key":"2000_CR14","doi-asserted-by":"publisher","first-page":"348","DOI":"10.1016\/j.neucom.2020.04.110","volume":"403","author":"YK Ding","year":"2020","unstructured":"Ding YK, Zhu YL, Feng J, Zhang PC, Cheng ZR (2020) Interpretable spatio-temporal attention LSTM model for flood forecasting. Neurocomputing 403:348\u2013359","journal-title":"Neurocomputing"},{"key":"2000_CR15","doi-asserted-by":"crossref","unstructured":"Ding K, Han HQ, Li LN, Yi ML (2021) Research on Question Answering System for COVID-19 Based on Knowledge Graph, 40th Chinese Control Conference (CCC). Ieee, Shanghai, PEOPLES R CHINA, pp. 4659\u20134664","DOI":"10.23919\/CCC52363.2021.9550437"},{"issue":"1","key":"2000_CR16","doi-asserted-by":"publisher","first-page":"2461809","DOI":"10.1080\/08839514.2025.2461809","volume":"39","author":"YF Dong","year":"2025","unstructured":"Dong YF, Li XF, He ML, Li J (2025) DC-BiLSTM-CNN algorithm for sentiment analysis of Chinese product reviews. Appl Artif Intell 39(1):2461809. https:\/\/doi.org\/10.1080\/08839514.2025.2461809","journal-title":"Appl Artif Intell"},{"key":"2000_CR17","doi-asserted-by":"crossref","first-page":"22","DOI":"10.3390\/ijgi9010022","volume":"9","author":"RY Fan","year":"2020","unstructured":"Fan RY, Wang LZ, Yan JN, Song WJ, Zhu YQ, Chen XD (2020) Deep Learning-Based named entity recognition and knowledge graph construction for geological hazards. ISPRS Int J Geo-Inf 9:22","journal-title":"ISPRS Int J Geo-Inf"},{"key":"2000_CR18","first-page":"895","volume":"14","author":"CN Huang","year":"2023","unstructured":"Huang CN, Jing B, Jiang LL, Zhu YQ (2023) Group intelligence recommendation system based on knowledge graph and fusion recommendation model. Int J Adv Comput Sci Appl 14:895\u2013904","journal-title":"Int J Adv Comput Sci Appl"},{"key":"2000_CR19","doi-asserted-by":"crossref","unstructured":"Kumar S, Sahu A, Sharan A (2021) Deep Learning Based Architecture for Entity Extraction from Covid Related Documents, 4th International Conference on Information Systems and Management Science (ISMS). Springer International Publishing Ag, Msida, MALTA, pp. 419\u2013427","DOI":"10.1007\/978-3-031-13150-9_33"},{"key":"2000_CR20","unstructured":"Lafferty J, McCallum A, Pereira FC (2001) Conditional random fields: Probabilistic models for segmenting and labeling sequence data"},{"key":"2000_CR21","unstructured":"Lan Z, Chen M, Goodman S, Gimpel K, Sharma P, Soricut R (2019) Albert: A lite Bert for self-supervised learning of Language representations. arXiv preprint arXiv:1909.11942."},{"key":"2000_CR22","doi-asserted-by":"crossref","unstructured":"Li QL, Zhu YH, Wei SG, Wang XZ, Lu L, Yu FH (2022) An attention-aware LSTM model for soil moisture and soil temperature prediction. Geoderma 409:115651","DOI":"10.1016\/j.geoderma.2021.115651"},{"key":"2000_CR23","doi-asserted-by":"publisher","DOI":"10.3390\/pr12122731","author":"P Li","year":"2024","unstructured":"Li P, Zhou M, Lin X, Zhou LS, Cai P (2024) An ancillary decision-making method for hydropower station failure handling based on case-based reasoning and knowledge graph. Processes. https:\/\/doi.org\/10.3390\/pr12122731","journal-title":"Processes"},{"issue":"12","key":"2000_CR24","doi-asserted-by":"publisher","first-page":"126205","DOI":"10.1088\/1361-6501\/ad75ae","volume":"35","author":"B Lin","year":"2024","unstructured":"Lin B, Zhu GH, Zhang QH, Sun GX (2024) A novel framework for bearing fault diagnosis across working conditions based on time-frequency fusion and multi-sensor data fusion. Meas Sci Technol 35(12):126205. https:\/\/doi.org\/10.1088\/1361-6501\/ad75ae","journal-title":"Meas Sci