{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T10:14:39Z","timestamp":1776939279353,"version":"3.51.4"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2022,1,21]],"date-time":"2022-01-21T00:00:00Z","timestamp":1642723200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,21]],"date-time":"2022-01-21T00:00:00Z","timestamp":1642723200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100006464","name":"Birla Institute of Technology and Science, Pilani","doi-asserted-by":"publisher","award":["(BITS\/GAU\/ACRG\/2019\/H0595)"],"award-info":[{"award-number":["(BITS\/GAU\/ACRG\/2019\/H0595)"]}],"id":[{"id":"10.13039\/501100006464","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2022,6]]},"DOI":"10.1007\/s00521-021-06741-w","type":"journal-article","created":{"date-parts":[[2022,1,21]],"date-time":"2022-01-21T00:04:01Z","timestamp":1642723441000},"page":"8597-8615","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Development of an AI-based FSA for real-time condition monitoring for industrial machine"],"prefix":"10.1007","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4398-0454","authenticated-orcid":false,"given":"Amar Kumar","family":"Verma","sequence":"first","affiliation":[]},{"given":"Pallav Devang","family":"Raval","sequence":"additional","affiliation":[]},{"given":"Neha","family":"Rajagopalan","sequence":"additional","affiliation":[]},{"given":"Vaishnavi","family":"Khariya","sequence":"additional","affiliation":[]},{"given":"Radhika","family":"Sudha","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,1,21]]},"reference":[{"issue":"4","key":"6741_CR1","doi-asserted-by":"publisher","first-page":"1221","DOI":"10.1007\/s11831-018-9286-z","volume":"26","author":"A Choudhary","year":"2019","unstructured":"Choudhary A, Goyal D, Shimi SL, Akula A (2019) Condition monitoring and fault diagnosis of induction motors: a review. Arch Comput Meth Eng 26(4):1221\u20131238","journal-title":"Arch Comput Meth Eng"},{"key":"6741_CR2","doi-asserted-by":"crossref","unstructured":"Wu Y, Jiang B, Wang Y (2019) Incipient winding fault detection and diagnosis for squirrel-cage induction motors equipped on crh trains. ISA Trans","DOI":"10.1016\/j.isatra.2019.09.020"},{"issue":"2","key":"6741_CR3","doi-asserted-by":"publisher","first-page":"1072","DOI":"10.1109\/JSEN.2020.2990727","volume":"21","author":"AK Verma","year":"2020","unstructured":"Verma AK, Akkulu P, Padmanabhan SV, Radhika S (2020) Automatic condition monitoring of industrial machines using fsa-based hall-effect transducer. IEEE Sens J 21(2):1072\u20131081","journal-title":"IEEE Sens J"},{"key":"6741_CR4","doi-asserted-by":"crossref","unstructured":"Verma AK, Radhika S, Padmanabhan S (2018) Wavelet based fault detection and diagnosis using online mcsa of stator winding faults due to insulation failure in industrial induction machine. In: 2018 IEEE Recent Advances in Intelligent Computational Systems (RAICS), IEEE, pp 204\u2013208","DOI":"10.1109\/RAICS.2018.8635058"},{"key":"6741_CR5","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1007\/978-981-15-1084-7_10","volume-title":"Intelligent computing and communication","author":"AK Verma","year":"2020","unstructured":"Verma AK, Spandana P, Padmanabhan SV, Radhika S (2020) Quantitative modeling and simulation for stator inter-turn fault detection in industrial machine. In: Bhateja V, Satapathy SC, Zhang YD, Aradhya VNM (eds) Intelligent computing and communication. Springer, Singapore, pp 87\u201397"},{"key":"6741_CR6","doi-asserted-by":"publisher","unstructured":"Ranjan GSK, Kumar Verma A, Radhika S (2019) K-nearest neighbors and grid search cv based real time fault monitoring system for industries. In: 2019 IEEE 5th International Conference for Convergence in Technology (I2CT), pp 1\u20135, https:\/\/doi.org\/10.1109\/I2CT45611.2019.9033691","DOI":"10.1109\/I2CT45611.2019.9033691"},{"key":"6741_CR7","doi-asserted-by":"publisher","first-page":"537","DOI":"10.1007\/978-981-10-7868-2_52","volume-title":"Artificial intelligence and evolutionary computations in engineering systems","author":"G Rajamany","year":"2018","unstructured":"Rajamany G, Srinivasan S (2018) Neural network approach for inter-turn short-circuit detection in induction motor stator winding. In: Dash SS, Naidu