{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T17:49:08Z","timestamp":1778953748281,"version":"3.51.4"},"reference-count":62,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2020,7,7]],"date-time":"2020-07-07T00:00:00Z","timestamp":1594080000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,7,7]],"date-time":"2020-07-07T00:00:00Z","timestamp":1594080000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2021,4]]},"DOI":"10.1007\/s00521-020-05163-4","type":"journal-article","created":{"date-parts":[[2020,7,7]],"date-time":"2020-07-07T13:03:57Z","timestamp":1594127037000},"page":"2667-2683","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Time series forecasting with feedforward neural networks trained using particle swarm optimizers for dynamic environments"],"prefix":"10.1007","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2941-8916","authenticated-orcid":false,"given":"Salihu A.","family":"Abdulkarim","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andries P.","family":"Engelbrecht","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,7,7]]},"reference":[{"issue":"1","key":"5163_CR1","doi-asserted-by":"publisher","first-page":"6","DOI":"10.4314\/bajopas.v9i1.2","volume":"9","author":"S Abdulkarim","year":"2016","unstructured":"Abdulkarim S, Garko A (2016) Effectiveness of firefly algorithm based neural network in time series forecasting. Bayero J Pure Appl Sci 9(1):6\u201310","journal-title":"Bayero J Pure Appl Sci"},{"key":"5163_CR2","unstructured":"Abdulkarim SA (2018) Time series forecasting using dynamic particle swarm optimizer trained neural networks. Phd thesis, University of Pretoria"},{"key":"5163_CR3","doi-asserted-by":"publisher","first-page":"2763","DOI":"10.1007\/s11063-019-10061-5","volume":"50","author":"SA Abdulkarim","year":"2019","unstructured":"Abdulkarim SA, Engelbrecht AP (2019) Time series forecasting using neural networks: Are recurrent connections necessary? Neural Process Lett 50:2763\u20132795","journal-title":"Neural Process Lett"},{"key":"5163_CR4","unstructured":"Adhikari R, Agrawal RK (2011) Effectiveness of PSO based neural network for seasonal time series forecasting. In: Proceedings of the fifth Indian international conference on artificial intelligence, pp 231\u2013244"},{"issue":"6","key":"5163_CR5","doi-asserted-by":"publisher","first-page":"1141","DOI":"10.1007\/s00521-012-0880-y","volume":"22","author":"MH Ahmadi","year":"2013","unstructured":"Ahmadi MH, Aghaj SSG, Nazeri A (2013) Prediction of power in solar stirling heat engine by using neural network based on hybrid genetic algorithm and particle swarm optimization. Neural Comput Appl 22(6):1141\u20131150","journal-title":"Neural Comput Appl"},{"issue":"4","key":"5163_CR6","doi-asserted-by":"publisher","first-page":"459","DOI":"10.1109\/TEVC.2005.857074","volume":"10","author":"T Blackwell","year":"2006","unstructured":"Blackwell T, Branke J (2006) Multiswarms, exclusion, and anti-convergence in dynamic environments. IEEE Trans Evol Comput 10(4):459\u2013472","journal-title":"IEEE Trans Evol Comput"},{"key":"5163_CR7","doi-asserted-by":"crossref","unstructured":"Blackwell T, Branke J, Li X (2008) Particle swarms for dynamic optimization problems. Swarm intelligence. Springer, Berlin pp 193\u2013217","DOI":"10.1007\/978-3-540-74089-6_6"},{"key":"5163_CR8","unstructured":"Blackwell TM, Bentley PJ (2002) Dynamic search with charged swarms. In: Proceedings of the genetic and evolutionary computation conference, pp 9\u201316"},{"key":"5163_CR9","volume-title":"Time series analysis: forecasting and control","author":"GEP Box","year":"2015","unstructured":"Box GEP, Jenkins GM, Reinsel GC, Ljung GM (2015) Time series analysis: forecasting and control. Wiley, New York"},{"key":"5163_CR10","doi-asserted-by":"crossref","unstructured":"Brezak D, Bacek T, Majetic D, Kasac J, Novakovic B (2012) A comparison of feed-forward and recurrent neural networks in time series forecasting. In: Proceedings of IEEE conference on computational intelligence for financial engineering and economics, pp 1\u20136","DOI":"10.1109\/CIFEr.2012.6327793"},{"key":"5163_CR11","first-page":"429","volume":"1","author":"A Carlisle","year":"2000","unstructured":"Carlisle A, Dozier G (2000) Adapting