{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T15:51:43Z","timestamp":1773157903712,"version":"3.50.1"},"reference-count":47,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2020,4,15]],"date-time":"2020-04-15T00:00:00Z","timestamp":1586908800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2020,4,15]],"date-time":"2020-04-15T00:00:00Z","timestamp":1586908800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Cloud Comp"],"published-print":{"date-parts":[[2020,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Accurate precipitation estimation is significant since it matters to everyone on social and economic activities and is of great importance to monitor and forecast disasters. The traditional method utilizes an exponential relation between radar reflectivity factors and precipitation called Z-R relationship which has a low accuracy in precipitation estimation. With the rapid development of computing power in cloud computing, recent researches show that artificial intelligence is a promising approach, especially deep learning approaches in learning accurate patterns and appear well suited for the task of precipitation estimation, given an ample account of radar data. In this study, we introduce these approaches to the precipitation estimation, proposing two models based on the back propagation neural networks (BPNN) and convolutional neural networks (CNN) respectively, to compare with the traditional method in meteorological service systems. The results of the three approaches show that deep learning algorithms outperform the traditional method with 75.84<jats:italic>%<\/jats:italic> and 82.30<jats:italic>%<\/jats:italic> lower mean square errors respectively. Meanwhile, the proposed method with CNN achieves a better performance than that with BPNN for its ability to preserve the spatial information by maintaining the interconnection between pixels, which improves 26.75<jats:italic>%<\/jats:italic> compared to that with BPNN.<\/jats:p>","DOI":"10.1186\/s13677-020-00167-w","type":"journal-article","created":{"date-parts":[[2020,4,15]],"date-time":"2020-04-15T10:03:05Z","timestamp":1586944985000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Ground radar precipitation estimation with deep learning approaches in meteorological private cloud"],"prefix":"10.1186","volume":"9","author":[{"given":"Wei","family":"Tian","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Yi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guangyi","family":"Ma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yonghong","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,4,15]]},"reference":[{"issue":"1","key":"167_CR1","doi-asserted-by":"publisher","first-page":"12","DOI":"10.26599\/BDMA.2018.9020028","volume":"2","author":"C Kong","year":"2018","unstructured":"Kong C, Luo G, Tian L, Cao X (2018) Disseminating authorized content via data analysis in opportunistic social networks. Big Data Min Anal 2(1):12\u201324.","journal-title":"Big Data Min Anal"},{"issue":"1","key":"167_CR2","doi-asserted-by":"publisher","first-page":"48","DOI":"10.26599\/BDMA.2018.9020031","volume":"2","author":"S Kumar","year":"2018","unstructured":"Kumar S, Singh M (2018) Big data analytics for healthcare industry: impact, applications, and tools. Big Data Min Anal 2(1):48\u201357.","journal-title":"Big Data Min Anal"},{"issue":"2","key":"167_CR3","doi-asserted-by":"publisher","first-page":"100","DOI":"10.26599\/BDMA.2018.9020034","volume":"2","author":"JS He","year":"2019","unstructured":"He JS, Han M, Ji S, Du T, Li Z (2019) Spreading social influence with both positive and negative opinions in online networks. Big Data Min Anal 2(2):100\u2013117.","journal-title":"Big Data Min Anal"},{"issue":"9513","key":"167_CR4","doi-asserted-by":"publisher","first-page":"859","DOI":"10.1016\/S0140-6736(06)68079-3","volume":"367","author":"AJ McMichael","year":"2006","unstructured":"McMichael AJ, Woodruff RE, Hales S, et al. (2006) Climate change and human health: present and future risks. The Lancet 367(9513):859\u2013869. Elsevier.","journal-title":"The