{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T15:23:29Z","timestamp":1759332209607},"reference-count":61,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2019,9,13]],"date-time":"2019-09-13T00:00:00Z","timestamp":1568332800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2019,9,13]],"date-time":"2019-09-13T00:00:00Z","timestamp":1568332800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Process Lett"],"published-print":{"date-parts":[[2020,2]]},"DOI":"10.1007\/s11063-019-10112-x","type":"journal-article","created":{"date-parts":[[2019,9,13]],"date-time":"2019-09-13T16:03:47Z","timestamp":1568390627000},"page":"759-796","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Random Regrouping and Factorization in Cooperative Particle Swarm Optimization Based Large-Scale Neural Network Training"],"prefix":"10.1007","volume":"51","author":[{"given":"Cody","family":"Dennis","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Beatrice M.","family":"Ombuki-Berman","sequence":"additional","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":[[2019,9,13]]},"reference":[{"key":"10112_CR1","doi-asserted-by":"crossref","unstructured":"Bai X, Gao X, Xue B (2018) Particle swarm optimization based two-stage feature selection in text mining. In: Proceedings of the congress on evolutionary computation. IEEE, pp 1\u20138","DOI":"10.1109\/CEC.2018.8477773"},{"issue":"6","key":"10112_CR2","doi-asserted-by":"publisher","first-page":"778","DOI":"10.1109\/3477.809032","volume":"29","author":"A Baraldi","year":"1999","unstructured":"Baraldi A, Blonda P (1999) A survey of fuzzy clustering algorithms for pattern recognition. IEEE Trans Syst Man Cybern Part B 29(6):778\u2013785. https:\/\/doi.org\/10.1109\/3477.809032","journal-title":"IEEE Trans Syst Man Cybern Part B"},{"key":"10112_CR3","doi-asserted-by":"crossref","DOI":"10.1093\/oso\/9780198538493.001.0001","volume-title":"Neural networks for pattern recognition","author":"C Bishop","year":"1995","unstructured":"Bishop C (1995) Neural networks for pattern recognition. Oxford University Press, Oxford"},{"key":"10112_CR4","unstructured":"Carlisle A, Dozier G (2001) An off-the-shelf pso. In: Proceedings of the workshop on particle swarm optimization, vol 1. Technology IUPUI, Indianapolis, IN, USA, pp 1\u20136"},{"key":"10112_CR5","unstructured":"Chen A, Huang S, Hong P, Cheng C, Lin E (2011) HDPS: heart disease prediction system. In: Computing in cardiology, pp 557\u2013560"},{"key":"10112_CR6","doi-asserted-by":"publisher","unstructured":"Chen A, Ren Z, Yang Y, Liang Y, Pang B (2018) A historical interdependency based differential grouping algorithm for large scale global optimization. In: Proceedings of the genetic and evolutionary computation conference companion, GECCO \u201918. ACM, New York, NY, USA, pp 1711\u20131715. https:\/\/doi.org\/10.1145\/3205651.3208278","DOI":"10.1145\/3205651.3208278"},{"key":"10112_CR7","unstructured":"Ciarelli P, Oliveira E (2009) CNAE-9 data set. https:\/\/archive.ics.uci.edu\/ml\/datasets\/CNAE-9 . Accessed 2 Aug 2018"},{"issue":"1","key":"10112_CR8","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1109\/4235.985692","volume":"6","author":"M Clerc","year":"2002","unstructured":"Clerc M, Kennedy J (2002) The particle swarm\u2014explosion, stability, and convergence in a multidimensional complex space. IEEE Trans Evolut Comput 6(1):58\u201373. https:\/\/doi.org\/10.1109\/4235.985692","journal-title":"IEEE Trans Evolut