Technol"},{"key":"2000_CR25","unstructured":"Liu Y, Ott M, Goyal N, Du J, Joshi M, Chen D, Levy O, Lewis M, Zettlemoyer L, Stoyanov V (2019) Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692"},{"key":"2000_CR26","doi-asserted-by":"publisher","first-page":"1133","DOI":"10.32604\/csse.2022.021525","volume":"42","author":"DL Lu","year":"2022","unstructured":"Lu DL, Zhu DJ, Du HW, Sun YD, Wang YS, Li XF, Qu RN, Cao N, Higgs R (2022) Fusion recommendation system based on collaborative filtering and knowledge graph. Comput Syst Sci Eng 42:1133\u20131146","journal-title":"Comput Syst Sci Eng"},{"key":"2000_CR27","doi-asserted-by":"crossref","unstructured":"Ma B, Li DQ, Wang CP, Li J, Li G, Cui XN (2022) A Cyberspace Security Knowledge System Based on Knowledge Graph, 8th International Conference on Artificial Intelligence and Security (ICAIS). Springer International Publishing Ag, Qinghai, PEOPLES R CHINA, pp. 349\u2013362","DOI":"10.1007\/978-3-031-06791-4_28"},{"issue":"6","key":"2000_CR28","doi-asserted-by":"publisher","first-page":"btae353","DOI":"10.1093\/bioinformatics\/btae353","volume":"40","author":"N Matsumoto","year":"2024","unstructured":"Matsumoto N, Moran J, Choi H, Hernandez ME, Venkatesan M, Wang PL, Moore JH (2024) Kragen: a knowledge graph-enhanced RAG framework for biomedical problem solving using large language models. Bioinformatics 40(6):btae353. https:\/\/doi.org\/10.1093\/bioinformatics\/btae353","journal-title":"Bioinformatics"},{"issue":"1","key":"2000_CR29","doi-asserted-by":"publisher","first-page":"2526148","DOI":"10.1080\/23322039.2025.2526148","volume":"13","author":"E Nsengiyumva","year":"2025","unstructured":"Nsengiyumva E, Mung\u2019atu JK, Ruranga C (2025) A comparative study of multivariate CNN, BiLSTM and hybrid CNN-BiLSTM models for forecasting foreign exchange rate using deep learning. Cogent Econ Finance 13(1):2526148. https:\/\/doi.org\/10.1080\/23322039.2025.2526148","journal-title":"Cogent Econ Finance"},{"key":"2000_CR30","doi-asserted-by":"publisher","first-page":"100448","DOI":"10.1016\/j.bdr.2024.100448","volume":"36","author":"M Peng","year":"2024","unstructured":"Peng M, Liu YX, Khan A, Ahmed B, Sarker SK, Ghadi YY, Bhatti UA, Al-Razgan M, Ali YA (2024) Crop monitoring using remote sensing land use and land change data: comparative analysis of deep learning methods using pre-trained CNN models. Big Data Res 36:100448. https:\/\/doi.org\/10.1016\/j.bdr.2024.100448","journal-title":"Big Data Res"},{"key":"2000_CR31","first-page":"1145","volume":"21","author":"S Singh","year":"2022","unstructured":"Singh S, Siwach M (2022) Handling heterogeneous data in knowledge graphs: a survey. J Web Eng 21:1145\u20131186","journal-title":"J Web Eng"},{"key":"2000_CR32","volume":"12","author":"JL Sun","year":"2022","unstructured":"Sun JL, Liu YR, Cui J, He HD (2022) Deep learning-based methods for natural hazard named entity recognition. Sci Rep 12:15","journal-title":"Sci Rep"},{"key":"2000_CR33","doi-asserted-by":"crossref","unstructured":"Sun Y, Yang WR, Liu Y (2024) The Application of Constructing Knowledge Graph of Oral Historical Archives Resources Based on LLM-RAG. 8th International Conference on Information System and Data Mining, Icisdm 2024, 142\u2013149","DOI":"10.1145\/3686397.3686420"},{"key":"2000_CR34","unstructured":"Tian YH, Qin H, Xia F, Song Y (2022) Syntax-driven Approach for Semantic Role Labeling, 13th International Conference on Language Resources and Evaluation (LREC), Marseille, FRANCE, pp. 7129\u20137139"},{"key":"2000_CR35","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I (2017) Attention is all you need. Advances in neural information processing systems 