{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T21:36:42Z","timestamp":1779399402170,"version":"3.53.1"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,1,8]],"date-time":"2024-01-08T00:00:00Z","timestamp":1704672000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,1,8]],"date-time":"2024-01-08T00:00:00Z","timestamp":1704672000000},"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":["Energy Inform"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Voltage stability detection is currently still becoming the main issue in the modern integrated renewable energy power systems. To assess the voltage stability, the classical methods based on continuation power flow (CPF) technique were used to show nose curve. However, the classical methods require complete model of power system and long computation time. Data driven analysis and synchronized real time measurement technologies currently are developing in power systems monitoring, including the stability detection. The detection method is built based on the historical event model and uses the real time measurement as an input. For that reason, the algorithm to detect the voltage instability and critical bus is proposed using the artificial neural network (ANN) technique to represent the historical event model using the PMU measurement data. The ANN model architecture for this application is developed by creating seven hidden layers consisting of one normalization, four rectifier linear unit, one softmax and one sigmoid layer. To warrant the accuracy, the k-fold cross-validation is used. The algorithm is simulated on modified IEEE 14 test system which consider different loading scenario, line contingency, number of PMU and Photovoltaic (PV) integration. To mimic the actual historical data, the synthetic data is generated and labelled. The result shows that the proposed method can represent the complete power system model by giving high accuracy which for voltage stability detection is\u2009&gt;\u200997% and critical buses detection is\u2009&gt;\u200996% for all scenarios. Moreover, the required computation time is between 16 and 18\u00a0s per detection which makes the scalability to the real time detection is reasonable.<\/jats:p>","DOI":"10.1186\/s42162-024-00302-w","type":"journal-article","created":{"date-parts":[[2024,1,8]],"date-time":"2024-01-08T09:02:45Z","timestamp":1704704565000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["A proposed PMU-based voltage stability and critical bus detection method using artificial neural network"],"prefix":"10.1186","volume":"7","author":[{"given":"Lesnanto Multa","family":"Putranto","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Izzuddin Fathin","family":"Azhar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,1,8]]},"reference":[{"key":"302_CR1","doi-asserted-by":"publisher","unstructured":"Adhikari A, Naetiladdanon S, Sagswang A, Gurung S (2020) Comparison of voltage stability assessment using different machine learning algorithms. 2020 IEEE 4th Conference on Energy Internet and Energy System Integration (EI2), IEEE; p. 2023\u20136. https:\/\/doi.org\/10.1109\/EI250167.2020.9346750","DOI":"10.1109\/EI250167.2020.9346750"},{"key":"302_CR2","doi-asserted-by":"publisher","first-page":"113512","DOI":"10.1109\/ACCESS.2020.3003568","volume":"8","author":"OA Alimi","year":"2020","unstructured":"Alimi OA, Ouahada K, Abu-Mahfouz AM (2020) A review of machine learning approaches to power system security and stability. IEEE Access 8:113512\u2013113531. https:\/\/doi.org\/10.1109\/ACCESS.2020.3003568","journal-title":"IEEE Access"},{"key":"302_CR3","doi-asserted-by":"publisher","first-page":"1258","DOI":"10.3390\/e23101258","volume":"23","author":"T Al-Shehari","year":"2021","unstructured":"Al-Shehari T, Alsowail RA (2021) An insider data leakage detection using one-hot encoding, synthetic minority oversampling and machine learning techniques. Entropy 23:1258. https:\/\/doi.org\/10.3390\/e23101258","journal-title":"Entropy"},{"key":"302_CR4","volume-title":"Big data application in power systems","author":"R Arghandeh","year":"2017","unstructured":"Arghandeh R, Zhou Y (2017) Big data