Comput"},{"issue":"4","key":"10112_CR9","doi-asserted-by":"crossref","first-page":"688","DOI":"10.1016\/j.engappai.2009.02.005","volume":"22","author":"M Das","year":"2009","unstructured":"Das M, Dulger L (2009) Signature vecification (SV) toolbox: applications of PSO-NN. Eng Appl Artif Intell 22(4):688\u2013694","journal-title":"Eng Appl Artif Intell"},{"key":"10112_CR10","first-page":"1","volume":"7","author":"J Demsar","year":"2006","unstructured":"Demsar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1\u201330","journal-title":"J Mach Learn Res"},{"key":"10112_CR11","doi-asserted-by":"crossref","unstructured":"Douglas J (2018) Efficient merging and decomposition variants of cooperative particle swarm optimization for large scale problems. Master\u2019s thesis, Brock University","DOI":"10.1145\/3206185.3206199"},{"key":"10112_CR12","doi-asserted-by":"crossref","unstructured":"Eberhart R, Shi Y (2000) Comparing inertia weights and constriction factors in particle swarm optimization. In: Proceedings of the congress on evolutionary computation, vol 1. IEEE, pp 84\u201388","DOI":"10.1109\/CEC.2000.870279"},{"key":"10112_CR13","doi-asserted-by":"publisher","unstructured":"Engelbrecht AP (2013) Roaming behavior of unconstrained particles. In: Proceedings of the Brazilian congress on computational intelligence, pp 104\u2013111. https:\/\/doi.org\/10.1109\/BRICS-CCI-CBIC.2013.28","DOI":"10.1109\/BRICS-CCI-CBIC.2013.28"},{"key":"10112_CR14","unstructured":"Fisher R (1936) Iris data set. https:\/\/archive.ics.uci.edu\/ml\/datasets\/Iris . Accessed 2 Aug 2018"},{"key":"10112_CR15","unstructured":"Forina M, et\u00a0al. (1991) Wine data set. https:\/\/archive.ics.uci.edu\/ml\/datasets\/Wine . Accessed 2 Aug 2018"},{"key":"10112_CR16","unstructured":"Graf F, Kriegel H, Schubert M, Poelsterl S, Cavallaro A (2011) Relative location of ct slices on axial axis data set. https:\/\/archive.ics.uci.edu\/ml\/datasets\/Relative+location+of+CT+slices+on+axial+axis# . Accessed 2 Aug 2018"},{"key":"10112_CR17","doi-asserted-by":"crossref","first-page":"889","DOI":"10.1007\/978-3-540-87700-4_88","volume-title":"Parallel Problem Solving from Nature\u2014PPSN X","author":"S Helwig","year":"2008","unstructured":"Helwig S, Wanka R (2008) Theoretical analysis of initial particle swarm behavior. In: Rudolph G, Jansen T, Beume N, Lucas S, Poloni C (eds) Parallel Problem Solving from Nature\u2014PPSN X. Springer, Berlin, pp 889\u2013898"},{"key":"10112_CR18","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1007\/978-3-642-04843-2_47","volume-title":"Advances in Computation and Intelligence","author":"C Hu","year":"2009","unstructured":"Hu C, Wu X, Wang Y, Xie F (2009) Multi-swarm particle swarm optimizer with cauchy mutation for dynamic optimization problems. In: Cai Z, Li Z, Kang Z, Liu Y (eds) Advances in Computation and Intelligence. Springer, Berlin, pp 443\u2013453"},{"key":"10112_CR19","doi-asserted-by":"crossref","unstructured":"Ismail A, Engelbrecht AP (2012) Measuring diversity in the cooperative particle swarm optimizer. In: Dorigo M, et al (eds) Proceedings of the international conference on swarm intelligence. Springer, Berlin, pp 97\u2013108","DOI":"10.1007\/978-3-642-32650-9_9"},{"key":"10112_CR20","unstructured":"Janosi A, Steinbrunn W, Pfisterer M, Detrano R (1989) Heart disease data set. https:\/\/archive.ics.uci.edu\/ml\/datasets\/Heart+Disease . Accessed 2 Aug 2018"},{"key":"10112_CR21","doi-asserted-by":"publisher","first-page":"1942","DOI":"10.1109\/ICNN.1995.488968","volume":"4","author":"J