{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,26]],"date-time":"2025-12-26T11:19:37Z","timestamp":1766747977356,"version":"3.37.3"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,11,23]],"date-time":"2024-11-23T00:00:00Z","timestamp":1732320000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,23]],"date-time":"2024-11-23T00:00:00Z","timestamp":1732320000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100012165","name":"Key Technologies Research and Development Program","doi-asserted-by":"publisher","award":["2022YFB4501700"],"award-info":[{"award-number":["2022YFB4501700"]}],"id":[{"id":"10.13039\/501100012165","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2025,1]]},"DOI":"10.1007\/s10489-024-06055-z","type":"journal-article","created":{"date-parts":[[2024,11,23]],"date-time":"2024-11-23T07:32:19Z","timestamp":1732347139000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["ST-NAMN: a spatial-temporal nonlinear auto-regressive multichannel neural network for traffic prediction"],"prefix":"10.1007","volume":"55","author":[{"given":"Jiankai","family":"Zuo","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9552-3274","authenticated-orcid":false,"given":"Yaying","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,11,23]]},"reference":[{"issue":"6","key":"6055_CR1","doi-asserted-by":"publisher","first-page":"4927","DOI":"10.1109\/TITS.2021.3054840","volume":"23","author":"X Yin","year":"2022","unstructured":"Yin X, Wu G, Wei J, Shen Y, Qi H, Yin B (2022) Deep learning on traffic prediction: methods, analysis, and future directions. IEEE Trans Intell Transp Syst 23(6):4927\u20134943. https:\/\/doi.org\/10.1109\/TITS.2021.3054840","journal-title":"IEEE Trans Intell Transp Syst"},{"issue":"4","key":"6055_CR2","doi-asserted-by":"publisher","first-page":"1544","DOI":"10.1109\/TKDE.2020.3001195","volume":"34","author":"DA Tedjopurnomo","year":"2022","unstructured":"Tedjopurnomo DA, Bao Z, Zheng B, Choudhury FM, Qin AK (2022) A survey on modern deep neural network for traffic prediction: trends, methods and challenges. IEEE Trans Knowl Data Eng 34(4):1544\u20131561. https:\/\/doi.org\/10.1109\/TKDE.2020.3001195","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"3","key":"6055_CR3","doi-asserted-by":"publisher","first-page":"2182","DOI":"10.1109\/TITS.2023.3319135","volume":"25","author":"L Zhu","year":"2024","unstructured":"Zhu L, Chen C, Wang H, Yu FR, Tang T (2024) Machine learning in urban rail transit systems: a survey. IEEE Trans Intell Transp Syst 25(3):2182\u20132207. https:\/\/doi.org\/10.1109\/TITS.2023.3319135","journal-title":"IEEE Trans Intell Transp Syst"},{"issue":"5","key":"6055_CR4","doi-asserted-by":"publisher","first-page":"4443","DOI":"10.1109\/TITS.2023.3329489","volume":"25","author":"H Yang","year":"2024","unstructured":"Yang H, Yu W, Zhang G, Du L (2024) Network-wide traffic flow dynamics prediction leveraging macroscopic traffic flow model and deep neural networks. IEEE Trans Intell Transp Syst 25(5):4443\u20134457. https:\/\/doi.org\/10.1109\/TITS.2023.3329489","journal-title":"IEEE Trans Intell Transp Syst"},{"issue":"1","key":"6055_CR5","doi-asserted-by":"publisher","first-page":"1889","DOI":"10.1109\/TCE.2023.3335155","volume":"70","author":"M Aljebreen","year":"2024","unstructured":"Aljebreen M et al (2024) Enhancing traffic flow prediction in intelligent cyber-physical systems: a novel bi-LSTM-based approach with kalman filter integration. IEEE Trans Consum Electron 70(1):1889\u20131902. https:\/\/doi.org\/10.1109\/TCE.2023.3335155","journal-title":"IEEE Trans Consum Electron"},{"issue":"4","key":"6055_CR6","doi-asserted-by":"publisher","first-page":"1054","DOI":"10.1109\/TITS.2017.2711046","volume":"19","author":"C Ding","year":"2018","unstructured":"Ding C, Duan J, Zhang Y, Wu X, Yu G (2018) Using an ARIMA-GARCH modeling approach to improve subway short-term ridership forecasting accounting