{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T15:50:41Z","timestamp":1759333841552,"version":"build-2065373602"},"reference-count":53,"publisher":"Springer Science and Business Media LLC","issue":"29","license":[{"start":{"date-parts":[[2025,9,4]],"date-time":"2025-09-04T00:00:00Z","timestamp":1756944000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,4]],"date-time":"2025-09-04T00:00:00Z","timestamp":1756944000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2025,10]]},"DOI":"10.1007\/s00521-025-11550-6","type":"journal-article","created":{"date-parts":[[2025,9,4]],"date-time":"2025-09-04T04:45:45Z","timestamp":1756961145000},"page":"24025-24049","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Improving DNNs for time-series classification using state and gradient abstraction-based preprocessing"],"prefix":"10.1007","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8086-2246","authenticated-orcid":false,"given":"Nevo","family":"Itzhak","sequence":"first","affiliation":[]},{"given":"Shahar","family":"Tal","sequence":"additional","affiliation":[]},{"given":"Hadas","family":"Cohen","sequence":"additional","affiliation":[]},{"given":"Osher","family":"Daniel","sequence":"additional","affiliation":[]},{"given":"Roze","family":"Kopylov","sequence":"additional","affiliation":[]},{"given":"Robert","family":"Moskovitch","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,9,4]]},"reference":[{"key":"11550_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecoinf.2021.101252","volume":"61","author":"C Capinha","year":"2021","unstructured":"Capinha C, Ceia-Hasse A, Kramer AM, Meijer C (2021) Deep learning for supervised classification of temporal data in ecology. Eco Inform 61:101252","journal-title":"Eco Inform"},{"key":"11550_CR2","unstructured":"Rabanser S, Januschowski T, Flunkert V, Salinas D, Gasthaus J (2020) The effectiveness of discretization in forecasting: An empirical study on neural time series models. arXiv preprint arXiv:2005.10111"},{"issue":"23","key":"11550_CR3","doi-asserted-by":"publisher","first-page":"3001","DOI":"10.3390\/electronics10233001","volume":"10","author":"A Alqahtani","year":"2021","unstructured":"Alqahtani A, Ali M, Xie X, Jones MW (2021) Deep time-series clustering: a review. Electronics 10(23):3001","journal-title":"Electronics"},{"issue":"4","key":"11550_CR4","doi-asserted-by":"publisher","first-page":"917","DOI":"10.1007\/s10618-019-00619-1","volume":"33","author":"H Ismail Fawaz","year":"2019","unstructured":"Ismail Fawaz H, Forestier G, Weber J, Idoumghar L, Muller P-A (2019) Deep learning for time series classification: a review. Data Min Knowl Disc 33(4):917\u2013963","journal-title":"Data Min Knowl Disc"},{"issue":"3","key":"11550_CR5","doi-asserted-by":"publisher","first-page":"606","DOI":"10.1007\/s10618-016-0483-9","volume":"31","author":"A Bagnall","year":"2017","unstructured":"Bagnall A, Lines J, Bostrom A, Large J, Keogh E (2017) The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances. Data Min Knowl Disc 31(3):606\u2013660","journal-title":"Data Min Knowl Disc"},{"key":"11550_CR6","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1016\/B978-0-12-811968-6.00009-7","volume":"2018","author":"GA Susto","year":"2018","unstructured":"Susto GA, Cenedese A, Terzi M (2018) Time-series classification methods: review and applications to power systems data. Big Data Appl Power Syst 2018:179\u2013220","journal-title":"Big Data Appl Power Syst"},{"issue":"2","key":"11550_CR7","doi-asserted-by":"publisher","first-page":"995","DOI":"10.1007\/s11277-022-09780-1","volume":"126","author":"T Alam","year":"2022","unstructured":"Alam T (2022) Blockchain-enabled deep reinforcement learning approach for performance optimization on the internet of things. Wireless Pers Commun 126(2):995\u20131011","journal-title":"Wireless Pers Commun"},{"issue":"4","key":"11550_CR8","doi-asserted-by":"publisher","first-page":"871","DOI":"10.1007\/s10618-014-0380-z","volume":"29","author":"R Moskovitch","year":"2015","unstructured":"Moskovitch