Kennedy","year":"1995","unstructured":"Kennedy J, Eberhart R (1995) Particle swarm optimization. Proc IEEE Int Conf Neural Netw 4:1942\u20131948. https:\/\/doi.org\/10.1109\/ICNN.1995.488968","journal-title":"Proc IEEE Int Conf Neural Netw"},{"key":"10112_CR22","doi-asserted-by":"crossref","unstructured":"Kennedy J, Mendes R (2002) Population structure and particle swarm performance. In: Proceedings of the international congress on evolutionary computation, vol 2. IEEE Computer Society, Washington, DC, USA, pp 1671\u20131676","DOI":"10.1109\/CEC.2002.1004493"},{"key":"10112_CR23","unstructured":"Lawrence S, Tsoi A, Back A (1996) Function approximation with neural networks and local methods: bias, variance and smoothness. In: Proceedings of the australian conference on neural networks, vol 1621. Australian National University"},{"key":"10112_CR24","unstructured":"LeCun Y, Cortes C, Burges J (1999) MNIST database of handwritten digits. http:\/\/yann.lecun.com\/exdb\/mnist\/ . Accessed 2 Aug 2018"},{"key":"10112_CR25","doi-asserted-by":"crossref","unstructured":"Lensen A, Xue B, Zhang M (2017) Using particle swarm optimisation and the silhouette metric to estimate the number of clusters, select features, and perform clustering. In: Proceedings of the European conference on the applications of evolutionary computation. Springer, pp 538\u2013554","DOI":"10.1007\/978-3-319-55849-3_35"},{"key":"10112_CR26","doi-asserted-by":"publisher","unstructured":"Li X, Yao X (2009) Tackling high dimensional nonseparable optimization problems by cooperatively coevolving particle swarms. In: Proceedings of the international congress on evolutionary computation, pp 1546\u20131553. https:\/\/doi.org\/10.1109\/CEC.2009.4983126","DOI":"10.1109\/CEC.2009.4983126"},{"issue":"2","key":"10112_CR27","doi-asserted-by":"publisher","first-page":"210","DOI":"10.1109\/TEVC.2011.2112662","volume":"16","author":"X Li","year":"2012","unstructured":"Li X, Yao X (2012) Cooperatively coevolving particle swarms for large scale optimization. IEEE Trans Evolut Comput 16(2):210\u2013224. https:\/\/doi.org\/10.1109\/TEVC.2011.2112662","journal-title":"IEEE Trans Evolut Comput"},{"key":"10112_CR28","doi-asserted-by":"publisher","first-page":"1895","DOI":"10.1109\/IJCNN.2002.1007808","volume":"2","author":"R Mendes","year":"2002","unstructured":"Mendes R, Cortez P, Rocha M, Neves J (2002) Particle swarms for feedforward neural network training. Proc IEEE Int Conf Neural Netw 2:1895\u20131899. https:\/\/doi.org\/10.1109\/IJCNN.2002.1007808","journal-title":"Proc IEEE Int Conf Neural Netw"},{"key":"10112_CR29","unstructured":"Michalski R, Chilausky R (1980) Soybean (large) data set. https:\/\/archive.ics.uci.edu\/ml\/datasets\/Soybean+%28Large%29 . Accessed 2 Aug 2018"},{"key":"10112_CR30","doi-asserted-by":"publisher","unstructured":"Mikula M, Gao X, Machov\u00e1 K (2017) Adapting sentiment analysis system from english to slovak. In: Proceedings of the symposium series on computational intelligence, pp 1\u20138. https:\/\/doi.org\/10.1109\/SSCI.2017.8285313","DOI":"10.1109\/SSCI.2017.8285313"},{"key":"10112_CR31","doi-asserted-by":"crossref","unstructured":"Oldewage E (2018) The perils of particle swarm optimization in high dimensional problem spaces. Master\u2019s thesis, University of Pretoria","DOI":"10.1007\/978-3-030-00533-7_27"},{"key":"10112_CR32","doi-asserted-by":"publisher","unstructured":"Oldewage E, Engelbrecht AP, Cleghorn C (2017) The merits of velocity clamping particle swarm optimisation in high dimensional spaces. In: Symposium series on computational intelligence, pp 1\u20138. https:\/\/doi.org\/10.1109\/SSCI.2017.8280887","DOI":"10.1109\/SSCI.2017.8280887"},{"key":"10112_CR33","doi-asserted-by":"crossref","unstructured":"Oldewage E, Engelbrecht A, Cleghorn C (2018) The importance of component-wise stochasticity in particle swarm optimization. In: International conference on swarm intelligence. Springer, pp 264\u2013276","DOI":"10.1007\/978-3-030-00533-7_21"},{"issue":"3","key":"10112_CR34","doi-asserted-by":"publisher","first-page":"378","DOI":"10.1109\/TEVC.2013.2281543","volume":"18","author":"MN Omidvar","year":"2014","unstructured":"Omidvar MN, Li X, Mei Y, Yao X (2014) Cooperative co-evolution with differential grouping for large scale optimization. IEEE Trans Evol Comput 18(3):378\u2013393. https:\/\/doi.org\/10.1109\/TEVC.2013.2281543","journal-title":"IEEE Trans Evol Comput"},{"issue":"6","key":"10112_CR35","doi-asserted-by":"publisher","first-page":"929","DOI":"10.1109\/TEVC.2017.2694221","volume":"21","author":"MN Omidvar","year":"2017","unstructured":"Omidvar MN, Yang M, Mei Y, Li X, Yao X (2017) DG2: a faster and more accurate differential grouping for large-scale black-box optimization. IEEE Trans Evol Comput 21(6):929\u2013942. https:\/\/doi.org\/10.1109\/TEVC.2017.2694221","journal-title":"IEEE Trans Evol Comput"},{"key":"10112_CR36","doi-asserted-by":"publisher","unstructured":"Pillai K, Sheppard J (2011) Overlapping swarm intelligence for training artificial neural networks. In: Proceedings of the Symposium on Swarm Intelligence, pp 1\u20138. https:\/\/doi.org\/10.1109\/SIS.2011.5952566","DOI":"10.1109\/SIS.2011.5952566"},{"key":"10112_CR37","doi-asserted-by":"publisher","unstructured":"Qureshi S, Sheppard JW (2016) Dynamic sampling in training artificial neural networks with overlapping swarm intelligence. In: Proceedings of the congress on evolutionary computation, pp 440\u2013446. https:\/\/doi.org\/10.1109\/CEC.2016.7743827","DOI":"10.1109\/CEC.2016.7743827"},{"key":"10112_CR38","doi-asserted-by":"publisher","unstructured":"Rakitianskaia A, Engelbrecht AP (2014a) Training high-dimensional neural networks with cooperative particle swarm optimiser. In: Proceedings of the international joint conference on neural networks, pp 4011\u20134018. https:\/\/doi.org\/10.1109\/IJCNN.2014.6889933","DOI":"10.1109\/IJCNN.2014.6889933"},{"key":"10112_CR39","doi-asserted-by":"publisher","unstructured":"Rakitianskaia A, Engelbrecht AP (2014b) Weight regularisation in particle swarm optimisation neural network training. In: Proceedings of the symposium on swarm intelligence, pp 1\u20138. https:\/\/doi.org\/10.1109\/SIS.2014.7011773","DOI":"10.1109\/SIS.2014.7011773"},{"key":"10112_CR40","unstructured":"Redmond M (2009) Communities and crime data set. https:\/\/archive.ics.uci.edu\/ml\/datasets\/Communities+and+Crime . Accessed 2 Aug 2018"},{"key":"10112_CR41","doi-asserted-by":"publisher","unstructured":"Ren Z, Chen A, Wang L, Liang Y, Pang B (2017) An efficient vector-growth decomposition algorithm for cooperative coevolution in solving large scale problems. In: Proceedings of the genetic and evolutionary computation conference companion, GECCO \u201917, ACM, New York, NY, USA, pp 41\u201342. https:\/\/doi.org\/10.1145\/3067695.3082048","DOI":"10.1145\/3067695.3082048"},{"key":"10112_CR42","doi-asserted-by":"crossref","unstructured":"R\u00f6bel