{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T18:37:48Z","timestamp":1762799868968,"version":"build-2065373602"},"reference-count":44,"publisher":"Springer Science and Business Media LLC","issue":"16","license":[{"start":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T00:00:00Z","timestamp":1762732800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T00:00:00Z","timestamp":1762732800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["No. 52231014","No. 52231014","No. 52231014","No. 52231014"],"award-info":[{"award-number":["No. 52231014","No. 52231014","No. 52231014","No. 52231014"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"DOI":"10.1007\/s11227-025-08045-5","type":"journal-article","created":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T18:12:08Z","timestamp":1762798328000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Hierarchical heterogeneous graph convolution network and improved LightGCN for service recommendation"],"prefix":"10.1007","volume":"81","author":[{"given":"Zhiying","family":"Cao","sequence":"first","affiliation":[]},{"given":"Miao","family":"Cheng","sequence":"additional","affiliation":[]},{"given":"Xiuguo","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Dezhen","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,11,10]]},"reference":[{"issue":"6","key":"8045_CR1","doi-asserted-by":"publisher","first-page":"3620","DOI":"10.1109\/TSC.2021.3103481","volume":"15","author":"M Shi","year":"2022","unstructured":"Shi M, Zhuang Y, Tang Y et al (2022) Web service network embedding based on link prediction and convolutional learning. IEEE Trans Serv Comput 15(6):3620\u20133633. https:\/\/doi.org\/10.1109\/TSC.2021.3103481","journal-title":"IEEE Trans Serv Comput"},{"key":"8045_CR2","doi-asserted-by":"publisher","unstructured":"Hao Y, Fan Y, Tan W et\u00a0al (2017) Service recommendation based on targeted reconstruction of service descriptions. In: 2017 IEEE International Conference on Web Services (ICWS), pp 285\u2013292, https:\/\/doi.org\/10.1109\/ICWS.2017.44","DOI":"10.1109\/ICWS.2017.44"},{"issue":"5","key":"8045_CR3","doi-asserted-by":"publisher","first-page":"1077","DOI":"10.1109\/TPDS.2018.2877363","volume":"30","author":"M Shi","year":"2019","unstructured":"Shi M, Tang Y, Liu J (2019) Functional and contextual attention-based lSTM for service recommendation in mashup creation. IEEE Trans Parallel Distrib Syst 30(5):1077\u20131090. https:\/\/doi.org\/10.1109\/TPDS.2018.2877363","journal-title":"IEEE Trans Parallel Distrib Syst"},{"key":"8045_CR4","doi-asserted-by":"publisher","unstructured":"Gong W, Lv C, Duan Y et\u00a0al (2021) Keywords-driven web APIS group recommendation for automatic app service creation process. Softw Pract Exp 51(11):2337\u20132354. https:\/\/doi.org\/10.1002\/spe.2902, https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/spe.2902","DOI":"10.1002\/spe.2902"},{"issue":"3","key":"8045_CR5","doi-asserted-by":"publisher","first-page":"2930","DOI":"10.1109\/TII.2022.3177411","volume":"19","author":"H Chen","year":"2023","unstructured":"Chen H, Wu H, Li J et al (2023) Keyword-driven service recommendation via deep reinforced Steiner tree search. IEEE Trans Industr Inf 19(3):2930\u20132941. https:\/\/doi.org\/10.1109\/TII.2022.3177411","journal-title":"IEEE Trans Industr Inf"},{"issue":"5","key":"8045_CR6","doi-asserted-by":"publisher","first-page":"1809","DOI":"10.1007\/s11280-021-00943-x","volume":"25","author":"F Wang","year":"2022","unstructured":"Wang F, Wang L, Li G et al (2022) Edge-cloud-enabled matrix factorization for diversified APIS recommendation in mashup creation. World Wide Web 25(5):1809\u20131829. https:\/\/doi.org\/10.1007\/s11280-021-00943-x","journal-title":"World Wide Web"},{"issue":"2","key":"8045_CR7","doi-asserted-by":"publisher","first-page":"502","DOI":"10.1109\/TSC.2018.2803171","volume":"14","author":"L Yao","year":"2021","unstructured":"Yao