particle swarm optimization to dynamic environments. Int Conf Artif Intell 1:429\u2013434","journal-title":"Int Conf Artif Intell"},{"key":"5163_CR12","doi-asserted-by":"crossref","unstructured":"Carlisle A, Dozler G (2002) Tracking changing extrema with adaptive particle swarm optimizer. In: Automation congress, 2002 proceedings of the 5th biannual world, vol\u00a013. IEEE, pp 265\u2013270","DOI":"10.1109\/WAC.2002.1049555"},{"key":"5163_CR13","volume-title":"The analysis of time series: an introduction","author":"C Chatfield","year":"2016","unstructured":"Chatfield C (2016) The analysis of time series: an introduction. CRC Press, Boca Raton"},{"key":"5163_CR14","doi-asserted-by":"publisher","first-page":"902","DOI":"10.1016\/j.rser.2017.02.085","volume":"74","author":"C Deb","year":"2017","unstructured":"Deb C, Zhang F, Yang J, Lee SE, Shah KW (2017) A review on time series forecasting techniques for building energy consumption. Renew Sustain Energy Rev 74:902\u2013924","journal-title":"Renew Sustain Energy Rev"},{"issue":"Jan","key":"5163_CR15","first-page":"1","volume":"7","author":"J Dem\u0161ar","year":"2006","unstructured":"Dem\u0161ar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7(Jan):1\u201330","journal-title":"J Mach Learn Res"},{"key":"5163_CR16","unstructured":"Duhain JGOL (2011) Particle swarm optimization in dynamically changing environment an empirical study. MSc Thesis, University of Pretoria"},{"key":"5163_CR17","unstructured":"Eberhart RC, Kennedy J (1995) Particles swarm optimization. In: Proceedings of IEEE international conference on neural networks, Perth, Australia, pp 1942\u20131948"},{"key":"5163_CR18","doi-asserted-by":"publisher","DOI":"10.1002\/9780470512517","volume-title":"Computational intelligence: an introduction","author":"AP Engelbrecht","year":"2007","unstructured":"Engelbrecht AP (2007) Computational intelligence: an introduction. Wiley, New York"},{"issue":"1\u20133","key":"5163_CR19","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1023\/A:1012074215150","volume":"31","author":"RJ Frank","year":"2001","unstructured":"Frank RJ, Davey N, Hunt SP (2001) Time series prediction and neural networks. J Intell Robot Syst 31(1\u20133):91\u2013103","journal-title":"J Intell Robot Syst"},{"issue":"2","key":"5163_CR20","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1023\/A:1011324221407","volume":"13","author":"IM Galvan","year":"2001","unstructured":"Galvan IM, Isasi P (2001) Multi-step learning rule for recurrent neural models: an application to time series forecasting. Neural Process Lett 13(2):115\u2013133","journal-title":"Neural Process Lett"},{"issue":"1","key":"5163_CR21","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1007\/s00366-015-0400-7","volume":"32","author":"B Gordan","year":"2016","unstructured":"Gordan B, Armaghani DJ, Hajihassani M, Monjezi M (2016) Prediction of seismic slope stability through combination of particle swarm optimization and neural network. Eng Comput 32(1):85\u201397","journal-title":"Eng Comput"},{"key":"5163_CR22","doi-asserted-by":"crossref","unstructured":"Grimaldi EA, Grimaccia F, Mussetta M, Zich RE (2004) PSO as an effective learning algorithm for neural network applications. In: 2004 3rd International Conference on proceedings of the computational electromagnetics and its applications, 2004 (ICCEA 2004). IEEE, pp 557\u2013560","DOI":"10.1109\/ICCEA.2004.1459416"},{"issue":"4","key":"5163_CR23","doi-asserted-by":"publisher","first-page":"2799","DOI":"10.1007\/s12665-015-4274-1","volume":"74","author":"M Hajihassani","year":"2015","unstructured":"Hajihassani M, Armaghani DJ, Monjezi M, Mohamad ET, Marto A (2015) Blast-induced air and ground vibration prediction: a particle swarm optimization-based artificial neural network approach. Environ Earth Sci 74(4):2799\u20132817","journal-title":"Environ Earth Sci"},{"issue":"23","key":"5163_CR24","doi-asserted-by":"publisher","first-page":"4550","DOI":"10.1016\/j.ins.2008.07.024","volume":"178","author":"C Hamzacebi","year":"2008","unstructured":"Hamzacebi C (2008) Improving artificial neural networks performance in seasonal time series forecasting. Inf Sci 178(23):4550\u20134559","journal-title":"Inf Sci"},{"key":"5163_CR25","doi-asserted-by":"crossref","unstructured":"Harrison