PCB, Bayindir R, Das S (eds) Artificial intelligence and evolutionary computations in engineering systems. Springer, Singapore, pp 537\u2013550"},{"key":"6741_CR8","doi-asserted-by":"crossref","unstructured":"Vamsi IV, Abhinav N, Verma AK, Radhika S (2018) Random forest based real time fault monitoring system for industries. In: 2018 4th International Conference on Computing Communication and Automation (ICCCA), IEEE, pp 1\u20136","DOI":"10.1109\/CCAA.2018.8777673"},{"issue":"1","key":"6741_CR9","doi-asserted-by":"publisher","first-page":"90","DOI":"10.3390\/electronics8010090","volume":"8","author":"A Mejia-Barron","year":"2019","unstructured":"Mejia-Barron A, de Santiago-Perez JJ, Granados-Lieberman D, Amezquita-Sanchez JP, Valtierra-Rodriguez M (2019) Shannon entropy index and a fuzzy logic system for the assessment of stator winding short-circuit faults in induction motors. Electronics 8(1):90","journal-title":"Electronics"},{"issue":"4","key":"6741_CR10","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1007\/s40313-018-0388-5","volume":"29","author":"CG Dias","year":"2018","unstructured":"Dias CG, de Sousa CM (2018) A neuro-fuzzy approach for locating broken rotor bars in induction motors at very low slip. J Cont Automat Elect Syst 29(4):489\u2013499. https:\/\/doi.org\/10.1007\/s40313-018-0388-5","journal-title":"J Cont Automat Elect Syst"},{"key":"6741_CR11","doi-asserted-by":"crossref","unstructured":"Sharma A, Jigyasu R, Mathew L, Chatterji S (2018) Bearing fault diagnosis using weighted k-nearest neighbor. In: 2018 2nd International Conference on Trends in Electronics and Informatics (ICOEI), IEEE, pp 1132\u20131137","DOI":"10.1109\/ICOEI.2018.8553800"},{"key":"6741_CR12","doi-asserted-by":"crossref","unstructured":"Vilhekar TG, Ballal MS, Umre BS (2016) Application of sweep frequency response analysis for the detection of winding faults in induction motor. In: IECON 2016-42nd Annual Conference of the IEEE Industrial Electronics Society, IEEE, pp 1458\u20131463","DOI":"10.1109\/IECON.2016.7793565"},{"issue":"11","key":"6741_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TMAG.2017.2710181","volume":"53","author":"DG Dorrell","year":"2017","unstructured":"Dorrell DG, Makhoba K (2017) Detection of inter-turn stator faults in induction motors using short-term averaging of forward and backward rotating stator current phasors for fast prognostics. IEEE Trans Magnet 53(11):1\u20137","journal-title":"IEEE Trans Magnet"},{"issue":"8","key":"6741_CR14","doi-asserted-by":"publisher","first-page":"540","DOI":"10.1049\/iet-epa.2015.0024","volume":"9","author":"NR Devi","year":"2015","unstructured":"Devi NR, Sarma DVS, Rao PVR (2015) Detection of stator incipient faults and identification of faulty phase in three-phase induction motor-simulation and experimental verification. IET Elect Power Appl 9(8):540\u2013548","journal-title":"IET Elect Power Appl"},{"key":"6741_CR15","doi-asserted-by":"crossref","unstructured":"Kumar\u00a0Verma A, Radhika S, Surampudi N (2020) Web based application for quick and handy health condition monitoring system for a reliable wind power generation. In: ASME International Mechanical Engineering Congress and Exposition, American Society of Mechanical Engineers, vol 84669, p V014T14A009","DOI":"10.1115\/IMECE2020-23713"},{"issue":"1","key":"6741_CR16","doi-asserted-by":"publisher","first-page":"450","DOI":"10.1016\/j.eswa.2009.05.046","volume":"37","author":"S Radhika","year":"2010","unstructured":"Radhika S, Sabareesh G, Jagadanand G, Sugumaran V (2010) Precise wavelet for current signature in 3$$\\phi $$ im. Exp Syst Appl 37(1):450\u2013455","journal-title":"Exp Syst Appl"},{"key":"6741_CR17","doi-asserted-by":"crossref","unstructured":"Verma AK, Radhika S (2021) Multi-level stator winding failure analysis on the insulation material for industrial induction motor. Experimental techniques pp 1\u201315","DOI":"10.1007\/s40799-021-00490-0"},{"issue":"3","key":"6741_CR18","doi-asserted-by":"publisher","first-page":"339","DOI":"10.1049\/iet-epa.2017.0457","volume":"12","author":"S Haroun","year":"2017","unstructured":"Haroun S, Seghir AN, Touati S (2017) Multiple features extraction and selection for detection and classification of stator winding faults. IET