for dynamic volatility. IEEE Trans Intell Transp Syst 19(4):1054\u20131064. https:\/\/doi.org\/10.1109\/TITS.2017.2711046","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"6055_CR7","doi-asserted-by":"crossref","unstructured":"Liu C, Hoi SCH, Zhao P, Sun J (2016) Online ARIMA algorithms for time series prediction. In: 30th AAAI conference on artificial intelligence, pp 1867\u20131873","DOI":"10.1609\/aaai.v30i1.10257"},{"issue":"2","key":"6055_CR8","doi-asserted-by":"publisher","first-page":"613","DOI":"10.1007\/s00521-020-05002-6","volume":"33","author":"C Li","year":"2021","unstructured":"Li C, Xu P (2021) Application on traffic flow prediction of machine learning in intelligent transportation. Neural Comput Appl 33(2):613\u2013624","journal-title":"Neural Comput Appl"},{"issue":"12","key":"6055_CR9","doi-asserted-by":"publisher","first-page":"12301","DOI":"10.1109\/TVT.2019.2947080","volume":"68","author":"L Han","year":"2019","unstructured":"Han L, Zheng K, Zhao L, Wang X, Shen X (2019) Short-term traffic prediction based on deepcluster in large-scale road networks. IEEE Trans Veh Technol 68(12):12301\u201312313. https:\/\/doi.org\/10.1109\/TVT.2019.2947080","journal-title":"IEEE Trans Veh Technol"},{"key":"6055_CR10","doi-asserted-by":"crossref","unstructured":"Deng D, Shahabi C, Demiryurek U et al (2016) Latent space model for road networks to predict time-varying traffic. In: 22nd ACM SIGKDD international conference on knowledge discovery and data mining (KDD\u201916). New York, USA, pp 1525\u20131534","DOI":"10.1145\/2939672.2939860"},{"issue":"6","key":"6055_CR11","doi-asserted-by":"publisher","first-page":"2573","DOI":"10.1109\/TKDE.2019.2954868","volume":"33","author":"A Baggag","year":"2021","unstructured":"Baggag A et al (2021) Learning spatiotemporal latent factors of traffic via regularized tensor factorization: imputing missing values and forecasting. IEEE Trans Knowl Data Eng 33(6):2573\u20132587. https:\/\/doi.org\/10.1109\/TKDE.2019.2954868","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"9","key":"6055_CR12","doi-asserted-by":"publisher","first-page":"4659","DOI":"10.1109\/TPAMI.2021.3066551","volume":"44","author":"X Chen","year":"2022","unstructured":"Chen X, Sun L (2022) Bayesian temporal factorization for multidimensional time series prediction. IEEE Trans Pattern Anal Mach Intell 44(9):4659\u20134673. https:\/\/doi.org\/10.1109\/TPAMI.2021.3066551","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"1","key":"6055_CR13","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1109\/TVT.2019.2952605","volume":"69","author":"F Zhao","year":"2020","unstructured":"Zhao F, Zeng G-Q, Lu K-D (2020) EnLSTM-WPEO: short-term traffic flow prediction by ensemble LSTM, NNCT weight integration, and population extremal optimization. IEEE Trans Veh Technol 69(1):101\u2013113. https:\/\/doi.org\/10.1109\/TVT.2019.2952605","journal-title":"IEEE Trans Veh Technol"},{"issue":"8","key":"6055_CR14","doi-asserted-by":"publisher","first-page":"8257","DOI":"10.1109\/TVT.2020.2999358","volume":"69","author":"P-Y Ting","year":"2020","unstructured":"Ting P-Y et al (2020) Freeway travel time prediction using deep hybrid model - taking Sun Yat-Sen freeway as an example. IEEE Trans Veh Technol 69(8):8257\u20138266. https:\/\/doi.org\/10.1109\/TVT.2020.2999358","journal-title":"IEEE Trans Veh Technol"},{"key":"6055_CR15","doi-asserted-by":"publisher","first-page":"113217","DOI":"10.1109\/ACCESS.2023.3324035","volume":"11","author":"EH Lee","year":"2023","unstructured":"Lee EH (2023) Traffic speed prediction of urban road network based on high importance links using XGB and SHAP. IEEE Access 11:113217\u2013113226. https:\/\/doi.org\/10.1109\/ACCESS.2023.3324035","journal-title":"IEEE Access"},{"key":"6055_CR16","doi-asserted-by":"publisher","first-page":"820","DOI":"10.1109\/JRFID.2022.3217031","volume":"6","author":"D Zhao","year":"2022","unstructured":"Zhao