L, Wang X, Sheng QZ et al (2021) Mashup recommendation by regularizing matrix factorization with API co-invocations. IEEE Trans Serv Comput 14(2):502\u2013515. https:\/\/doi.org\/10.1109\/TSC.2018.2803171","journal-title":"IEEE Trans Serv Comput"},{"issue":"4","key":"8045_CR8","doi-asserted-by":"publisher","first-page":"4183","DOI":"10.1109\/TNSM.2021.3125028","volume":"18","author":"G Kang","year":"2021","unstructured":"Kang G, Liu J, Xiao Y et al (2021) Neural and attentional factorization machine-based web API recommendation for mashup development. IEEE Trans Netw Serv Manage 18(4):4183\u20134196. https:\/\/doi.org\/10.1109\/TNSM.2021.3125028","journal-title":"IEEE Trans Netw Serv Manage"},{"issue":"1","key":"8045_CR9","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1109\/TEM.2019.2961376","volume":"68","author":"Y Ma","year":"2021","unstructured":"Ma Y, Geng X, Wang J (2021) A deep neural network with multiplex interactions for cold-start service recommendation. IEEE Trans Eng Manage 68(1):105\u2013119. https:\/\/doi.org\/10.1109\/TEM.2019.2961376","journal-title":"IEEE Trans Eng Manage"},{"key":"8045_CR10","doi-asserted-by":"publisher","unstructured":"Wei C, Fan Y, Zhang J et\u00a0al (2020) A-HSG: Neural attentive service recommendation based on high-order social graph. In: 2020 IEEE International Conference on Web Services (ICWS), pp 338\u2013346, https:\/\/doi.org\/10.1109\/ICWS49710.2020.00051","DOI":"10.1109\/ICWS49710.2020.00051"},{"issue":"4","key":"8045_CR11","doi-asserted-by":"publisher","first-page":"4615","DOI":"10.1109\/TNSM.2022.3186396","volume":"19","author":"C Wei","year":"2022","unstructured":"Wei C, Fan Y, Zhang J (2022) High-order social graph neural network for service recommendation. IEEE Trans Netw Serv Manage 19(4):4615\u20134628. https:\/\/doi.org\/10.1109\/TNSM.2022.3186396","journal-title":"IEEE Trans Netw Serv Manage"},{"key":"8045_CR12","doi-asserted-by":"publisher","unstructured":"Wang K, Zhu Y, Zang T et\u00a0al (2023) Multi-aspect graph contrastive learning for review-enhanced recommendation. ACM Trans Inf Syst 42(2). https:\/\/doi.org\/10.1145\/3618106","DOI":"10.1145\/3618106"},{"issue":"1","key":"8045_CR13","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1109\/TSC.2017.2681666","volume":"13","author":"B Bai","year":"2020","unstructured":"Bai B, Fan Y, Tan W et al (2020) Dltsr: a deep learning framework for recommendations of long-tail web services. IEEE Trans Serv Comput 13(1):73\u201385. https:\/\/doi.org\/10.1109\/TSC.2017.2681666","journal-title":"IEEE Trans Serv Comput"},{"key":"8045_CR14","doi-asserted-by":"publisher","unstructured":"Zhang X, Liu J, Shi M et\u00a0al (2021) Word embedding-based web service representations for classification and clustering. In: 2021 IEEE International Conference on Services Computing (SCC), pp 34\u201343, https:\/\/doi.org\/10.1109\/SCC53864.2021.00015","DOI":"10.1109\/SCC53864.2021.00015"},{"issue":"5","key":"8045_CR15","doi-asserted-by":"publisher","first-page":"3077","DOI":"10.1109\/TSC.2021.3075053","volume":"15","author":"H Mezni","year":"2022","unstructured":"Mezni H (2022) Temporal knowledge graph embedding for effective service recommendation. IEEE Trans Serv Comput 15(5):3077\u20133088. https:\/\/doi.org\/10.1109\/TSC.2021.3075053","journal-title":"IEEE Trans Serv Comput"},{"issue":"10","key":"8045_CR16","doi-asserted-by":"publisher","first-page":"12621","DOI":"10.1007\/s11227-022-04369-8","volume":"78","author":"X Li","year":"2022","unstructured":"Li X, Zhang X, Wang P et al (2022) Web services recommendation based on metapath-guided graph attention network. J Supercomput 78(10):12621\u201312647. https:\/\/doi.org\/10.1007\/s11227-022-04369-8","journal-title":"J Supercomput"},{"key":"8045_CR17","doi-asserted-by":"publisher","unstructured":"Wu S, Sun F, Zhang W et\u00a0al (2022) Graph neural networks in recommender systems: a survey. ACM Comput