KR, Ombuki-berman BM, Engelbrecht AP (2016) A radius-free quantum particle swarm optimization technique for dynamic optimization problems. In: Proceedings of IEEE congress on evolutionary computation, pp 578\u2013585","DOI":"10.1109\/CEC.2016.7743845"},{"issue":"5","key":"5163_CR26","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1016\/0893-6080(89)90020-8","volume":"2","author":"K Hornik","year":"1989","unstructured":"Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural Netw 2(5):359\u2013366","journal-title":"Neural Netw"},{"key":"5163_CR27","doi-asserted-by":"crossref","unstructured":"Iqbal S, Zang X, Zhu Y, Liu X (2014) Introducing undergraduate electrical engineering students to chaotic dynamics: computer simulations with logistic map and buck converter. In: IEEE modelling symposium, pp 47\u201352","DOI":"10.1109\/AMS.2014.20"},{"key":"5163_CR28","doi-asserted-by":"crossref","unstructured":"Jha GK, Thulasiraman P, Thulasiram RK (2009) PSO based neural network for time series forecasting. In: Proceedings of IEEE international joint conference on neural networks, pp 1422\u20131427","DOI":"10.1109\/IJCNN.2009.5178707"},{"key":"5163_CR29","doi-asserted-by":"publisher","first-page":"1507","DOI":"10.1007\/s00521-014-1661-6","volume":"25","author":"AR Jordehi","year":"2014","unstructured":"Jordehi AR (2014) Particle swarm optimisation for dynamic optimisation problems: a review. Neural Comput Appl 25:1507\u20131516","journal-title":"Neural Comput Appl"},{"key":"5163_CR30","doi-asserted-by":"crossref","unstructured":"Kedrowski R, Nelson J, Nair AS, Ranganathan P (2018) Short-term seasonal energy forecasting. In: 2018 IEEE international conference on electro\/information technology (EIT). IEEE, pp 696\u2013700","DOI":"10.1109\/EIT.2018.8500215"},{"key":"5163_CR31","first-page":"1671","volume":"2","author":"J Kennedy","year":"2002","unstructured":"Kennedy J, Mendes R (2002) Population structure and particle swarm performance. Proc IEEE Congr Evol Comput 2:1671\u20131676","journal-title":"Proc IEEE Congr Evol Comput"},{"key":"5163_CR32","unstructured":"Laepes A, Farben R (1987) Nonlinear signal processing using neural networks: prediction and system modelling. In: Technical Report, Los Alamos National Laboratory, Los Alamos, NM"},{"key":"5163_CR33","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/j.resourpol.2015.03.004","volume":"45","author":"FS Lasheras","year":"2015","unstructured":"Lasheras FS, de Cos Juez FJ, S\u00e1nchez AS, Krzemie\u0144 A, Fern\u00e1ndez PR (2015) Forecasting the comex copper spot price by means of neural networks and arima models. Resour Policy 45:37\u201343","journal-title":"Resour Policy"},{"key":"5163_CR34","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1007\/978-3-642-35289-8_3","volume-title":"Efficient backprop. Neural networks: tricks of the trade","author":"YA LeCun","year":"2012","unstructured":"LeCun YA, Bottou L, Orr GB, M\u00fcller KR (2012) Efficient backprop. Neural networks: tricks of the trade. Springer, Berlin, pp 9\u201348"},{"issue":"10","key":"5163_CR35","doi-asserted-by":"publisher","first-page":"888","DOI":"10.1016\/j.physleta.2015.01.003","volume":"379","author":"C Li","year":"2015","unstructured":"Li C, Sprott JC, Thio W (2015) Linearization of the Lorenz system. Phys Lett A 379(10):888\u2013893","journal-title":"Phys Lett A"},{"key":"5163_CR36","first-page":"1772","volume":"3","author":"X Li","year":"2003","unstructured":"Li X, Dam KH (2003) Comparing particle swarms for tracking extrema in dynamic environments. IEEE Proc Evol Comput 3:1772\u20131779","journal-title":"IEEE Proc Evol Comput"},{"issue":"1","key":"5163_CR37","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1214\/aoms\/1177730491","volume":"18","author":"HB Mann","year":"1947","unstructured":"Mann HB, Whitney DR (1947) On a test of whether one of two random variables is stochastically larger than the other. Ann Math Stat 18(1):50\u201360","journal-title":"Ann Math Stat"},{"key":"5163_CR38","unstructured":"Milano P, Elettrotecnica D (2004) PSO as an effective learning algorithm for neural network applications. In: Proceedings of the international conference on computational electronics and its applications, pp 