Elect Power Appl 12(3):339\u2013346","journal-title":"IET Elect Power Appl"},{"key":"6741_CR19","doi-asserted-by":"crossref","unstructured":"Padmakumar S, Roy K, Agarwal V (2008) Induction machines: a novel, model based non-invasive fault detection and diagnosis technique. In: 2008 Joint International Conference on Power System Technology and IEEE Power India Conference, IEEE, pp 1\u20135","DOI":"10.1109\/ICPST.2008.4745282"},{"key":"6741_CR20","unstructured":"Chandra SR, Ayyappan G, Srinivas K, Ganesh D (2016) Simulation and testing of induction motor faults in matlab for online condition monitoring. IUP J Elect Elect Eng 9(2)"},{"key":"6741_CR21","doi-asserted-by":"publisher","first-page":"554","DOI":"10.1016\/j.ymssp.2019.01.030","volume":"123","author":"Y Xie","year":"2019","unstructured":"Xie Y, Chen P, Li F, Liu H (2019) Electromagnetic forces signature and vibration characteristic for diagnosis broken bars in squirrel cage induction motors. Mech Syst Sig Proc 123:554\u2013572","journal-title":"Mech Syst Sig Proc"},{"key":"6741_CR22","unstructured":"Mortazavizadeh VAZA S\u00a0A (2012) Detection of stator winding inter-turn short circuit in induction motor using vibration specified harmonic amplitude. In: In 2nd International Conf. on Acoustics & Vibration, ISAV, pp 1\u20138"},{"key":"6741_CR23","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1016\/j.infrared.2016.06.010","volume":"77","author":"G Singh","year":"2016","unstructured":"Singh G, Kumar TCA, Naikan V (2016) Induction motor inter turn fault detection using infrared thermographic analysis. Inf Phys Technol 77:277\u2013282","journal-title":"Inf Phys Technol"},{"key":"6741_CR24","doi-asserted-by":"crossref","unstructured":"Haroun S, Seghir AN, Touati S, Hamdani S (2015) Misalignment fault detection and diagnosis using ar model of torque signal. In: 2015 IEEE 10th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED), IEEE, pp 322\u2013326","DOI":"10.1109\/DEMPED.2015.7303709"},{"issue":"1","key":"6741_CR25","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/j.epsr.2004.08.015","volume":"75","author":"M Arkan","year":"2005","unstructured":"Arkan M, Kostic-Perovic D, Unsworth P (2005) Modelling and simulation of induction motors with inter-turn faults for diagnostics. Elect Pow Syst Res 75(1):57\u201366","journal-title":"Elect Pow Syst Res"},{"issue":"4","key":"6741_CR26","doi-asserted-by":"publisher","first-page":"1036","DOI":"10.1016\/j.epsr.2010.12.003","volume":"81","author":"A Ukil","year":"2011","unstructured":"Ukil A, Chen S, Andenna A (2011) Detection of stator short circuit faults in three-phase induction motors using motor current zero crossing instants. Elect Power Syst Res 81(4):1036\u20131044","journal-title":"Elect Power Syst Res"},{"key":"6741_CR27","doi-asserted-by":"crossref","unstructured":"Ranga C, Chandel AK (2015) Advanced tool based condition monitoring of induction machines by using labview\u2014a review. In: 2015 IEEE UP Section Conference on electrical computer and electronics (UPCON), IEEE, pp 1\u20136","DOI":"10.1109\/UPCON.2015.7456693"},{"key":"6741_CR28","doi-asserted-by":"publisher","first-page":"1297","DOI":"10.1007\/s00521-020-05033-z","volume":"33","author":"AK Verma","year":"2020","unstructured":"Verma AK, Nagpal S, Desai A, Sudha R (2020) An efficient neural-network model for real-time fault detection in industrial machine. Neural Comput Appl 33:1297\u20131310","journal-title":"Neural Comput Appl"},{"issue":"1","key":"6741_CR29","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1007\/s00202-016-0487-x","volume":"100","author":"JD Mart\u00ednez-Morales","year":"2018","unstructured":"Mart\u00ednez-Morales JD, Palacios-Hern\u00e1ndez ER, Campos-Delgado D (2018) Multiple-fault diagnosis in induction motors through support vector machine classification at variable operating conditions. Elect Eng 100(1):59\u201373","journal-title":"Elect Eng"},{"key":"6741_CR30","doi-asserted-by":"publisher","first-page":"1323","DOI":"10.1016\/j.egypro.2015.07.779","volume":"74","author":"A Bechkaoui","year":"2015","unstructured":"Bechkaoui A, Ameur A, Bouras S, Hadjadj A (2015) Open-circuit and inter-turn short-circuit detection in pmsg for wind turbine applications using fuzzy logic. Energy Procedia 