Surv 55(5). https:\/\/doi.org\/10.1145\/3535101","DOI":"10.1145\/3535101"},{"issue":"8","key":"8045_CR18","doi-asserted-by":"publisher","first-page":"8003","DOI":"10.1007\/s10462-022-10375-2","volume":"56","author":"R Bing","year":"2023","unstructured":"Bing R, Yuan G, Zhu M et al (2023) Heterogeneous graph neural networks analysis: a survey of techniques, evaluations and applications. Artif Intell Rev 56(8):8003\u20138042. https:\/\/doi.org\/10.1007\/s10462-022-10375-2","journal-title":"Artif Intell Rev"},{"issue":"3","key":"8045_CR19","doi-asserted-by":"publisher","first-page":"1551","DOI":"10.1109\/TSC.2020.3001307","volume":"15","author":"J Zhang","year":"2020","unstructured":"Zhang J, Fan Y, Zhang J et al (2020) Learning to build accurate service representations and visualization. IEEE Trans Serv Comput 15(3):1551\u20131563. https:\/\/doi.org\/10.1109\/TSC.2020.3001307","journal-title":"IEEE Trans Serv Comput"},{"issue":"6","key":"8045_CR20","doi-asserted-by":"publisher","first-page":"3283","DOI":"10.1007\/s10115-024-02061-2","volume":"66","author":"D Dang","year":"2024","unstructured":"Dang D, Guo B, Fang T et al (2024) Multi-representation web service recommendation system based on attention mechanism. Knowl Inf Syst 66(6):3283\u20133302. https:\/\/doi.org\/10.1007\/s10115-024-02061-2","journal-title":"Knowl Inf Syst"},{"issue":"3","key":"8045_CR21","doi-asserted-by":"publisher","first-page":"1934","DOI":"10.1109\/TSC.2022.3189503","volume":"16","author":"Y Shu","year":"2022","unstructured":"Shu Y, Zhang J, Zhang WE et al (2022) IQSREC: an efficient and diversified skyline services recommendation on incomplete QOS. IEEE Trans Serv Comput 16(3):1934\u20131948. https:\/\/doi.org\/10.1109\/TSC.2022.3189503","journal-title":"IEEE Trans Serv Comput"},{"issue":"1","key":"8045_CR22","doi-asserted-by":"publisher","first-page":"457","DOI":"10.1109\/TSC.2019.2950291","volume":"15","author":"L Ren","year":"2022","unstructured":"Ren L, Wang W (2022) A granular SVM-based method for top-n web services recommendation. IEEE Trans Serv Comput 15(1):457\u2013469. https:\/\/doi.org\/10.1109\/TSC.2019.2950291","journal-title":"IEEE Trans Serv Comput"},{"key":"8045_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2023.119574","volume":"648","author":"Y Pan","year":"2023","unstructured":"Pan Y, Xu L, Wu DD et al (2023) An online-to-offline service recommendation method based on two-layer knowledge networks. Inf Sci 648:119574. https:\/\/doi.org\/10.1016\/j.ins.2023.119574","journal-title":"Inf Sci"},{"key":"8045_CR24","doi-asserted-by":"publisher","unstructured":"He X, Deng K, Wang X et\u00a0al (2020) Lightgcn: Simplifying and powering graph convolution network for recommendation. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. Association for Computing Machinery, New York, NY, USA, SIGIR \u201920, p 639\u2013648, https:\/\/doi.org\/10.1145\/3397271.3401063","DOI":"10.1145\/3397271.3401063"},{"issue":"4","key":"8045_CR25","doi-asserted-by":"publisher","first-page":"1940","DOI":"10.1109\/TSC.2020.3026188","volume":"15","author":"G Zou","year":"2022","unstructured":"Zou G, Qin Z, He Q et al (2022) Deepwsc: clustering web services via integrating service composability into deep semantic features. IEEE Trans Serv Comput 15(4):1940\u20131953. https:\/\/doi.org\/10.1109\/TSC.2020.3026188","journal-title":"IEEE Trans Serv Comput"},{"issue":"2","key":"8045_CR26","doi-asserted-by":"publisher","first-page":"1078","DOI":"10.1109\/TNSM.2023.3234067","volume":"20","author":"B Cao","year":"2023","unstructured":"Cao B, Zhang L, Peng M et al (2023) Web service recommendation via combining bilinear graph representation and xdeepfm quality prediction. IEEE Trans Netw Serv Manage 20(2):1078\u20131092. https:\/\/doi.org\/10.1109\/TNSM.2023.3234067","journal-title":"IEEE Trans Netw Serv Manage"},{"key":"8045_CR27","doi-asserted-by":"publisher","first-page":"13285","DOI":"10.1109\/ACCESS.2024.3505943","volume":"13","author":"L