Lancet"},{"issue":"6","key":"167_CR5","doi-asserted-by":"publisher","first-page":"1336","DOI":"10.1016\/j.camwa.2015.07.022","volume":"70","author":"W Tian","year":"2015","unstructured":"Tian W, Ma T, Zheng Y, Wang X, Tian Y, Al-Dhelaan A, Al-Rodhaan M (2015) Weighted curvature-preserving pde image filtering method. Comput Math Appl 70(6):1336\u20131344.","journal-title":"Comput Math Appl"},{"issue":"23","key":"167_CR6","doi-asserted-by":"publisher","first-page":"2801","DOI":"10.3390\/rs11232801","volume":"11","author":"Y Zhang","year":"2019","unstructured":"Zhang Y, Ge T, Tian W, Liou Y-A (2019) Debris flow susceptibility mapping using machine-learning techniques in shigatse area, china. Remote Sens 11(23):2801.","journal-title":"Remote Sens"},{"issue":"9","key":"167_CR7","doi-asserted-by":"publisher","first-page":"1205","DOI":"10.1175\/BAMS-84-9-1205","volume":"84","author":"KE Trenberth","year":"2003","unstructured":"Trenberth KE, Dai A, Rasmussen RM, Parsons DB (2003) The changing character of precipitation. Bull Am Meteorol Soc 84(9):1205\u20131218.","journal-title":"Bull Am Meteorol Soc"},{"key":"167_CR8","doi-asserted-by":"publisher","first-page":"101631","DOI":"10.1016\/j.sysarc.2019.08.004","volume":"100","author":"J Zhou","year":"2019","unstructured":"Zhou J, Wang T, Cong P, Lu P, Wei T, Chen M (2019) Cost and makespan-aware workflow scheduling in hybrid clouds. J Syst Archit 100:101631.","journal-title":"J Syst Archit"},{"key":"167_CR9","doi-asserted-by":"publisher","unstructured":"Qi L, He Q, Chen F, Dou W, Wan S, Zhang X, Xu X (2019) Finding all you need: Web apis recommendation in web of things through keywords search. IEEE Trans Comput Soc Syst. https:\/\/doi.org\/10.1109\/tcss.2019.2906925.","DOI":"10.1109\/tcss.2019.2906925"},{"issue":"21","key":"167_CR10","doi-asserted-by":"publisher","first-page":"7773","DOI":"10.1175\/JCLI-D-15-0618.1","volume":"29","author":"M Gehne","year":"2016","unstructured":"Gehne M, Hamill TM, Kiladis GN, Trenberth KE (2016) Comparison of global precipitation estimates across a range of temporal and spatial scales. J Climate 29(21):7773\u20137795.","journal-title":"J Climate"},{"issue":"1","key":"167_CR11","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1175\/1525-7541(2001)002<0036:GPAODD>2.0.CO;2","volume":"2","author":"GJ Huffman","year":"2001","unstructured":"Huffman GJ, Adler RF, Morrissey MM, Bolvin DT, Curtis S, Joyce R, McGavock B, Susskind J (2001) Global precipitation at one-degree daily resolution from multisatellite observations. J Hydrometeorol 2(1):36\u201350.","journal-title":"J Hydrometeorol"},{"issue":"12","key":"167_CR12","doi-asserted-by":"publisher","first-page":"1785","DOI":"10.1109\/TC.2019.2935042","volume":"68","author":"J Zhou","year":"2019","unstructured":"Zhou J, Hu XS, Ma Y, Sun J, Wei T, Hu S (2019) Improving availability of multicore real-time systems suffering both permanent and transient faults. IEEE Trans Comput 68(12):1785\u20131801.","journal-title":"IEEE Trans Comput"},{"issue":"3","key":"167_CR13","doi-asserted-by":"publisher","first-page":"181","DOI":"10.26599\/BDMA.2019.9020002","volume":"2","author":"M Bouazizi","year":"2019","unstructured":"Bouazizi M, Ohtsuki T (2019) Multi-class sentiment analysis on twitter: Classification performance and challenges. Big Data Min Anal 2(3):181\u2013194.","journal-title":"Big Data Min Anal"},{"key":"167_CR14","doi-asserted-by":"publisher","unstructured":"Wang S, Zhou A, Bao R, Chou W, Yau SS (2018) Towards green service composition approach in the cloud. IEEE Trans Serv Comput. https:\/\/doi.org\/10.1109\/tsc.2018.2868356.","DOI":"10.1109\/tsc.2018.2868356"},{"key":"167_CR15","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1016\/j.jnca.2019.02.008","volume":"133","author":"X Xu","year":"2019","unstructured":"Xu X, Li Y, Huang T, Xue Y, Peng K, Qi L, Dou W (2019) An energy-aware computation offloading method for smart edge computing in wireless metropolitan area networks. J Netw Comput Appl 133:75\u201385.","journal-title":"J Netw Comput