D, Chen F (2022) A hybrid ensemble model for urban lane-level traffic flow prediction. IEEE J Radio Freq Identif 6:820\u2013824. https:\/\/doi.org\/10.1109\/JRFID.2022.3217031","journal-title":"IEEE J Radio Freq Identif"},{"issue":"9","key":"6055_CR17","doi-asserted-by":"publisher","first-page":"16654","DOI":"10.1109\/TITS.2021.3094659","volume":"23","author":"W Shu","year":"2022","unstructured":"Shu W, Cai K, Xiong NN (2022) A short-term traffic flow prediction model based on an improved gate recurrent unit neural network. IEEE Trans Intell Transp Syst 23(9):16654\u201316665. https:\/\/doi.org\/10.1109\/TITS.2021.3094659","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"6055_CR18","doi-asserted-by":"publisher","unstructured":"Fouladgar M, Parchami M, Elmasri R, Ghaderi A (2017) Scalable deep traffic flow neural networks for urban traffic congestion prediction. In: 2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, USA, pp 2251\u20132258. https:\/\/doi.org\/10.1109\/IJCNN.2017.7966128.","DOI":"10.1109\/IJCNN.2017.7966128."},{"key":"6055_CR19","doi-asserted-by":"crossref","unstructured":"Yu H, Wu Z, Wang S, Wang Y, Ma X (2017) Spatiotemporal recurrent convolutional networks for traffic prediction in transportation networks. Sensors 17(7)","DOI":"10.3390\/s17071501"},{"key":"6055_CR20","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1016\/j.artint.2018.03.002","volume":"259","author":"J Zhang","year":"2018","unstructured":"Zhang J, Zheng Y, Qi D et al (2018) Predicting citywide crowd flows using deep spatio-temporal residual networks. Artif Intell 259:147\u2013166","journal-title":"Artif Intell"},{"issue":"10","key":"6055_CR21","doi-asserted-by":"publisher","first-page":"3913","DOI":"10.1109\/TITS.2019.2906365","volume":"20","author":"S Guo","year":"2019","unstructured":"Guo S, Lin Y, Li S, Chen Z, Wan H (2019) Deep spatial-temporal 3D convolutional neural networks for traffic data forecasting. IEEE Trans Intell Transp Syst 20(10):3913\u20133926. https:\/\/doi.org\/10.1109\/TITS.2019.2906365","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"6055_CR22","doi-asserted-by":"publisher","first-page":"24458","DOI":"10.1007\/s10489-023-04792-1","volume":"53","author":"X Sun","year":"2023","unstructured":"Sun X, Wang X, Huang B et al (2023) Multidirectional short-term traffic volume prediction based on spatiotemporal networks. Appl Intell 53:24458\u201324473. https:\/\/doi.org\/10.1007\/s10489-023-04792-1","journal-title":"Appl Intell"},{"key":"6055_CR23","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1016\/j.trc.2015.03.014","volume":"54","author":"X Ma","year":"2015","unstructured":"Ma X, Tao Z, Wang Y, Yu H, Wang Y (2015) Long short-term memory neural network for traffic speed prediction using remote microwave sensor data. Transp Res Part C Emerg Technol 54:187\u2013197","journal-title":"Transp Res Part C Emerg Technol"},{"issue":"6","key":"6055_CR24","doi-asserted-by":"publisher","first-page":"5615","DOI":"10.1109\/TITS.2021.3055258","volume":"23","author":"C Ma","year":"2022","unstructured":"Ma C, Dai G, Zhou J (2022) Short-term traffic flow prediction for urban road sections based on time series analysis and LSTM_BILSTM method. IEEE Trans Intell Transp Syst 23(6):5615\u20135624. https:\/\/doi.org\/10.1109\/TITS.2021.3055258","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"6055_CR25","doi-asserted-by":"publisher","first-page":"103502","DOI":"10.1109\/ACCESS.2023.3305398","volume":"11","author":"Y Chen","year":"2023","unstructured":"Chen Y, Guo J, Xu H, Huang J, Su L (2023) Improved long short-term memory-based periodic traffic volume prediction method. IEEE Access 11:103502\u2013103510. https:\/\/doi.org\/10.1109\/ACCESS.2023.3305398","journal-title":"IEEE Access"},{"issue":"4","key":"6055_CR26","doi-asserted-by":"publisher","first-page":"3728","DOI":"10.1109\/TITS.2021.3117835","volume":"24","author":"C