Shen","year":"2025","unstructured":"Shen L, Wang Y, Li C et al (2025) A cloud API personalized recommendation method based on multiple attribute features and mashup requirement attention. IEEE Access 13:13285\u201313299. https:\/\/doi.org\/10.1109\/ACCESS.2024.3505943","journal-title":"IEEE Access"},{"key":"8045_CR28","doi-asserted-by":"publisher","unstructured":"Yu T, Yu D, Wang D et al (2024) Iterative framework based on multi-task learning for service recommendation. J Syst Softw 207:111873. https:\/\/doi.org\/10.1016\/j.jss.2023.111873, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0164121223002686","DOI":"10.1016\/j.jss.2023.111873"},{"key":"8045_CR29","doi-asserted-by":"publisher","unstructured":"Yu D, Yu T, Wang D et al (2024) Long tail service recommendation based on cross-view and contrastive learning. Expert Syst Appl 238:121957. https:\/\/doi.org\/10.1016\/j.eswa.2023.121957, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0957417423024594","DOI":"10.1016\/j.eswa.2023.121957"},{"key":"8045_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.107499","volume":"128","author":"D Han","year":"2024","unstructured":"Han D, Kim D, Han K et al (2024) Keyword-enhanced recommender system based on inductive graph matrix completion. Eng Appl Artif Intell 128:107499. https:\/\/doi.org\/10.1016\/j.engappai.2023.107499","journal-title":"Eng Appl Artif Intell"},{"issue":"10","key":"8045_CR31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3661821","volume":"56","author":"K Sharma","year":"2024","unstructured":"Sharma K, Lee YC, Nambi S et al (2024) A survey of graph neural networks for social recommender systems. ACM Comput Surv 56(10):1\u201334. https:\/\/doi.org\/10.1145\/3661821","journal-title":"ACM Comput Surv"},{"key":"8045_CR32","doi-asserted-by":"publisher","unstructured":"Zhu Y, Liu M, Tu Z et\u00a0al (2021) Sraslr: A novel social relation aware service label recommendation model. In: 2021 IEEE International Conference on Web Services (ICWS), pp 87\u201396, https:\/\/doi.org\/10.1109\/ICWS53863.2021.00024","DOI":"10.1109\/ICWS53863.2021.00024"},{"issue":"3","key":"8045_CR33","doi-asserted-by":"publisher","first-page":"2229","DOI":"10.1109\/TSC.2022.3197655","volume":"16","author":"C Wei","year":"2023","unstructured":"Wei C, Fan Y, Zhang J (2023) Time-aware service recommendation with social-powered graph hierarchical attention network. IEEE Trans Serv Comput 16(3):2229\u20132240. https:\/\/doi.org\/10.1109\/TSC.2022.3197655","journal-title":"IEEE Trans Serv Comput"},{"issue":"5","key":"8045_CR34","doi-asserted-by":"publisher","first-page":"2033","DOI":"10.1109\/TKDE.2020.3008732","volume":"34","author":"W Fan","year":"2022","unstructured":"Fan W, Ma Y, Li Q et al (2022) A graph neural network framework for social recommendations. IEEE Trans Knowl Data Eng 34(5):2033\u20132047. https:\/\/doi.org\/10.1109\/TKDE.2020.3008732","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"8045_CR35","doi-asserted-by":"publisher","unstructured":"Lin W, Zhu M, Zhou X et\u00a0al (2023) A deep neural collaborative filtering based service recommendation method with multi-source data for smart cloud-edge collaboration applications. Tsinghua Sci Technol 29(3):897\u2013910. https:\/\/doi.org\/10.26599\/TST.2023.9010050","DOI":"10.26599\/TST.2023.9010050"},{"issue":"11","key":"8045_CR36","doi-asserted-by":"publisher","first-page":"5225","DOI":"10.1109\/TKDE.2021.3059506","volume":"34","author":"H Mezni","year":"2022","unstructured":"Mezni H, Benslimane D, Bellatreche L (2022) Context-aware service recommendation based on knowledge graph embedding. IEEE Trans Knowl Data Eng 34(11):5225\u20135238. https:\/\/doi.org\/10.1109\/TKDE.2021.3059506","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"8045_CR37","doi-asserted-by":"publisher","unstructured":"Afoudi Y, Lazaar M, Hmaidi S (2023) An enhanced