application in power systems, 1st edn. Elsevier Science","edition":"1"},{"key":"302_CR5","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1016\/j.ijepes.2016.11.008","volume":"87","author":"SM Ashraf","year":"2017","unstructured":"Ashraf SM, Gupta A, Choudhary DK, Chakrabarti S (2017) Voltage stability monitoring of power systems using reduced network and artificial neural network. Int J Electr Power Energy Syst 87:43\u201351. https:\/\/doi.org\/10.1016\/j.ijepes.2016.11.008","journal-title":"Int J Electr Power Energy Syst"},{"key":"302_CR6","doi-asserted-by":"publisher","unstructured":"Ayub M, El-Alfy E-SM (2020) Impact of Normalization on BiLSTM based models for energy disaggregation. 2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), IEEE; p. 1\u20136. https:\/\/doi.org\/10.1109\/ICDABI51230.2020.9325593","DOI":"10.1109\/ICDABI51230.2020.9325593"},{"key":"302_CR7","unstructured":"Azhar IF, Putranto LM, Irnawan R (2022) Pengembangan Metode Deteksi Stabilitas Transien Berbasis PMU Menggunakan Algoritma CNN-LSTM dengan Memperhatikan Runtun-Waktu Data. Universitas Gadjah Mada, 2022"},{"key":"302_CR8","doi-asserted-by":"publisher","unstructured":"Berrar D (2019) Cross-validation. Encyclopedia of bioinformatics and computational biology, vol. 1\u20133, Elsevier; p. 542\u20135. https:\/\/doi.org\/10.1016\/B978-0-12-809633-8.20349-X","DOI":"10.1016\/B978-0-12-809633-8.20349-X"},{"key":"302_CR9","doi-asserted-by":"publisher","first-page":"1533","DOI":"10.1109\/TPWRS.2008.2002175","volume":"23","author":"F Capitanescu","year":"2008","unstructured":"Capitanescu F, Wehenkel L (2008) A new iterative approach to the corrective security-constrained optimal power flow problem. IEEE Trans Power Syst 23:1533\u20131541. https:\/\/doi.org\/10.1109\/TPWRS.2008.2002175","journal-title":"IEEE Trans Power Syst"},{"key":"302_CR10","doi-asserted-by":"publisher","first-page":"1690","DOI":"10.1109\/TPWRS.2007.907528","volume":"22","author":"F Capitanescu","year":"2007","unstructured":"Capitanescu F, Glavic M, Ernst D, Wehenkel L (2007) Contingency filtering techniques for preventive security-constrained optimal power flow. IEEE Trans Power Syst 22:1690\u20131697. https:\/\/doi.org\/10.1109\/TPWRS.2007.907528","journal-title":"IEEE Trans Power Syst"},{"key":"302_CR11","doi-asserted-by":"publisher","first-page":"222544","DOI":"10.1109\/ACCESS.2020.3043935","volume":"8","author":"KD Dharmapala","year":"2020","unstructured":"Dharmapala KD, Rajapakse A, Narendra K, Zhang Y (2020) Machine learning based real-time monitoring of long-term voltage stability using voltage stability indices. IEEE Access 8:222544\u2013222555. https:\/\/doi.org\/10.1109\/ACCESS.2020.3043935","journal-title":"IEEE Access"},{"key":"302_CR12","unstructured":"DIgSILENT GmbH (2020) 14 Bus System"},{"key":"302_CR13","doi-asserted-by":"publisher","first-page":"189","DOI":"10.22146\/ijccs.42036","volume":"13","author":"MF Dzulqarnain","year":"2019","unstructured":"Dzulqarnain MF, Suprapto S, Makhrus F (2019) Improvement of convolutional neural network accuracy on Salak classification based quality on digital image. IJCCS Indonesian J Comput Cybern Syst 13:189. https:\/\/doi.org\/10.22146\/ijccs.42036","journal-title":"IJCCS Indonesian J Comput Cybern Syst"},{"key":"302_CR14","doi-asserted-by":"publisher","DOI":"10.1214\/ss\/1177013815","author":"B Efron","year":"1986","unstructured":"Efron B, Tibshirani R (1986) Bootstrap methods for standard errors, confidence intervals, and other measures of statistical accuracy. Stat Sci. https:\/\/doi.org\/10.1214\/ss\/1177013815","journal-title":"Stat Sci"},{"key":"302_CR15","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1006\/jcss.1997.1504","volume":"55","author":"Y Freund","year":"1997","unstructured":"Freund Y, Schapire RE (1997) A decision-theoretic generalization of on-line learning and an application to boosting. J Comput Syst Sci 55:119\u2013139. https:\/\/doi.org\/10.1006\/jcss.1997.1504","journal-title":"J Comput Syst Sci"},{"key":"302_CR16","doi-asserted-by":"publisher","first-page":"322","DOI":"10.1109\/TSSC.1969.300225","volume":"5","author":"K Fukushima","year":"1969","unstructured":"Fukushima K (1969) Visual feature extraction by a multilayered network of analog threshold elements. IEEE Trans Syst Sci Cybern 5:322\u2013333. https:\/\/doi.org\/10.1109\/TSSC.1969.300225","journal-title":"IEEE Trans Syst Sci Cybern"},{"key":"302_CR17","volume-title":"Neural networks: a comprehensive foundation","author":"SS Haykin","year":"1999","unstructured":"Haykin SS (1999) Neural networks: a comprehensive foundation, 2nd edn. Prentice Hall","edition":"2"},{"key":"302_CR18","unstructured":"Karki MJ (2009) Methods for online voltage stability monitoring. Master Thesis. Iowa State University"},{"key":"302_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.2200\/S00822ED1V01Y201712COV015","volume":"8","author":"S Khan","year":"2018","unstructured":"Khan S, Rahmani H, Shah SAA, Bennamoun M (2018) A guide to convolutional neural networks for computer vision. Synth Lect Comput vis 8:1\u2013207. https:\/\/doi.org\/10.2200\/S00822ED1V01Y201712COV015","journal-title":"Synth Lect Comput vis"},{"key":"302_CR20","unstructured":"Kingma DP, Ba J (2014) Adam: a method for stochastic optimization"},{"key":"302_CR21","unstructured":"Kohavi R (1995) A study of cross-validation and bootstrap for accuracy estimation and model selection. Proceedings of the 14th International Joint Conference on Artificial Intelligence. Vol 2, San Francisco: Morgan Kaufmann Publishers Inc.; p. 1137\u201343"},{"key":"302_CR22","volume-title":"Power system stability and control","author":"P Kundur","year":"1994","unstructured":"Kundur P (1994) Power system stability and control. McGraw-Hill Professional, New York"},{"key":"302_CR23","doi-asserted-by":"publisher","first-page":"1387","DOI":"10.1109\/TPWRS.2004.825981","volume":"19","author":"P Kundur","year":"2004","unstructured":"Kundur P, Paserba J, Ajjarapu V, Andersson G, Bose A, Canizares C et al (2004) Definition and classification of power system stability IEEE\/CIGRE joint task force on stability terms and definitions. IEEE Trans Power Syst 19:1387\u20131401. https:\/\/doi.org\/10.1109\/TPWRS.2004.825981","journal-title":"IEEE Trans Power Syst"},{"key":"302_CR24","doi-asserted-by":"publisher","first-page":"3117","DOI":"10.1109\/TSG.2017.2693394","volume":"8","author":"V Malbasa","year":"2017","unstructured":"Malbasa V, Zheng C, Chen P-C, Popovic T, Kezunovic M (2017) Voltage stability prediction using active machine learning. IEEE Trans Smart Grid 8:3117\u20133124. https:\/\/doi.org\/10.1109\/TSG.2017.2693394","journal-title":"IEEE Trans Smart Grid"},{"key":"302_CR25","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1007\/978-3-030-77696-1_9","volume-title":"Application of machine learning and deep learning methods to power system problems","author":"A Mollaiee","year":"2021","unstructured":"Mollaiee A, Azad S, Ameli MT, Nazari-Heris M (2021) Voltage stability assessment in power grids using novel machine learning-based methods. In: Nazari-Heris M, Asadi S, Mohammadi-Ivatloo B, Abdar M, Jebelli H, Sadat-Mohammadi M (eds) Application of machine learning and deep learning methods to power system problems. Springer International Publishing, Cham, pp 177\u2013210"},{"key":"302_CR26","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1016\/j.egypro.2018.06.023","volume":"144","author":"SR Nandanwar","year":"2018","unstructured":"Nandanwar SR, Kolhe ML, Warkad SB, Patidar NP, Singh VK (2018) Voltage security assessment by using PFDT and CBR methods in emerging power system. Energy Procedia 144:170\u2013181. https:\/\/doi.org\/10.1016\/j.egypro.2018.06.023","journal-title":"Energy Procedia"},{"key":"302_CR27","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-77696-1","volume-title":"Application of machine learning and deep learning methods to power system problems","author":"M Nazari-Heris","year":"2021","unstructured":"Nazari-Heris M, Asadi S, Mohammadi-Ivatloo B, Abdar M, Jebelli H, Sadat-Mohammadi M (2021) Application of machine learning and deep learning methods to power system problems. Springer International Publishing, Cham"},{"key":"302_CR28","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1016\/j.epsr.2016.12.026","volume":"145","author":"SM