Appl"},{"issue":"7","key":"167_CR16","doi-asserted-by":"publisher","first-page":"1088","DOI":"10.1175\/1520-0450(2000)039<1088:IUIZRR>2.0.CO;2","volume":"39","author":"E Campos","year":"2000","unstructured":"Campos E, Zawadzki I (2000) Instrumental uncertainties in z\u2013r relations. J Appl Meteorol 39(7):1088\u20131102.","journal-title":"J Appl Meteorol"},{"key":"167_CR17","doi-asserted-by":"publisher","unstructured":"Gong W, Qi L, Xu Y (2018) Privacy-aware multidimensional mobile service quality prediction and recommendation in distributed fog environment. Wirel Commun Mobile Comput 2018. https:\/\/doi.org\/10.1155\/2018\/3075849.","DOI":"10.1155\/2018\/3075849"},{"key":"167_CR18","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1016\/j.future.2019.01.012","volume":"96","author":"X Xu","year":"2019","unstructured":"Xu X, Xue Y, Qi L, Yuan Y, Zhang X, Umer T, Wan S (2019) An edge computing-enabled computation offloading method with privacy preservation for internet of connected vehicles. Futur Gener Comput Syst 96:89\u2013100.","journal-title":"Futur Gener Comput Syst"},{"issue":"1-4","key":"167_CR19","doi-asserted-by":"publisher","first-page":"216","DOI":"10.1016\/S0022-1694(01)00611-4","volume":"260","author":"P Arnaud","year":"2002","unstructured":"Arnaud P, Bouvier C, Cisneros L, Dominguez R (2002) Influence of rainfall spatial variability on flood prediction. J Hydrol 260(1-4):216\u2013230.","journal-title":"J Hydrol"},{"key":"167_CR20","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1016\/j.jnca.2018.09.006","volume":"124","author":"X Xu","year":"2018","unstructured":"Xu X, Fu S, Qi L, Zhang X, Liu Q, He Q, Li S (2018) An iot-oriented data placement method with privacy preservation in cloud environment. J Netw Comput Appl 124:148\u2013157.","journal-title":"J Netw Comput Appl"},{"issue":"1","key":"167_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13638-015-0498-8","volume":"2019","author":"H Liu","year":"2019","unstructured":"Liu H, Kou H, Yan C, Qi L (2019) Link prediction in paper citation network to construct paper correlation graph. EURASIP J Wirel Commun Netw 2019(1):1\u201312.","journal-title":"EURASIP J Wirel Commun Netw"},{"key":"167_CR22","unstructured":"Wang S, Zhou A, Yang M, Sun L, Hsu C-H, et al. (2017) Service composition in cyber-physical-social systems. IEEE Trans Emerg Top Comput."},{"key":"167_CR23","doi-asserted-by":"publisher","unstructured":"Zhou J, Sun J, Cong P, Liu Z, Wei T, Zhou X, Hu SSecurity-Critical Energy-Aware Task Scheduling for Heterogeneous Real-Time MPSoCs in IoT. in press. https:\/\/doi.org\/10.1109\/tsc.2019.2963301.","DOI":"10.1109\/tsc.2019.2963301"},{"key":"167_CR24","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.neucom.2016.12.038","volume":"234","author":"W Liu","year":"2017","unstructured":"Liu W, Wang Z, Liu X, Zeng N, Liu Y, Alsaadi FE (2017) A survey of deep neural network architectures and their applications. Neurocomputing 234:11\u201326.","journal-title":"Neurocomputing"},{"key":"167_CR25","doi-asserted-by":"publisher","first-page":"522","DOI":"10.1016\/j.future.2018.12.055","volume":"95","author":"X Xu","year":"2019","unstructured":"Xu X, Liu Q, Luo Y, Peng K, Zhang X, Meng S, Qi L (2019) A computation offloading method over big data for iot-enabled cloud-edge computing. Futur Gener Comput Syst 95:522\u2013533.","journal-title":"Futur Gener Comput Syst"},{"key":"167_CR26","doi-asserted-by":"publisher","first-page":"636","DOI":"10.1016\/j.future.2018.02.050","volume":"88","author":"L Qi","year":"2018","unstructured":"Qi L, Zhang X, Dou W, Hu C, Yang C, Chen J (2018) A two-stage locality-sensitive hashing based approach for privacy-preserving mobile service recommendation in cross-platform edge environment. Futur Gener Comput Syst 88:636\u2013643.","journal-title":"Futur Gener Comput Syst"},{"key":"167_CR27","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","volume":"42","author":"G Litjens","year":"2017","unstructured":"Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, Van Der Laak JA, Van Ginneken B, S\u00e1nchez CI (2017) A survey on deep