A (1994) The dynamic pattern selection algorithm: effective training and controlled generalization of backpropagation neural networks. Technical report, Technische Universit\u00e4t Berlin","DOI":"10.1007\/978-1-4471-2097-1_151"},{"issue":"1","key":"10112_CR43","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/S0167-9236(00)00086-5","volume":"30","author":"RS Sexton","year":"2000","unstructured":"Sexton RS, Dorsey RE (2000) Reliable classification using neural networks: a genetic algorithm and backpropagation comparison. Decis Support Syst 30(1):11\u201322","journal-title":"Decis Support Syst"},{"key":"10112_CR44","doi-asserted-by":"publisher","unstructured":"Shi Y, Eberhart R (1998) A modified particle swarm optimizer. In: Proceedings of the international congress on evolutionary computation, pp 69\u201373. https:\/\/doi.org\/10.1109\/ICEC.1998.699146","DOI":"10.1109\/ICEC.1998.699146"},{"issue":"2","key":"10112_CR45","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1109\/TEVC.2016.2601922","volume":"21","author":"S Strasser","year":"2017","unstructured":"Strasser S, Sheppard J, Fortier N, Goodman R (2017) Factored evolutionary algorithms. IEEE Trans Evol Comput 21(2):281\u2013293. https:\/\/doi.org\/10.1109\/TEVC.2016.2601922","journal-title":"IEEE Trans Evol Comput"},{"issue":"1","key":"10112_CR46","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.ins.2011.09.033","volume":"186","author":"L Sun","year":"2012","unstructured":"Sun L, Yoshida S, Cheng X, Liang Y (2012) A cooperative particle swarm optimizer with statistical variable interdependence learning. Inf Sci 186(1):20\u201339","journal-title":"Inf Sci"},{"issue":"5","key":"10112_CR47","doi-asserted-by":"crossref","first-page":"647","DOI":"10.1109\/TEVC.2017.2778089","volume":"22","author":"Y Sun","year":"2018","unstructured":"Sun Y, Kirley M, Halgamuge SK (2018) A recursive decomposition method for large scale continuous optimization. IEEE Trans Evol Comput 22(5):647\u2013661","journal-title":"IEEE Trans Evol Comput"},{"issue":"9","key":"10112_CR48","doi-asserted-by":"crossref","first-page":"2719","DOI":"10.1007\/s10489-017-1113-y","volume":"48","author":"R Tang","year":"2018","unstructured":"Tang R, Li X (2018) Adaptive multi-context cooperatively coevolving in differential evolution. Appl Intell 48(9):2719\u20132729","journal-title":"Appl Intell"},{"issue":"16","key":"10112_CR49","doi-asserted-by":"crossref","first-page":"4735","DOI":"10.1007\/s00500-016-2081-6","volume":"21","author":"R Tang","year":"2017","unstructured":"Tang R, Wu Z, Fang Y (2017) Adaptive multi-context cooperatively coevolving particle swarm optimization for large-scale problems. Soft Comput 21(16):4735\u20134754","journal-title":"Soft Comput"},{"key":"10112_CR50","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1016\/j.apenergy.2018.06.092","volume":"228","author":"R Tang","year":"2018","unstructured":"Tang R, Li X, Lai J (2018) A novel optimal energy-management strategy for a maritime hybrid energy system based on large-scale global optimization. Appl Energy 228:254\u2013264","journal-title":"Appl Energy"},{"key":"10112_CR51","unstructured":"Van den Bergh F (2001) An analysis of particle swarm optimizers. PhD thesis, University of Pretoria"},{"issue":"26","key":"10112_CR52","first-page":"84","volume":"2000","author":"F Van den Bergh","year":"2000","unstructured":"Van den Bergh F, Engelbrecht AP (2000) Cooperative learning in neural networks using particle swarm optimizers. S