Ma","year":"2023","unstructured":"Ma C, Zhao Y, Dai G, Xu X, Wong S-C (2023) A novel STFSA-CNN-GRU hybrid model for short-term traffic speed prediction. IEEE Trans Intell Transp Syst 24(4):3728\u20133737. https:\/\/doi.org\/10.1109\/TITS.2021.3117835","journal-title":"IEEE Trans Intell Transp Syst"},{"issue":"9","key":"6055_CR27","doi-asserted-by":"publisher","first-page":"16612","DOI":"10.1109\/TITS.2021.3113935","volume":"23","author":"H Hu","year":"2022","unstructured":"Hu H, Lin Z, Hu Q, Zhang Y (2022) Attention mechanism with spatial-temporal joint model for traffic flow speed prediction. IEEE Trans Intell Transp Syst 23(9):16612\u201316621. https:\/\/doi.org\/10.1109\/TITS.2021.3113935","journal-title":"IEEE Trans Intell Transp Syst"},{"issue":"7","key":"6055_CR28","doi-asserted-by":"publisher","first-page":"1909","DOI":"10.1109\/LCOMM.2023.3275327","volume":"27","author":"X Ma","year":"2023","unstructured":"Ma X, Zheng B, Jiang G, Liu L (2023) Cellular network traffic prediction based on correlation ConvLSTM and self-attention network. IEEE Commun Lett 27(7):1909\u20131912. https:\/\/doi.org\/10.1109\/LCOMM.2023.3275327","journal-title":"IEEE Commun Lett"},{"key":"6055_CR29","doi-asserted-by":"crossref","unstructured":"Yao H, Tang X, Wei H, Zheng G, Li Z (2019) Revisiting spatial-temporal similarity: a deep learning framework for traffic prediction. In: 33rd AAAI conference on artificial intelligence, pp 5668\u20135675","DOI":"10.1609\/aaai.v33i01.33015668"},{"issue":"8","key":"6055_CR30","doi-asserted-by":"publisher","first-page":"8846","DOI":"10.1109\/TITS.2023.3257759","volume":"24","author":"S Rahmani","year":"2023","unstructured":"Rahmani S, Baghbani A, Bouguila N, Patterson Z (2023) Graph neural networks for intelligent transportation systems: a survey. IEEE Trans Intell Transp Syst 24(8):8846\u20138885. https:\/\/doi.org\/10.1109\/TITS.2023.3257759","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"6055_CR31","doi-asserted-by":"publisher","first-page":"29153","DOI":"10.1007\/s10489-023-04976-9","volume":"53","author":"A Gupta","year":"2023","unstructured":"Gupta A, Maurya MK, Goyal N et al (2023) ISTGCN: integrated spatio-temporal modeling for traffic prediction using traffic graph convolution network. Appl Intell 53:29153\u201329168. https:\/\/doi.org\/10.1007\/s10489-023-04976-9","journal-title":"Appl Intell"},{"key":"6055_CR32","doi-asserted-by":"publisher","first-page":"18293","DOI":"10.1007\/s11227-023-05383-0","volume":"79","author":"Z Su","year":"2023","unstructured":"Su Z, Liu T, Hao X et al (2023) Spatial-temporal graph convolutional networks for traffic flow prediction considering multiple traffic parameters. J Supercomput 79:18293\u201318312. https:\/\/doi.org\/10.1007\/s11227-023-05383-0","journal-title":"J Supercomput"},{"issue":"9","key":"6055_CR33","doi-asserted-by":"publisher","first-page":"3848","DOI":"10.1109\/TITS.2019.2935152","volume":"21","author":"L Zhao","year":"2020","unstructured":"Zhao L et al (2020) T-GCN: a temporal graph convolutional network for traffic prediction. IEEE Trans Intell Transp Syst 21(9):3848\u20133858. https:\/\/doi.org\/10.1109\/TITS.2019.2935152","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"6055_CR34","unstructured":"Li Y, Yu R, Shahabi C, Liu Y (2018) Diffusion convolutional recurrent neural network: data-driven traffic forecasting. In: 6th International conference on learning representations, ICLR"},{"key":"6055_CR35","doi-asserted-by":"crossref","unstructured":"Yu B, Yin H, Zhu Z (2018) Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting. In: IJCAI International joint conference on artificial intelligence, pp 3634\u20133640","DOI":"10.24963\/ijcai.2018\/505"},{"key":"6055_CR36","doi-asserted-by":"crossref","unstructured":"Wu Z, Pan S, Long G, Jiang J, Zhang C (2019) Graph wavenet for deep spatial-temporal graph modeling. In: IJCAI International joint conference on artificial intelligence, pp 