R, Shahar Y (2015) Classification-driven temporal discretization of multivariate time series. Data Min Knowl Disc 29(4):871\u2013913","journal-title":"Data Min Knowl Disc"},{"key":"11550_CR9","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 25"},{"issue":"1","key":"11550_CR10","doi-asserted-by":"publisher","first-page":"373","DOI":"10.1007\/s12652-022-03897-8","volume":"15","author":"M Rostami","year":"2022","unstructured":"Rostami M, Farajollahi A, Parvin H (2022) Deep learning-based face detection and recognition on drones. J Ambient Intell Humaniz Comput 15(1):373\u2013387","journal-title":"J Ambient Intell Humaniz Comput"},{"key":"11550_CR11","unstructured":"Devlin J, Chang M-W, Lee K, Toutanova K (2018) Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805"},{"issue":"2","key":"11550_CR12","doi-asserted-by":"publisher","first-page":"1913","DOI":"10.1007\/s11277-022-09640-y","volume":"125","author":"KB Bhangale","year":"2022","unstructured":"Bhangale KB, Kothandaraman M (2022) Survey of deep learning paradigms for speech processing. Wirel Pers Commun 125(2):1913\u20131949","journal-title":"Wirel Pers Commun"},{"key":"11550_CR13","unstructured":"Shukla SN, Marlin BM (2020) A survey on principles, models and methods for learning from irregularly sampled time series. arXiv preprint arXiv:2012.00168"},{"key":"11550_CR14","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1016\/j.neucom.2021.02.046","volume":"441","author":"PB Weerakody","year":"2021","unstructured":"Weerakody PB, Wong KW, Wang G, Ela W (2021) A review of irregular time series data handling with gated recurrent neural networks. Neurocomputing 441:161\u2013178","journal-title":"Neurocomputing"},{"key":"11550_CR15","unstructured":"Huang L, Qin J, Zhou Y, Zhu F, Liu L, Shao L (2020) Normalization techniques in training dnns: Methodology, analysis and application. arXiv preprint arXiv:2009.12836"},{"key":"11550_CR16","unstructured":"Yi B-K, Faloutsos C (2000) Fast time sequence indexing for arbitrary Lp norms. In: VLDB, vol. 385, p. 99. Citeseer"},{"issue":"3","key":"11550_CR17","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1007\/PL00011669","volume":"3","author":"E Keogh","year":"2001","unstructured":"Keogh E, Chakrabarti K, Pazzani M, Mehrotra S (2001) Dimensionality reduction for fast similarity search in large time series databases. Knowl Inf Syst 3(3):263\u2013286","journal-title":"Knowl Inf Syst"},{"issue":"2","key":"11550_CR18","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1007\/s10618-007-0064-z","volume":"15","author":"J Lin","year":"2007","unstructured":"Lin J, Keogh E, Wei L, Lonardi S (2007) Experiencing sax: a novel symbolic representation of time series. Data Min Knowl Disc 15(2):107\u2013144","journal-title":"Data Min Knowl Disc"},{"key":"11550_CR19","doi-asserted-by":"crossref","unstructured":"Itzhak N, Nagori A, Lior E, Schvetz M, Lodha R, Sethi T, Moskovitch R (2020) Acute hypertensive episodes prediction. In: International conference on artificial intelligence in medicine. Springer, Cham, pp. 392\u2013402","DOI":"10.1007\/978-3-030-59137-3_35"},{"key":"11550_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2022.104198","volume":"134","author":"P Novitski","year":"2022","unstructured":"Novitski P, Cohen CM, Karasik A, Hodik G, Moskovitch R (2022) Temporal patterns selection for all-cause mortality prediction in T2D with ANNs. J Biomed Inform 134:104198","journal-title":"J Biomed Inform"},{"key":"11550_CR21","doi-asserted-by":"crossref","unstructured":"Lin J, Keogh E, Lonardi S, Chiu B (2003) A symbolic representation of time series, with implications for streaming algorithms. In: Proceedings of the 8th ACM SIGMOD workshop on research issues in data mining and knowledge discovery, pp 2\u201311. ACM","DOI":"10.1145\/882082.882086"},{"key":"11550_CR22","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1016\/j.ins.2014.09.038","volume":"294","author":"M-Y Chen","year":"2015","unstructured":"Chen M-Y, Chen B-T (2015) A hybrid fuzzy time series