P\u00e9rez-Londo\u00f1o","year":"2017","unstructured":"P\u00e9rez-Londo\u00f1o SM, Olivar-Tost G, Mora-Florez JJ (2017) Online determination of voltage stability weak areas for situational awareness improvement. Electric Power Syst Res 145:112\u2013121. https:\/\/doi.org\/10.1016\/j.epsr.2016.12.026","journal-title":"Electric Power Syst Res"},{"key":"302_CR29","doi-asserted-by":"publisher","unstructured":"Phadke AG (2002) Synchronized phasor measurements-a historical overview. IEEE\/PES Transmission and Distribution Conference and Exhibition, vol. 1, IEEE; n.d., p. 476\u20139. https:\/\/doi.org\/10.1109\/TDC.2002.1178427","DOI":"10.1109\/TDC.2002.1178427"},{"key":"302_CR30","doi-asserted-by":"publisher","first-page":"402","DOI":"10.1016\/j.ijepes.2019.01.022","volume":"108","author":"JD Pinz\u00f3n","year":"2019","unstructured":"Pinz\u00f3n JD, Colom\u00e9 DG (2019) Real-time multi-state classification of short-term voltage stability based on multivariate time series machine learning. Int J Electr Power Energy Syst 108:402\u2013414. https:\/\/doi.org\/10.1016\/j.ijepes.2019.01.022","journal-title":"Int J Electr Power Energy Syst"},{"key":"302_CR31","doi-asserted-by":"publisher","DOI":"10.3389\/fnano.2022.972421","author":"S Prusty","year":"2022","unstructured":"Prusty S, Patnaik S, Dash SK (2022) SKCV: stratified K-fold cross-validation on ML classifiers for predicting cervical cancer. Front Nanotechnol. https:\/\/doi.org\/10.3389\/fnano.2022.972421","journal-title":"Front Nanotechnol"},{"key":"302_CR32","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1541\/ieejpes.137.59","volume":"137","author":"LM Putranto","year":"2017","unstructured":"Putranto LM, Hara R, Kita H, Tanaka E (2017) Multistage preventive scheme for improving voltage stability and security in an integrated renewable energy system. IEEJ Trans Power Energy 137:59\u201370. https:\/\/doi.org\/10.1541\/ieejpes.137.59","journal-title":"IEEJ Trans Power Energy"},{"key":"302_CR33","doi-asserted-by":"publisher","first-page":"128345","DOI":"10.1109\/ACCESS.2021.3107248","volume":"9","author":"SMH Rizvi","year":"2021","unstructured":"Rizvi SMH, Sadanandan SK, Srivastava AK (2021) Data-driven short-term voltage stability assessment using convolutional neural networks considering data anomalies and localization. IEEE Access 9:128345\u2013128358. https:\/\/doi.org\/10.1109\/ACCESS.2021.3107248","journal-title":"IEEE Access"},{"key":"302_CR34","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1038\/323533a0","volume":"323","author":"DE Rumelhart","year":"1986","unstructured":"Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by back-propagating errors. Nature 323:533\u2013536. https:\/\/doi.org\/10.1038\/323533a0","journal-title":"Nature"},{"key":"302_CR35","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1007\/BF00993309","volume":"16","author":"SL Salzberg","year":"1994","unstructured":"Salzberg SL (1994) C4.5: programs for Machine Learning by J. Ross Quinlan. Morgan Kaufmann Publishers, Inc., 1993. Mach Learn 16:235\u2013240. https:\/\/doi.org\/10.1007\/BF00993309","journal-title":"Mach Learn"},{"key":"302_CR36","doi-asserted-by":"publisher","DOI":"10.1142\/S0218001420520138","author":"D Soydaner","year":"2020","unstructured":"Soydaner D (2020) A comparison of optimization algorithms for deep learning. Intern J Pattern Recognit Artif Intell. https:\/\/doi.org\/10.1142\/S0218001420520138","journal-title":"Intern J Pattern Recognit Artif Intell"},{"key":"302_CR37","doi-asserted-by":"publisher","first-page":"6696","DOI":"10.1109\/TPWRS.2018.2849717","volume":"33","author":"H-Y Su","year":"2018","unstructured":"Su H-Y, Liu T-Y (2018) Enhanced-online-random-forest model for static voltage stability assessment using wide area measurements. IEEE Trans Power Syst 33:6696\u20136704. https:\/\/doi.org\/10.1109\/TPWRS.2018.2849717","journal-title":"IEEE Trans Power Syst"},{"key":"302_CR38","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1109\/JPROC.2010.2060450","volume":"99","author":"V Terzija","year":"2011","unstructured":"Terzija V, Valverde G, Cai D, Regulski P, Madani V, Fitch J et al (2011) Wide-area monitoring, protection, and control of future electric