1907\u20131913","DOI":"10.24963\/ijcai.2019\/264"},{"issue":"11","key":"6055_CR37","doi-asserted-by":"publisher","first-page":"5415","DOI":"10.1109\/TKDE.2021.3056502","volume":"34","author":"S Guo","year":"2022","unstructured":"Guo S, Lin Y, Wan H, Li X, Cong G (2022) Learning dynamics and heterogeneity of spatial-temporal graph data for traffic forecasting. IEEE Trans Knowl Data Eng 34(11):5415\u20135428. https:\/\/doi.org\/10.1109\/TKDE.2021.3056502","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"6055_CR38","doi-asserted-by":"crossref","unstructured":"Wang X, Ma Y, Wang Y et al (2020) Traffic flow prediction via spatial temporal graph neural network. In: Proceedings of the world wide web conference, WWW, pp 1082\u20131092","DOI":"10.1145\/3366423.3380186"},{"key":"6055_CR39","doi-asserted-by":"crossref","unstructured":"Zheng C, Fan X, Wang C, Qi J (2020) GMAN: a graph multi-attention network for traffic prediction. In: Proceedings of the AAAI conference on artificial intelligence, pp 1234\u20131241","DOI":"10.1609\/aaai.v34i01.5477"},{"key":"6055_CR40","doi-asserted-by":"crossref","unstructured":"Wu Z, Pan S, Long G, Jiang J, Chang X, Zhang C (2020) Connecting the dots: multivariate time series forecasting with graph neural networks. In: Proceedings of the ACM SIGKDD international conference on knowledge discovery and data mining (KDD\u201920), pp 753\u2013763","DOI":"10.1145\/3394486.3403118"},{"key":"6055_CR41","doi-asserted-by":"crossref","unstructured":"Oreshkin BN, Amini A, Coyle L, Coates MJ (2021) FC-GAGA: fully connected gated graph architecture for spatio-temporal traffic forecasting. In: 35th AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v35i10.17114"},{"key":"6055_CR42","unstructured":"Chen X, Chen Y, He Z (2018) Urban traffic speed dataset of Guangzhou, Sun Yat-Sen University, China, Zenodo. https:\/\/zenodo.org\/record\/1205229"},{"key":"6055_CR43","doi-asserted-by":"publisher","unstructured":"Han L, Du B, Sun L, Fu Y, Lv Y, Xiong H (2021) Dynamic and multi-faceted spatio-temporal deep learning for traffic speed forecasting. In: Proceedings of the 27th ACM SIGKDD conference on knowledge discovery and data mining (KDD\u201921), pp 547\u2013555. https:\/\/doi.org\/10.1145\/3447548.3467275","DOI":"10.1145\/3447548.3467275"},{"key":"6055_CR44","doi-asserted-by":"crossref","unstructured":"Liu H, Dong Z, Jiang R, Deng J, Deng J, Chen Q, Song X (2023) Spatio-temporal adaptive embedding makes vanilla transformer sota for traffic forecasting. In: Proceedings of ACM international conference on information and knowledge management (CIKM), pp 4125\u20134129","DOI":"10.1145\/3583780.3615160"},{"key":"6055_CR45","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.120203","volume":"227","author":"Q Ren","year":"2023","unstructured":"Ren Q, Li Y, Liu Y (2023) Transformer-enhanced periodic temporal convolution network for long short-term traffic flow forecasting. Expert Syst Appl 227:120203","journal-title":"Expert Syst Appl"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-06055-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-024-06055-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-06055-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,2]],"date-time":"2025-01-02T15:05:47Z","timestamp":1735830347000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-024-06055-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,23]]},"references-count":45,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["6055"],"URL":"https:\/\/doi.org\/10.1007\/s10489-024-06055-z","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"type":"print","value":"0924-669X"},{"type":"electronic","value":"1573-7497"}],"subject":[],"published":{"date-parts":[[2024,11,23]]},"assertion":[{"value":"7 September 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 November 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}],"article-number":"14"}}