model based on granular computing for stock price forecasting. Inf Sci 294:227\u2013241","journal-title":"Inf Sci"},{"key":"11550_CR23","doi-asserted-by":"crossref","unstructured":"Novitski P, Cohen CM, Karasik A, Shalev V, Hodik G, Moskovitch R (2020) All-cause mortality prediction in T2D patients. In: International Conference on Artificial Intelligence in Medicine. Springer, pp 3\u201313","DOI":"10.1007\/978-3-030-59137-3_1"},{"key":"11550_CR24","doi-asserted-by":"crossref","unstructured":"Itzhak N, Tal S, Cohen H, Daniel O, Kopylov R, Moskovitch R (2022) Classification of univariate time series via temporal abstraction and deep learning. In: 2022 IEEE International Conference on Big Data (Big Data), pp 1260\u20131265. IEEE","DOI":"10.1109\/BigData55660.2022.10020752"},{"issue":"6","key":"11550_CR25","doi-asserted-by":"publisher","first-page":"1293","DOI":"10.1109\/JAS.2019.1911747","volume":"6","author":"HA Dau","year":"2019","unstructured":"Dau HA, Bagnall A, Kamgar K, Yeh C-CM, Zhu Y, Gharghabi S, Ratanamahatana CA, Keogh E (2019) The UCR time series archive. IEEE\/CAA J Autom Sinica 6(6):1293\u20131305","journal-title":"IEEE\/CAA J Autom Sinica"},{"issue":"2","key":"11550_CR26","doi-asserted-by":"publisher","first-page":"400","DOI":"10.1007\/s10618-014-0349-y","volume":"29","author":"MG Baydogan","year":"2015","unstructured":"Baydogan MG, Runger G (2015) Learning a symbolic representation for multivariate time series classification. Data Min Knowl Disc 29(2):400\u2013422","journal-title":"Data Min Knowl Disc"},{"issue":"1","key":"11550_CR27","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1002\/widm.1173","volume":"6","author":"S Ram\u00edrez-Gallego","year":"2016","unstructured":"Ram\u00edrez-Gallego S, Garc\u00eda S, Mouri\u00f1o-Tal\u00edn H, Mart\u00ednez-Rego D, Bol\u00f3n-Canedo V, Alonso-Betanzos A, Ben\u00edtez JM, Herrera F (2016) Data discretization: taxonomy and big data challenge. Wiley Interdiscip Rev Data Min Knowl Discov 6(1):5\u201321","journal-title":"Wiley Interdiscip Rev Data Min Knowl Discov"},{"key":"11550_CR28","doi-asserted-by":"crossref","unstructured":"Patel D, Hsu W, Lee ML (2008) Mining relationships among interval-based events for classification. In: Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, pp 393\u2013404. ACM","DOI":"10.1145\/1376616.1376658"},{"issue":"2\u20133","key":"11550_CR29","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1080\/00437956.1954.11659520","volume":"10","author":"ZS Harris","year":"1954","unstructured":"Harris ZS (1954) Distributional structure. Word 10(2\u20133):146\u2013162","journal-title":"Word"},{"issue":"4","key":"11550_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2508037.2508044","volume":"4","author":"I Batal","year":"2013","unstructured":"Batal I, Valizadegan H, Cooper GF, Hauskrecht M (2013) A temporal pattern mining approach for classifying electronic health record data. ACM Trans Intell Syst Technol (TIST) 4(4):1\u201322","journal-title":"ACM Trans Intell Syst Technol (TIST)"},{"issue":"3","key":"11550_CR31","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1109\/TCBB.2016.2591539","volume":"14","author":"R Moskovitch","year":"2016","unstructured":"Moskovitch R, Choi H, Hripcsak G, Tatonetti NP (2016) Prognosis of clinical outcomes with temporal patterns and experiences with one class feature selection. IEEE\/ACM Trans Comput Biol Bioinf 14(3):555\u2013563","journal-title":"IEEE\/ACM Trans Comput Biol Bioinf"},{"key":"11550_CR32","doi-asserted-by":"publisher","first-page":"111546","DOI":"10.1016\/j.patcog.2025.111546","volume":"168","author":"N Itzhak","year":"2025","unstructured":"Itzhak N, Jaroszewicz S, Moskovitch R (2025) Time-intervals-related pattern selection for continuous event prediction. Pattern Recog 168:111546. https:\/\/doi.org\/10.1016\/j.patcog.2025.111546","journal-title":"Pattern Recog"},{"key":"11550_CR33","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-025-06756-7","author":"N Itzhak","year":"2025","unstructured":"Itzhak N, Jaroszewicz