30"},{"key":"2000_CR36","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1061\/(ASCE)CP.1943-5487.0001064","volume":"37","author":"XY Wang","year":"2023","unstructured":"Wang XY, El-Gohary N (2023) Deep learning-based named entity recognition and resolution of referential ambiguities for enhanced information extraction from construction safety regulations. J Comput Civil Eng 37:17","journal-title":"J Comput Civil Eng"},{"key":"2000_CR37","first-page":"14","volume":"10","author":"HF Wang","year":"2022","unstructured":"Wang HF, Du HF, Qi GL, Chen HJ, Hu W, Chen Z (2022) Construction of a linked data set of COVID-19 knowledge graphs: development and applications. JMIR Med Inf 10:14","journal-title":"JMIR Med Inf"},{"key":"2000_CR38","doi-asserted-by":"publisher","first-page":"2609","DOI":"10.1007\/s12145-024-01304-8","volume":"17","author":"L Wei","year":"2024","unstructured":"Wei L, Lu QH, Duan YL, Yao H, Kang XJ (2024) CEDG-geoqa: knowledge base question answering for the geoscience domain via Chinese entity description graph. Earth Sci Inf 17:2609\u20132621","journal-title":"Earth Sci Inf"},{"key":"2000_CR39","doi-asserted-by":"crossref","unstructured":"Weng JT, Gao Y, Qiu J, Ding GZ, Zheng HQ (2019) Construction and Application of Teaching System Based on Crowdsourcing Knowledge Graph, 4th China Conference on Knowledge Graph and Semantic Computing (CCKS). Springer-Verlag Singapore Pte Ltd, Hangzhou, PEOPLES R CHINA, pp. 25\u201337","DOI":"10.1007\/978-981-15-1956-7_3"},{"key":"2000_CR40","doi-asserted-by":"crossref","unstructured":"Wu YK, Zhu YP, Li JC, Zhang CY, Gong TL, Du XY, Wu TX (2021) Unmanned aerial vehicle knowledge graph construction with SpERT, 6th China Conference on Knowledge Graph and Semantic Computing (CCKS). Springer-Verlag Singapore Pte Ltd, Guangzhou, PEOPLES R CHINA, pp. 151\u2013159","DOI":"10.1007\/978-981-19-0713-5_17"},{"key":"2000_CR41","doi-asserted-by":"crossref","unstructured":"Wu ZJ, Cui NB, Zhang WJ, Liu CW, Jin XL, Gong DZ, Xing LW, Zhao L, Wen SL, Yang YN (2024) Estimating soil moisture content in citrus orchards using multi-temporal sentinel-1A data-based LSTM and PSO-LSTM models. J Hydrol 637:131336","DOI":"10.1016\/j.jhydrol.2024.131336"},{"key":"2000_CR42","doi-asserted-by":"crossref","unstructured":"Wu YN, Lu YM, Zhou Y, Ding YF, Liu JP, Ruan T (2025) MKGF: A multi-modal knowledge graph based RAG framework to enhance LVLMs for medical visual question answering. Neurocomputing 635:129999","DOI":"10.1016\/j.neucom.2025.129999"},{"key":"2000_CR43","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1016\/j.oceaneng.2024.119115","volume":"312","author":"CX Xie","year":"2024","unstructured":"Xie CX, Zhong ZG, Zhang LM (2024) Intelligent maritime question-answering and recommendation system based on maritime vessel activity knowledge graph. Ocean Eng 312:17","journal-title":"Ocean Eng"},{"key":"2000_CR44","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1007\/s11704-016-5228-9","volume":"12","author":"JH Yan","year":"2018","unstructured":"Yan JH, Wang CY, Cheng WL, Gao M, Zhou AY (2018) A retrospective of knowledge graphs. Front Comput Sci 12:55\u201374","journal-title":"Front Comput Sci"},{"key":"2000_CR45","doi-asserted-by":"crossref","unstructured":"Yang YH, Huang MS, Zhang CW, Yu MJ, Ieee (2022) Put theory into practice knowledge graph based aviation quality reliability knowledge system, 4th international conference on system reliability and safety engineering (SRSE). Ieee, Electr Network, pp. 30\u201334","DOI":"10.1109\/SRSE56746.2022.10067790"},{"key":"2000_CR46","doi-asserted-by":"crossref","unstructured":"Yu XB, Stahr M, Chen H, Yan RM, Ieee, Design