learning in medical image analysis. Med Image Anal 42:60\u201388.","journal-title":"Med Image Anal"},{"key":"167_CR28","doi-asserted-by":"publisher","unstructured":"Darji MP, Dabhi VK, Prajapati HB (2015) Rainfall forecasting using neural network: A survey In: 2015 International Conference on Advances in Computer Engineering and Applications, 706\u2013713.. IEEE. https:\/\/doi.org\/10.1109\/icacea.2015.7164782.","DOI":"10.1109\/icacea.2015.7164782"},{"key":"167_CR29","doi-asserted-by":"crossref","unstructured":"Nayak DR, Mahapatra A, Mishra P (2013) A survey on rainfall prediction using artificial neural network. Int J Comput Appl 72(16).","DOI":"10.5120\/12580-9217"},{"issue":"1","key":"167_CR30","doi-asserted-by":"publisher","first-page":"138","DOI":"10.1016\/j.asr.2013.10.005","volume":"53","author":"M Lazri","year":"2014","unstructured":"Lazri M, Ameur S, Mohia Y (2014) Instantaneous rainfall estimation using neural network from multispectral observations of seviri radiometer and its application in estimation of daily and monthly rainfall. Adv Space Res 53(1):138\u2013155.","journal-title":"Adv Space Res"},{"key":"167_CR31","doi-asserted-by":"publisher","unstructured":"Hern\u00e1ndez E, Sanchez-Anguix V, Julian V, Palanca J, Duque N (2016) Rainfall prediction: A deep learning approach In: International Conference on Hybrid Artificial Intelligence Systems, 151\u2013162.. Springer. https:\/\/doi.org\/10.1007\/978-3-319-32034-2_13.","DOI":"10.1007\/978-3-319-32034-2_13"},{"key":"167_CR32","doi-asserted-by":"publisher","first-page":"30865","DOI":"10.1109\/ACCESS.2018.2839699","volume":"6","author":"F Beritelli","year":"2018","unstructured":"Beritelli F, Capizzi G, Sciuto GL, Napoli C, Scaglione F (2018) Rainfall estimation based on the intensity of the received signal in a lte\/4g mobile terminal by using a probabilistic neural network. IEEE Access 6:30865\u201330873.","journal-title":"IEEE Access"},{"key":"167_CR33","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1016\/j.atmosres.2018.05.001","volume":"211","author":"F Ouallouche","year":"2018","unstructured":"Ouallouche F, Lazri M, Ameur S (2018) Improvement of rainfall estimation from msg data using random forests classification and regression. Atmos Res 211:62\u201372.","journal-title":"Atmos Res"},{"key":"167_CR34","doi-asserted-by":"publisher","unstructured":"Zhang P, Jia Y, Gao J, Song W, Leung HK (2018) Short-term rainfall forecasting using multi-layer perceptron. IEEE Trans Big Data. https:\/\/doi.org\/10.1109\/tbdata.2018.2871151.","DOI":"10.1109\/tbdata.2018.2871151"},{"key":"167_CR35","doi-asserted-by":"publisher","unstructured":"Folino G, Guarascio M, Chiaravalloti F, Gabriele S (2019) A deep learning based architecture for rainfall estimation integrating heterogeneous data sources In: 2019 International Joint Conference on Neural Networks (IJCNN), 1\u20138.. IEEE. https:\/\/doi.org\/10.1109\/ijcnn.2019.8852229.","DOI":"10.1109\/ijcnn.2019.8852229"},{"issue":"12","key":"167_CR36","doi-asserted-by":"publisher","first-page":"2273","DOI":"10.1175\/JHM-D-19-0110.1","volume":"20","author":"M Sadeghi","year":"2019","unstructured":"Sadeghi M, Asanjan AA, Faridzad M, Nguyen P, Hsu K, Sorooshian S, Braithwaite D (2019) Persiann-cnn: Precipitation estimation from remotely sensed information using artificial neural networks\u2013convolutional neural networks. J Hydrometeorol 20(12):2273\u20132289.","journal-title":"J Hydrometeorol"},{"key":"167_CR37","doi-asserted-by":"publisher","unstructured":"Xu X, Mo R, Dai F, Lin W, Wan S, Dou W (2019) Dynamic resource provisioning with fault tolerance for data-intensive meteorological workflows in cloud. IEEE Trans Ind Inform. https:\/\/doi.org\/10.1109\/tii.2019.2959258.","DOI":"10.1109\/tii.2019.2959258"},{"key":"167_CR38","doi-asserted-by":"publisher","unstructured":"Qi L, Chen Y, Yuan Y, Fu S, Zhang X, Xu X (2019) A qos-aware virtual machine scheduling method for energy conservation in cloud-based cyber-physical systems. World Wide