S, Moskovitch R (2025) Temporal ensemble of multiple patterns\u2019 instances for continuous prediction of events. Machine Learn 114(5). https:\/\/doi.org\/10.1007\/s10994-025-06756-7","journal-title":"Machine Learn"},{"key":"11550_CR34","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-023-01910-w","author":"N Itzhak","year":"2023","unstructured":"Itzhak N, Jaroszewicz S, Moskovitch R (2023) Continuous prediction of a time intervals-related pattern\u2019s completion. Knowl Inf Syst 65: https:\/\/doi.org\/10.1007\/s10115-023-01910-w","journal-title":"Knowl Inf Syst"},{"key":"11550_CR35","doi-asserted-by":"crossref","unstructured":"Itzhak N, Jaroszewicz S, Moskovitch R (2023) Continuously Predicting the Completion of a Time Intervals Related Pattern. In: Advances in Knowledge Discovery and Data Mining (PAKDD 2023). Springer","DOI":"10.1007\/978-3-031-33374-3_19"},{"key":"11550_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2024.104665","author":"N Itzhak","year":"2024","unstructured":"Itzhak N, Jaroszewicz S, Moskovitch R (2024) Event prediction by estimating continuously the completion of a single temporal pattern\u2019s instances. J Biomed Info 156. https:\/\/doi.org\/10.1016\/j.jbi.2024.104665","journal-title":"J Biomed Info"},{"key":"11550_CR37","doi-asserted-by":"crossref","unstructured":"Senin P, Malinchik S (2013) Sax-vsm: Interpretable time series classification using sax and vector space model. In: 2013 IEEE 13th International Conference on Data Mining, pp 1175\u20131180. IEEE","DOI":"10.1109\/ICDM.2013.52"},{"key":"11550_CR38","doi-asserted-by":"crossref","unstructured":"Sch\u00e4fer P, H\u00f6gqvist M (2012) SFA: a symbolic fourier approximation and index for similarity search in high dimensional datasets. In: Proceedings of the 15th International Conference on Extending Database Technology, pp 516\u2013527","DOI":"10.1145\/2247596.2247656"},{"key":"11550_CR39","doi-asserted-by":"publisher","first-page":"1505","DOI":"10.1007\/s10618-014-0377-7","volume":"29","author":"P Sch\u00e4fer","year":"2015","unstructured":"Sch\u00e4fer P (2015) The boss is concerned with time series classification in the presence of noise. Data Min Knowl Disc 29:1505\u20131530","journal-title":"Data Min Knowl Disc"},{"key":"11550_CR40","doi-asserted-by":"crossref","unstructured":"Sch\u00e4fer P, Leser U (2017) Fast and accurate time series classification with weasel. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp 637\u2013646","DOI":"10.1145\/3132847.3132980"},{"key":"11550_CR41","unstructured":"Naduvil-Vadukootu S, Angryk RA, Riley P (2017) Evaluating preprocessing strategies for time series prediction using deep learning architectures. In: The Thirtieth International Flairs Conference"},{"issue":"5","key":"11550_CR42","doi-asserted-by":"publisher","first-page":"1321","DOI":"10.1109\/JBHI.2019.2942938","volume":"24","author":"J Niu","year":"2019","unstructured":"Niu J, Tang Y, Sun Z, Zhang W (2019) Inter-patient ECG classification with symbolic representations and multi-perspective convolutional neural networks. IEEE J Biomed Health Inform 24(5):1321\u20131332","journal-title":"IEEE J Biomed Health Inform"},{"key":"11550_CR43","doi-asserted-by":"publisher","first-page":"646","DOI":"10.1007\/s13198-022-01691-5","volume":"15","author":"R Gadekar","year":"2022","unstructured":"Gadekar R, Sarkar B, Gadekar A (2022) Model development for assessing inhibitors impacting industry 4.0 implementation in Indian manufacturing industries: an integrated ism-fuzzy micmac approach. Int J Syst Assur Eng Manag 15:646\u2013671","journal-title":"Int J Syst Assur Eng Manag"},{"issue":"4","key":"11550_CR44","doi-asserted-by":"publisher","first-page":"2029","DOI":"10.1007\/s13198-021-01616-8","volume":"13","author":"T Sreenivasula Reddy","year":"2022","unstructured":"Sreenivasula Reddy T, Sathya R, Nuka M (2022) Intuitionistic fuzzy rough sets and fruit fly algorithm for association rule mining. Int J Syst Assur Eng Manag 13(4):2029\u20132039","journal-title":"Int J Syst Assur Eng