74:1323\u20131336","journal-title":"Energy Procedia"},{"key":"6741_CR31","doi-asserted-by":"crossref","unstructured":"Verma AK, Jain A, Radhika S (2020) Neuro-fuzzy classifier for identification of stator winding inter-turn fault for industrial machine. In: International conference on modelling, simulation and intelligent computing, Springer, pp 101\u2013110","DOI":"10.1007\/978-981-15-4775-1_12"},{"issue":"1","key":"6741_CR32","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1109\/TIE.2006.888786","volume":"54","author":"H Su","year":"2007","unstructured":"Su H, Chong KT (2007) Induction machine condition monitoring using neural network modeling. IEEE Trans Indus Elect 54(1):241\u2013249","journal-title":"IEEE Trans Indus Elect"},{"key":"6741_CR33","doi-asserted-by":"crossref","unstructured":"Verma AK, Vamsi I, Saurabh P, Sudha R, Sabareesh G, Rajkumar S (2021) Wavelet and deep learning-based detection of sars-ncov from thoracic x-ray images for rapid and efficient testing. Exp Syst Appl. p 115650","DOI":"10.1016\/j.eswa.2021.115650"},{"issue":"3","key":"6741_CR34","doi-asserted-by":"publisher","first-page":"653","DOI":"10.3390\/en11030653","volume":"11","author":"L Maraaba","year":"2018","unstructured":"Maraaba L, Al-Hamouz Z, Abido M (2018) An efficient stator inter-turn fault diagnosis tool for induction motors. Energies 11(3):653","journal-title":"Energies"},{"issue":"1","key":"6741_CR35","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1109\/TIE.2003.822083","volume":"51","author":"M Eltabach","year":"2004","unstructured":"Eltabach M, Charara A, Zein I (2004) A comparison of external and internal methods of signal spectral analysis for broken rotor bars detection in induction motors. IEEE Trans Indus Elect 51(1):107\u2013121","journal-title":"IEEE Trans Indus Elect"},{"key":"6741_CR36","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1016\/j.apacoust.2016.10.012","volume":"117","author":"A Glowacz","year":"2017","unstructured":"Glowacz A, Glowacz Z (2017) Diagnosis of stator faults of the single-phase induction motor using acoustic signals. Appl Acoust 117:20\u201327","journal-title":"Appl Acoust"},{"key":"6741_CR37","doi-asserted-by":"crossref","unstructured":"Verma AK, Vinod JV, Sudha R (2021) A modular zigbee-based iot platform for reliable health monitoring of industrial machines using refsa. In: Microelectronics and Signal Processing, CRC Press, pp 179\u2013188","DOI":"10.1201\/9781003168225-10"},{"issue":"4","key":"6741_CR38","doi-asserted-by":"publisher","first-page":"585","DOI":"10.1007\/s11831-015-9145-0","volume":"23","author":"D Goyal","year":"2016","unstructured":"Goyal D, Pabla B (2016) The vibration monitoring methods and signal processing techniques for structural health monitoring: a review. Arch Comput Meth Eng 23(4):585\u2013594","journal-title":"Arch Comput Meth Eng"},{"key":"6741_CR39","doi-asserted-by":"publisher","unstructured":"Pires V, Foito D, Martins J, Pires AJ (2015) Detection of stator winding fault in induction motors using a motor square current signature analysis (mscsa) 2015:507\u2013512. https:\/\/doi.org\/10.1109\/PowerEng.2015.7266369","DOI":"10.1109\/PowerEng.2015.7266369"},{"key":"6741_CR40","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1016\/j.ymssp.2018.02.016","volume":"108","author":"R Liu","year":"2018","unstructured":"Liu R, Yang B, Zio E, Chen X (2018) Artificial intelligence for fault diagnosis of rotating machinery: a review. Mech Syst Sig Proc 108:33\u201347","journal-title":"Mech Syst Sig Proc"},{"key":"6741_CR41","unstructured":"Samarasinghe S (2016) Neural networks for applied sciences and engineering: from fundamentals to complex pattern recognition. Auerbach publications"},{"key":"6741_CR42","unstructured":"Kumar P, Hati AS (2020) Review on machine learning algorithm based fault detection in induction motors. Arch Comput Meth Eng. pp 1\u201312"},{"issue":"10","key":"6741_CR43","doi-asserted-by":"publisher","first-page":"4891","DOI":"10.1016\/j.eswa.2014.02.028","volume":"41","author":"M Seera","year":"2014","unstructured":"Seera M, Lim CP, Nahavandi S, Loo CK (2014) Condition monitoring of induction motors: a review and an application of an ensemble of hybrid intelligent models. Exp Syst Appl 41(10):4891\u20134903","journal-title":"Exp