Afr Comput J 2000(26):84\u201390","journal-title":"S Afr Comput J"},{"issue":"3","key":"10112_CR53","doi-asserted-by":"crossref","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":"10112_CR54","unstructured":"Van der Putten P, Van Someren M (eds) (2000) Insurance company benchmark (coil 2000) data set. https:\/\/archive.ics.uci.edu\/ml\/datasets\/Insurance+Company+Benchmark+%28COIL+2000%29 . Accessed 02 Aug 2018"},{"key":"10112_CR55","doi-asserted-by":"publisher","unstructured":"Van Wyk A, Engelbrecht AP (2010) Overfitting by PSO trained feedforward neural networks. In: Proceedings of the congress on evolutionary computation, pp 1\u20138. https:\/\/doi.org\/10.1109\/CEC.2010.5586333","DOI":"10.1109\/CEC.2010.5586333"},{"key":"10112_CR56","doi-asserted-by":"publisher","unstructured":"Van Wyk A, Engelbrecht AP (2016) Analysis of activation functions for particle swarm optimised feedforward neural networks. In: Proceedings of the congress on evolutionary computation, pp 423\u2013430. https:\/\/doi.org\/10.1109\/CEC.2016.7743825","DOI":"10.1109\/CEC.2016.7743825"},{"key":"10112_CR57","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1007\/978-3-319-41000-5_37","volume-title":"Advances in Swarm Intelligence","author":"A Volschenk","year":"2016","unstructured":"Volschenk A, Engelbrecht AP (2016) An analysis of competitive coevolutionary particle swarm optimizers to train neural network game tree evaluation functions. In: Tan Y, Shi Y, Niu B (eds) Advances in Swarm Intelligence. Springer, Cham, pp 369\u2013380"},{"key":"10112_CR58","unstructured":"Wolberg W (1990) Breast cancer Wisconsin (original) data set. https:\/\/archive.ics.uci.edu\/ml\/datasets\/Breast+Cancer+Wisconsin+%28Original%29 . Accessed 2 Aug 2018"},{"key":"10112_CR59","unstructured":"Xiao H, Rasul K, Vollgraf R (2017) Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms. https:\/\/arxiv.org\/abs\/1708.07747 . Accessed 2 Aug 2018"},{"key":"10112_CR60","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/j.asoc.2014.12.026","volume":"29","author":"X Xu","year":"2015","unstructured":"Xu X, Tang Y, Li J, Hua C, Guan X (2015) Dynamic multi-swarm particle swarm optimizer with cooperative learning strategy. Appl Soft Comput 29:169\u2013183","journal-title":"Appl Soft Comput"},{"key":"10112_CR61","doi-asserted-by":"publisher","unstructured":"Zyl E, Engelbrecht AP (2015) A subspace-based method for PSO initialization. In: Symposium series on computational intelligence, pp 226\u2013233. https:\/\/doi.org\/10.1109\/SSCI.2015.42","DOI":"10.1109\/SSCI.2015.42"}],"container-title":["Neural Processing Letters"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-019-10112-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11063-019-10112-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-019-10112-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,23]],"date-time":"2024-07-23T11:37:57Z","timestamp":1721734677000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11063-019-10112-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,9,13]]},"references-count":61,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2020,2]]}},"alternative-id":["10112"],"URL":"https:\/\/doi.org\/10.1007\/s11063-019-10112-x","relation":{},"ISSN":["1370-4621","1573-773X"],"issn-type":[{"value":"1370-4621","type":"print"},{"value":"1573-773X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,9,13]]},"assertion":[{"value":"13 September 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}