Manag"},{"issue":"8","key":"11550_CR45","doi-asserted-by":"publisher","first-page":"10947","DOI":"10.1007\/s12652-022-04362-2","volume":"14","author":"JK Samriya","year":"2023","unstructured":"Samriya JK, Kumar M, Tiwari R (2023) Energy-aware ACO-DNN optimization model for intrusion detection of unmanned aerial vehicle (UAVs). J Ambient Intell Humaniz Comput 14(8):10947\u201310962","journal-title":"J Ambient Intell Humaniz Comput"},{"key":"11550_CR46","doi-asserted-by":"crossref","unstructured":"Wang Z, Yan W, Oates T (2017) Time series classification from scratch with deep neural networks: a strong baseline. In: 2017 International Joint Conference on Neural Networks (IJCNN), pp 1578\u20131585. IEEE","DOI":"10.1109\/IJCNN.2017.7966039"},{"issue":"4","key":"11550_CR47","doi-asserted-by":"publisher","first-page":"541","DOI":"10.1162\/neco.1989.1.4.541","volume":"1","author":"Y LeCun","year":"1989","unstructured":"LeCun Y, Boser B, Denker JS, Henderson D, Howard RE, Hubbard W, Jackel LD (1989) Backpropagation applied to handwritten zip code recognition. Neural Comput 1(4):541\u2013551","journal-title":"Neural Comput"},{"key":"11550_CR48","doi-asserted-by":"crossref","unstructured":"Zheng Y, Liu Q, Chen E, Ge Y, Zhao JL (2014) Time series classification using multi-channels deep convolutional neural networks. In: International Conference on Web-age Information Management. Springer, pp 298\u2013310","DOI":"10.1007\/978-3-319-08010-9_33"},{"issue":"1","key":"11550_CR49","doi-asserted-by":"publisher","first-page":"162","DOI":"10.21629\/JSEE.2017.01.18","volume":"28","author":"B Zhao","year":"2017","unstructured":"Zhao B, Lu H, Chen S, Liu J, Wu D (2017) Convolutional neural networks for time series classification. J Syst Eng Electron 28(1):162\u2013169","journal-title":"J Syst Eng Electron"},{"issue":"6","key":"11550_CR50","doi-asserted-by":"publisher","first-page":"1936","DOI":"10.1007\/s10618-020-00710-y","volume":"34","author":"H Ismail Fawaz","year":"2020","unstructured":"Ismail Fawaz H, Lucas B, Forestier G, Pelletier C, Schmidt DF, Weber J, Webb GI, Idoumghar L, Muller P-A, Petitjean F (2020) Inceptiontime: finding alexnet for time series classification. Data Min Knowl Disc 34(6):1936\u20131962","journal-title":"Data Min Knowl Disc"},{"issue":"8","key":"11550_CR51","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735\u20131780","journal-title":"Neural Comput"},{"key":"11550_CR52","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1016\/j.neunet.2019.04.014","volume":"116","author":"F Karim","year":"2019","unstructured":"Karim F, Majumdar S, Darabi H, Harford S (2019) Multivariate LSTM-FCNs for time series classification. Neural Netw 116:237\u2013245","journal-title":"Neural Netw"},{"key":"11550_CR53","unstructured":"Grandini M, Bagli E, Visani G (2020) Metrics for multi-class classification: an overview. arXiv preprint arXiv:2008.05756"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11550-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-025-11550-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11550-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T05:23:00Z","timestamp":1759209780000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-025-11550-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,4]]},"references-count":53,"journal-issue":{"issue":"29","published-print":{"date-parts":[[2025,10]]}},"alternative-id":["11550"],"URL":"https:\/\/doi.org\/10.1007\/s00521-025-11550-6","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2025,9,4]]},"assertion":[{"value":"24 January 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 July 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 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":"All authors declare that there is no Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"The authors did not receive support from any organization for the submitted work. 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":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing financial interests"}}]}}