and implementation of curriculum system based on knowledge graph, IEEE international conference on consumer electronics and, engineering C (2021) (ICCECE). Ieee, Guangzhou, PEOPLES R CHINA, pp. 767\u2013770","DOI":"10.1109\/ICCECE51280.2021.9342370"},{"key":"2000_CR47","doi-asserted-by":"publisher","first-page":"154","DOI":"10.23919\/JSEE.2023.000150","volume":"35","author":"Y Zhang","year":"2024","unstructured":"Zhang Y, Xiao G (2024) Classification of knowledge graph completeness measurement techniques. J Syst Eng Electron 35:154\u2013162","journal-title":"J Syst Eng Electron"},{"key":"2000_CR48","first-page":"16","volume":"11","author":"Q Zhang","year":"2019","unstructured":"Zhang Q, Wen YQ, Zhou CH, Long H, Han D, Zhang F, Xiao CS (2019a) Construction of knowledge graphs for maritime dangerous goods. Sustainability-Basel 11:16","journal-title":"Sustainability-Basel"},{"key":"2000_CR49","doi-asserted-by":"publisher","first-page":"108278","DOI":"10.1109\/ACCESS.2019.2933370","volume":"7","author":"XM Zhang","year":"2019","unstructured":"Zhang XM, Sun XL, Xie CJ, Lun B (2019b) From vision to content: construction of domain-specific multi-modal knowledge graph. IEEE Access 7:108278\u2013108294","journal-title":"IEEE Access"},{"key":"2000_CR50","volume":"12","author":"WH Zhang","year":"2022","unstructured":"Zhang WH, Wang CS, Wu HR, Zhao CJ, Teng GF, Huang SF, Liu Z (2022) Research on the Chinese named-entity-relation-extraction method for crop diseases based on BERT. Agronomy 12:14","journal-title":"Agronomy"},{"key":"2000_CR51","doi-asserted-by":"crossref","unstructured":"Zhao ZH, Yang ZH, Luo L, Zhang Y, Wang L, Lin HF, Wang J (2016) ML-CNN: a novel deep learning based disease named entity recognition architecture, IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM). Ieee Computer Soc, Shenzhen, PEOPLES R CHINA, pp. 794\u2013794","DOI":"10.1109\/BIBM.2016.7822625"},{"key":"2000_CR52","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1016\/j.jlp.2022.104736","volume":"76","author":"YC Zhao","year":"2022","unstructured":"Zhao YC, Zhang BK, Gao D (2022) Construction of petrochemical knowledge graph based on deep learning. J Loss Prev Process Ind 76:13","journal-title":"J Loss Prev Process Ind"},{"key":"2000_CR53","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1016\/j.cose.2023.103524","volume":"136","author":"XJ Zhao","year":"2024","unstructured":"Zhao XJ, Jiang R, Han Y, Li AP, Peng ZC (2024) A survey on cybersecurity knowledge graph construction. Comput Secur 136:14","journal-title":"Comput Secur"}],"container-title":["Earth Science Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12145-025-02000-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12145-025-02000-x","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12145-025-02000-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,26]],"date-time":"2025-12-26T06:00:35Z","timestamp":1766728835000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12145-025-02000-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,24]]},"references-count":53,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,12]]}},"alternative-id":["2000"],"URL":"https:\/\/doi.org\/10.1007\/s12145-025-02000-x","relation":{},"ISSN":["1865-0473","1865-0481"],"issn-type":[{"type":"print","value":"1865-0473"},{"type":"electronic","value":"1865-0481"}],"subject":[],"published":{"date-parts":[[2025,9,24]]},"assertion":[{"value":"2 March 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 August 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 September 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interest"}}],"article-number":"508"}}