Web:1\u201323. https:\/\/doi.org\/10.1007\/s11280-019-00684-y.","DOI":"10.1007\/s11280-019-00684-y"},{"key":"167_CR39","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1016\/j.jpdc.2018.06.008","volume":"127","author":"S Wang","year":"2019","unstructured":"Wang S, Zhao Y, Xu J, Yuan J, Hsu C-H (2019) Edge server placement in mobile edge computing. J Parallel Distrib Comput 127:160\u2013168.","journal-title":"J Parallel Distrib Comput"},{"issue":"3","key":"167_CR40","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1175\/BAMS-D-15-00164.1","volume":"97","author":"M Gosset","year":"2016","unstructured":"Gosset M, Kunstmann H, Zougmore F, Cazenave F, Leijnse H, Uijlenhoet R, Chwala C, Keis F, Doumounia A, Boubacar B, et al. (2016) Improving rainfall measurement in gauge poor regions thanks to mobile telecommunication networks. Bull Am Meteorol Soc 97(3):49\u201351.","journal-title":"Bull Am Meteorol Soc"},{"key":"167_CR41","doi-asserted-by":"publisher","unstructured":"Wu W, Zou H, Shan J, Wu S (2018) A dynamical zr relationship for precipitation estimation based on radar echo-top height classification. Adv Meteorol 2018. https:\/\/doi.org\/10.1155\/2018\/8202031.","DOI":"10.1155\/2018\/8202031"},{"key":"167_CR42","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1016\/j.knosys.2016.10.001","volume":"114","author":"AA Hameed","year":"2016","unstructured":"Hameed AA, Karlik B, Salman MS (2016) Back-propagation algorithm with variable adaptive momentum. Knowl Based Syst 114:79\u201387.","journal-title":"Knowl Based Syst"},{"key":"167_CR43","doi-asserted-by":"publisher","unstructured":"Bottou L (2012) Stochastic gradient descent tricks In: Neural Networks: Tricks of the Trade, 421\u2013436.. Springer. https:\/\/doi.org\/10.1007\/978-3-642-35289-8_25.","DOI":"10.1007\/978-3-642-35289-8_25"},{"key":"167_CR44","doi-asserted-by":"publisher","unstructured":"LeCun YA, Bottou L, Orr GB, M\u00fcller K-R (2012) Efficient backprop In: Neural Networks: Tricks of the Trade, 9\u201348.. Springer. https:\/\/doi.org\/10.1007\/3-540-49430-8_2.","DOI":"10.1007\/3-540-49430-8_2"},{"issue":"6","key":"167_CR45","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1109\/MSP.2017.2739299","volume":"34","author":"MT McCann","year":"2017","unstructured":"McCann MT, Jin KH, Unser M (2017) Convolutional neural networks for inverse problems in imaging: A review. IEEE Signal Process Mag 34(6):85\u201395.","journal-title":"IEEE Signal Process Mag"},{"key":"167_CR46","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1016\/j.ins.2016.01.039","volume":"364","author":"L Zhang","year":"2016","unstructured":"Zhang L, Suganthan PN (2016) A survey of randomized algorithms for training neural networks. Inf Sci 364:146\u2013155.","journal-title":"Inf Sci"},{"issue":"9","key":"167_CR47","first-page":"2508","volume":"36","author":"Y Li","year":"2016","unstructured":"Li Y, Hao Z, Lei H (2016) Survey of convolutional neural network. J Comput Appl 36(9):2508\u20132515.","journal-title":"J Comput Appl"}],"container-title":["Journal of Cloud Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13677-020-00167-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13677-020-00167-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13677-020-00167-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,14]],"date-time":"2021-04-14T23:20:48Z","timestamp":1618442448000},"score":1,"resource":{"primary":{"URL":"https:\/\/journalofcloudcomputing.springeropen.com\/articles\/10.1186\/s13677-020-00167-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,4,15]]},"references-count":47,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2020,12]]}},"alternative-id":["167"],"URL":"https:\/\/doi.org\/10.1186\/s13677-020-00167-w","relation":{},"ISSN":["2192-113X"],"issn-type":[{"value":"2192-113X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,4,15]]},"assertion":[{"value":"13 January 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 March 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 April 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare that they have no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"22"}}