557\u2013560"},{"issue":"3","key":"5163_CR39","doi-asserted-by":"publisher","first-page":"582","DOI":"10.1111\/j.1365-2478.2012.01080.x","volume":"61","author":"AA Mohammad","year":"2013","unstructured":"Mohammad AA, Sohrab Z, Ali L, Ali E, Ioannis E (2013) Reservoir permeability prediction by neural networks combined with hybrid genetic algorithm and particle swarm optimization. Geophys Prospect 61(3):582\u2013598","journal-title":"Geophys Prospect"},{"key":"5163_CR40","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1016\/j.measurement.2014.09.075","volume":"60","author":"E Momeni","year":"2015","unstructured":"Momeni E, Armaghani DJ, Hajihassani M, Amin M (2015) Prediction of uniaxial compressive strength of rock samples using hybrid particle swarm optimization-based artificial neural networks. Measurement 60:50\u201363","journal-title":"Measurement"},{"key":"5163_CR41","unstructured":"Morrison RW (2003) Performance measurement in dynamic environments. In: Proceedings of the GECCO workshop on evolutionary algorithms for dynamic optimization problems, pp 5\u20138"},{"key":"5163_CR42","doi-asserted-by":"crossref","unstructured":"Pampara G, Engelbrecht AP (2015) Towards a generic computational intelligence library: preventing insanity. In: Proceedings of the IEEE symposium series on computational intelligence, pp 1460\u20131467","DOI":"10.1109\/SSCI.2015.207"},{"issue":"3","key":"5163_CR43","doi-asserted-by":"publisher","first-page":"666","DOI":"10.1016\/S0377-2217(00)00171-5","volume":"132","author":"M Qi","year":"2001","unstructured":"Qi M, Zhang GP (2001) An investigation of model selection criteria for neural network time series forecasting. Eur J Oper Res 132(3):666\u2013680","journal-title":"Eur J Oper Res"},{"key":"5163_CR44","unstructured":"Rakitianskaia A (2011) Using particle swarm optimisation to train feedforward neural networks in dynamic environments. MSc. thesis, University of Pretoria"},{"key":"5163_CR45","doi-asserted-by":"crossref","unstructured":"Rakitianskaia A, Engelbrecht AP (2008) Cooperative charged particle swarm optimiser. In: IEEE congress on evolutionary computation, pp 933\u2013939","DOI":"10.1109\/CEC.2008.4630908"},{"key":"5163_CR46","doi-asserted-by":"crossref","unstructured":"Rakitianskaia A, Engelbrecht AP (2009) Training neural networks with PSO in dynamic environments. In: IEEE congress on evolutionary computation, 2009 (CEC\u201909), pp 667\u2013673","DOI":"10.1109\/CEC.2009.4983009"},{"key":"5163_CR47","first-page":"125","volume":"2","author":"A Rakitianskaia","year":"2015","unstructured":"Rakitianskaia A, Engelbrecht AP (2015) Saturation in PSO neural network training: good or evil? IEEE Congr Evol Comput 2:125\u2013132","journal-title":"IEEE Congr Evol Comput"},{"key":"5163_CR48","doi-asserted-by":"crossref","unstructured":"Riedmiller M, Braun H (1993) A direct adaptive method for faster backpropagation learning: the RPROP algorithm. In: IEEE international conference on neural networks, pp 586\u2013591","DOI":"10.1109\/ICNN.1993.298623"},{"key":"5163_CR49","doi-asserted-by":"crossref","unstructured":"R\u00f6bel A (1994) The dynamic pattern selection algorithm: effective training and controlled generalization of backpropagation neural networks. In: Technical Report, Tech- nische Universit\u00e4t Berlin","DOI":"10.1007\/978-1-4471-2097-1_151"},{"issue":"4","key":"5163_CR50","doi-asserted-by":"publisher","first-page":"568","DOI":"10.1109\/5326.897083","volume":"30","author":"R Sitte","year":"2000","unstructured":"Sitte R, Sitte J (2000) Analysis of the predictive ability of time delay neural networks applied to the S&P 500 time series. IEEE Trans Syst Man Cybern C Appl Rev 30(4):568\u2013572","journal-title":"IEEE Trans Syst Man Cybern C Appl Rev"},{"key":"5163_CR51","doi-asserted-by":"publisher","unstructured":"Soodi HA, Vural AM (2018) STATCOM estimation using back-propagation, PSO, shuffled frog leap algorithm, and genetic algorithm based neural networks. Comput Intell Neurosci 2018:6381610. https:\/\/doi.org\/10.1155\/2018\/6381610","DOI":"10.1155\/2018\/6381610"},{"key":"5163_CR52","doi-asserted-by":"publisher","first-page":"268","DOI":"10.1016\/j.procs.2015.02.020","volume":"46","author":"A Suresh","year":"2015","unstructured":"Suresh A, Harish K, Radhika N (2015) Particle swarm optimization over back propagation neural network for length of stay prediction. Procedia Comput Sci 46:268\u2013275","journal-title":"Procedia Comput Sci"},{"issue":"4","key":"5163_CR53","doi-asserted-by":"publisher","first-page":"374","DOI":"10.1287\/ijoc.5.4.374","volume":"5","author":"Z Tang","year":"1993","unstructured":"Tang Z, Fishwick PA (1993) Feedforward neural nets as models for time series forecasting. ORSA J Comput 5(4):374\u2013385","journal-title":"ORSA J Comput"},{"key":"5163_CR54","doi-asserted-by":"crossref","unstructured":"Unger NJ, Ombuki-Berman BM, Engelbrecht AP (2013) Cooperative particle swarm optimization in dynamic environments. In: Proceedings of IEEE symposium on swarm intelligence, pp 172\u2013179","DOI":"10.1109\/SIS.2013.6615175"},{"key":"5163_CR55","unstructured":"Van\u00a0den Bergh F (2001) An analysis of particle swarm optimizers. Phd Thesis, University of Pretoria"},{"key":"5163_CR56","unstructured":"Van\u00a0den Bergh F, Engelbrecht AP (2001) Effects of swarm size on cooperative particle swarm optimisers. In: Proceedings of the genetic and evolutionary computation conference, pp 892\u2013899"},{"issue":"3","key":"5163_CR57","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1109\/TEVC.2004.826069","volume":"8","author":"F Van den Bergh","year":"2004","unstructured":"Van den Bergh F, Engelbrecht AP (2004) A cooperative approach to particle swarm optimization. IEEE Trans Evol Comput 8(3):225\u2013239","journal-title":"IEEE Trans Evol Comput"},{"key":"5163_CR58","doi-asserted-by":"crossref","unstructured":"Van\u00a0Wyk AB, Engelbrecht AP (2010) Overfitting by PSO trained feedforward neural networks. In: IEEE congress on evolutionary computation, pp 1\u20138","DOI":"10.1109\/CEC.2010.5586333"},{"issue":"6","key":"5163_CR59","doi-asserted-by":"publisher","first-page":"899","DOI":"10.1109\/72.165592","volume":"3","author":"LFA Wessels","year":"1992","unstructured":"Wessels LFA, Barnard E (1992) Avoiding false local minima by proper initialization of connections. IEEE Trans Neural Netw 3(6):899\u2013905","journal-title":"IEEE Trans Neural Netw"},{"issue":"2","key":"5163_CR60","doi-asserted-by":"publisher","first-page":"421","DOI":"10.3906\/elk-1212-151","volume":"23","author":"N Yalcin","year":"2015","unstructured":"Yalcin N, Tezel G, Karakuzu C (2015) Epilepsy diagnosis using artificial neural network learned by PSO. Turk J Electr Eng Comput Sci 23(2):421\u2013432","journal-title":"Turk J Electr Eng Comput Sci"},{"key":"5163_CR61","doi-asserted-by":"publisher","first-page":"741","DOI":"10.1016\/j.ijepes.2013.10.001","volume":"55","author":"W Yeh","year":"2014","unstructured":"Yeh W, Yeh Y, Chang P, Ke Y, Chung V (2014) Forecasting wind power in the Mai Liao wind farm based on the multi-layer perceptron artificial neural network model with improved simplified swarm optimization. Int J Electr Power Energy Syst 55:741\u2013748","journal-title":"Int J Electr Power Energy Syst"},{"issue":"1","key":"5163_CR62","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1016\/S0169-2070(97)00044-7","volume":"14","author":"G Zhang","year":"1998","unstructured":"Zhang G, Patuwo BE, Hu MY (1998) Forecasting with artificial neural networks: the state of the art. Int J Forecast 14(1):35\u201362","journal-title":"Int J Forecast"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-05163-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-020-05163-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-05163-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,7,6]],"date-time":"2021-07-06T23:34:24Z","timestamp":1625614464000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-020-05163-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,7]]},"references-count":62,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2021,4]]}},"alternative-id":["5163"],"URL":"https:\/\/doi.org\/10.1007\/s00521-020-05163-4","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,7,7]]},"assertion":[{"value":"4 February 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 June 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 July 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors declare that there is no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}