power networks. Proc IEEE 99:80\u201393. https:\/\/doi.org\/10.1109\/JPROC.2010.2060450","journal-title":"Proc IEEE"},{"key":"302_CR39","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s40565-018-0455-8","volume":"7","author":"MU Usman","year":"2019","unstructured":"Usman MU, Faruque MO (2019) Applications of synchrophasor technologies in power systems. J Modern Power Syst Clean Energy 7:211\u2013226. https:\/\/doi.org\/10.1007\/s40565-018-0455-8","journal-title":"J Modern Power Syst Clean Energy"},{"key":"302_CR40","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4757-3264-1","volume-title":"The nature of statistical learning theory","author":"VN Vapnik","year":"2000","unstructured":"Vapnik VN (2000) The nature of statistical learning theory. Springer, New York, New York. https:\/\/doi.org\/10.1007\/978-1-4757-3264-1"},{"key":"302_CR41","volume-title":"CRC standard curves and surfaces with Mathematica","author":"SDH Von","year":"2007","unstructured":"Von SDH (2007) CRC standard curves and surfaces with Mathematica, 2nd edn. Chapman and Hall\/CRC, Boca Raton","edition":"2"},{"key":"302_CR42","unstructured":"Williams C, Rasmussen C (1995) Gaussian processes for regression. In: Touretzky D, Mozer MC, Hasselmo M, eds. Adv Neural Inf Process Syst, vol. 8, MIT Press"},{"key":"302_CR43","doi-asserted-by":"publisher","unstructured":"Yari S, Khoshkhoo H (2017) Assessment of line stability indices in detection of voltage stability status. 2017 IEEE International Conference on Environment and Electrical Engineering and 2017 IEEE Industrial and Commercial Power Systems Europe (EEEIC \/ I&CPS Europe), IEEE; p. 1\u20135. https:\/\/doi.org\/10.1109\/EEEIC.2017.7977454","DOI":"10.1109\/EEEIC.2017.7977454"},{"key":"302_CR44","doi-asserted-by":"publisher","first-page":"924","DOI":"10.1049\/iet-gtd.2013.0724","volume":"8","author":"T Zabaiou","year":"2014","unstructured":"Zabaiou T, Dessaint L, Kamwa I (2014) Preventive control approach for voltage stability improvement using voltage stability constrained optimal power flow based on static line voltage stability indices. IET Gener Transm Distrib 8:924\u2013934. https:\/\/doi.org\/10.1049\/iet-gtd.2013.0724","journal-title":"IET Gener Transm Distrib"},{"key":"302_CR45","doi-asserted-by":"publisher","unstructured":"Zhang R, Xu Y, Yang Dong Z, Zhang P, Po Wong K (2013) Voltage stability margin prediction by ensemble based extreme learning machine. 2013 IEEE Power & Energy Society General Meeting, IEEE; p. 1\u20135. https:\/\/doi.org\/10.1109\/PESMG.2013.6672489","DOI":"10.1109\/PESMG.2013.6672489"},{"key":"302_CR46","doi-asserted-by":"publisher","first-page":"1566","DOI":"10.1109\/TPWRS.2009.2038059","volume":"25","author":"DQ Zhou","year":"2010","unstructured":"Zhou DQ, Annakkage UD, Rajapakse AD (2010) Online monitoring of voltage stability margin using an artificial neural network. IEEE Trans Power Syst 25:1566\u20131574. https:\/\/doi.org\/10.1109\/TPWRS.2009.2038059","journal-title":"IEEE Trans Power Syst"}],"container-title":["Energy Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s42162-024-00302-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s42162-024-00302-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s42162-024-00302-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,8]],"date-time":"2024-01-08T09:02:55Z","timestamp":1704704575000},"score":1,"resource":{"primary":{"URL":"https:\/\/energyinformatics.springeropen.com\/articles\/10.1186\/s42162-024-00302-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,8]]},"references-count":46,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["302"],"URL":"https:\/\/doi.org\/10.1186\/s42162-024-00302-w","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-3539827\/v1","asserted-by":"object"}]},"ISSN":["2520-8942"],"issn-type":[{"value":"2520-8942","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,8]]},"assertion":[{"value":"2 November 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 January 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 January 2024","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 they have no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"1"}}