recommender system based on heterogeneous graph link prediction. Eng Appl Artif Intell 124:106553. https:\/\/doi.org\/10.1016\/j.engappai.2023.106553, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0952197623007376","DOI":"10.1016\/j.engappai.2023.106553"},{"issue":"3","key":"8045_CR38","doi-asserted-by":"publisher","first-page":"2668","DOI":"10.1109\/TNSM.2023.3239847","volume":"20","author":"Z Jia","year":"2023","unstructured":"Jia Z, Fan Y, Zhang J (2023) MGMASR: multi-graph and multi-aspect neural network for service recommendation in internet of services. IEEE Trans Netw Serv Manage 20(3):2668\u20132681. https:\/\/doi.org\/10.1109\/TNSM.2023.3239847","journal-title":"IEEE Trans Netw Serv Manage"},{"issue":"5","key":"8045_CR39","doi-asserted-by":"publisher","first-page":"3837","DOI":"10.1109\/TSC.2023.3287189","volume":"16","author":"B Cao","year":"2023","unstructured":"Cao B, Peng M, Zhang L et al (2023) Web service recommendation via integrating heterogeneous graph attention network representation and fibinet score prediction. IEEE Trans Serv Comput 16(5):3837\u20133850. https:\/\/doi.org\/10.1109\/TSC.2023.3287189","journal-title":"IEEE Trans Serv Comput"},{"key":"8045_CR40","doi-asserted-by":"publisher","unstructured":"Reimers N, Gurevych I (2019) Sentence-BERT: Sentence embeddings using siamese BERT-networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, pp 3982\u20133992, https:\/\/doi.org\/10.18653\/v1\/D19-1410, https:\/\/aclanthology.org\/D19-1410","DOI":"10.18653\/v1\/D19-1410"},{"key":"8045_CR41","unstructured":"Glorot X, Bengio Y (2010) Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the thirteenth international conference on artificial intelligence and statistics, JMLR Workshop and Conference Proceedings, pp 249\u2013256"},{"key":"8045_CR42","doi-asserted-by":"crossref","unstructured":"Chen C, Zhang M, Liu Y et\u00a0al (2019) Social attentional memory network: Modeling aspect-and friend-level differences in recommendation. In: Proceedings of the twelfth ACM international conference on web search and data mining, pp 177\u2013185","DOI":"10.1145\/3289600.3290982"},{"key":"8045_CR43","doi-asserted-by":"publisher","unstructured":"Chen M, Huang C, Xia L et\u00a0al (2023) Heterogeneous graph contrastive learning for recommendation. In: Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining. Association for Computing Machinery, New York, NY, USA, WSDM \u201923, p 544\u2013552, https:\/\/doi.org\/10.1145\/3539597.3570484","DOI":"10.1145\/3539597.3570484"},{"key":"8045_CR44","doi-asserted-by":"publisher","unstructured":"Zhang Q, Ren S, Li X et al (2024) A service-recommendation method for the internet of things leveraging implicit social relationships. Comput Electr Eng 120:109734. https:\/\/doi.org\/10.1016\/j.compeleceng.2024.109734, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S004579062400661X","DOI":"10.1016\/j.compeleceng.2024.109734"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-025-08045-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-025-08045-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-025-08045-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T18:12:09Z","timestamp":1762798329000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-025-08045-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,10]]},"references-count":44,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2025,11]]}},"alternative-id":["8045"],"URL":"https:\/\/doi.org\/10.1007\/s11227-025-08045-5","relation":{},"ISSN":["1573-0484"],"issn-type":[{"type":"electronic","value":"1573-0484"}],"subject":[],"published":{"date-parts":[[2025,11,10]]},"assertion":[{"value":"30 May 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 November 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 November 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 conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"1552"}}