Syst Appl"},{"key":"6741_CR44","doi-asserted-by":"crossref","unstructured":"Yadav S, Shukla S (2016) Analysis of k-fold cross-validation over hold-out validation on colossal datasets for quality classification. In: 2016 IEEE 6th International conference on advanced computing (IACC), IEEE, pp 78\u201383","DOI":"10.1109\/IACC.2016.25"},{"issue":"1","key":"6741_CR45","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1007\/s00521-012-1310-x","volume":"23","author":"M Seera","year":"2013","unstructured":"Seera M, Lim CP, Ishak D, Singh H (2013) Application of the fuzzy min-max neural network to fault detection and diagnosis of induction motors. Neural Comput Appl 23(1):191\u2013200","journal-title":"Neural Comput Appl"},{"key":"6741_CR46","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1016\/j.epsr.2015.06.008","volume":"127","author":"RHC Pal\u00e1cios","year":"2015","unstructured":"Pal\u00e1cios RHC, da Silva IN, Goedtel A, Godoy WF (2015) A comprehensive evaluation of intelligent classifiers for fault identification in three-phase induction motors. Elect Power Syst Res 127:249\u2013258","journal-title":"Elect Power Syst Res"},{"issue":"2","key":"6741_CR47","doi-asserted-by":"publisher","first-page":"447","DOI":"10.1007\/s00521-018-3911-5","volume":"32","author":"AAAM Amiruddin","year":"2018","unstructured":"Amiruddin AAAM, Zabiri H, Taqvi SAA, Tufa LD (2018) Neural network applications in fault diagnosis and detection: an overview of implementations in engineering-related systems. Neural Comput Appl 32(2):447\u2013472","journal-title":"Neural Comput Appl"},{"issue":"3","key":"6741_CR48","doi-asserted-by":"publisher","first-page":"1250","DOI":"10.1016\/j.measurement.2012.11.011","volume":"46","author":"M Amarnath","year":"2013","unstructured":"Amarnath M, Sugumaran V, Kumar H (2013) Exploiting sound signals for fault diagnosis of bearings using decision tree. Measurement 46(3):1250\u20131256","journal-title":"Measurement"},{"issue":"5","key":"6741_CR49","doi-asserted-by":"publisher","first-page":"1272","DOI":"10.1109\/TIA.2003.816531","volume":"39","author":"H Henao","year":"2003","unstructured":"Henao H, Demian C, Capolino GA (2003) A frequency-domain detection of stator winding faults in induction machines using an external flux sensor. IEEE Trans Indus Appl 39(5):1272\u20131279","journal-title":"IEEE Trans Indus Appl"},{"issue":"2","key":"6741_CR50","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1007\/s00521-010-0512-3","volume":"20","author":"H Su","year":"2011","unstructured":"Su H, Chong KT, Kumar RR (2011) Vibration signal analysis for electrical fault detection of induction machine using neural networks. Neural Comput Appl 20(2):183\u2013194","journal-title":"Neural Comput Appl"},{"key":"6741_CR51","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1016\/j.measurement.2018.04.039","volume":"124","author":"JR Rivera-Guillen","year":"2018","unstructured":"Rivera-Guillen JR, De Santiago-Perez J, Amezquita-Sanchez JP, Valtierra-Rodriguez M, Romero-Troncoso RJ (2018) Enhanced fft-based method for incipient broken rotor bar detection in induction motors during the startup transient. Measurement 124:277\u2013285","journal-title":"Measurement"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06741-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-021-06741-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06741-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,14]],"date-time":"2022-05-14T05:37:09Z","timestamp":1652506629000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-021-06741-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,21]]},"references-count":51,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2022,6]]}},"alternative-id":["6741"],"URL":"https:\/\/doi.org\/10.1007\/s00521-021-06741-w","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,21]]},"assertion":[{"value":"5 October 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 November 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 January 2022","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 author(s) declared no potential conflicts of interest with respect to the research, authorship, and\/or publication of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}