{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T07:34:27Z","timestamp":1740123267762,"version":"3.37.3"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,5,22]],"date-time":"2021-05-22T00:00:00Z","timestamp":1621641600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,5,22]],"date-time":"2021-05-22T00:00:00Z","timestamp":1621641600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100014717","name":"National Outstanding Youth Science Fund Project of National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["71625003"],"award-info":[{"award-number":["71625003"]}],"id":[{"id":"10.13039\/100014717","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Basic Research Program of China","doi-asserted-by":"publisher","award":["2016YFA0602504"],"award-info":[{"award-number":["2016YFA0602504"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["91746208, 71573016, 71403021, 71521002, 71774014, 71804010"],"award-info":[{"award-number":["91746208, 71573016, 71403021, 71521002, 71774014, 71804010"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Science and Technology Project of State Grid Jiangxi Electric Power Co., Ltd.","award":["521852200068"],"award-info":[{"award-number":["521852200068"]}]},{"DOI":"10.13039\/501100010031","name":"Postdoctoral Research Foundation of China","doi-asserted-by":"publisher","award":["2019T120055"],"award-info":[{"award-number":["2019T120055"]}],"id":[{"id":"10.13039\/501100010031","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Ann Oper Res"],"published-print":{"date-parts":[[2022,9]]},"DOI":"10.1007\/s10479-021-04119-8","type":"journal-article","created":{"date-parts":[[2021,5,22]],"date-time":"2021-05-22T17:02:41Z","timestamp":1621702961000},"page":"603-622","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Residential power\u00a0demand side management optimization based on fine-grained mixed frequency data"],"prefix":"10.1007","volume":"316","author":[{"given":"Bo","family":"wang","sequence":"first","affiliation":[]},{"given":"Nana","family":"Deng","sequence":"additional","affiliation":[]},{"given":"Wenhui","family":"Zhao","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6673-4037","authenticated-orcid":false,"given":"Zhaohua","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,5,22]]},"reference":[{"issue":"4","key":"4119_CR1","doi-asserted-by":"publisher","first-page":"2364","DOI":"10.1109\/TSG.2013.2254506","volume":"4","author":"A Agnetis","year":"2013","unstructured":"Agnetis, A., De Pascale, G., Detti, P., et al. (2013). Load scheduling for household energy consumption optimization. IEEE Transactions on Smart Grid, 4(4), 2364\u20132373.","journal-title":"IEEE Transactions on Smart Grid"},{"key":"4119_CR2","doi-asserted-by":"crossref","unstructured":"Alaqeel, T.A., Suryanarayanan, S. (2019). A comprehensive cost-benefit analysis of the penetration of Smart Grid technologies in the Saudi Arabian electricity infrastructure. Utilities Policy 60, 100\u2013933.","DOI":"10.1016\/j.jup.2019.100933"},{"key":"4119_CR3","doi-asserted-by":"crossref","unstructured":"Alberini A, Gans W, Velez-Lopez D. (2011). Residential consumption of gas and electricity in the US: The role of prices and income. Energy Economics, 33(5), 870\u2013881.","DOI":"10.1016\/j.eneco.2011.01.015"},{"key":"4119_CR4","doi-asserted-by":"crossref","unstructured":"Alvarez-Miranda E., Salgado-Rojas, J. et al. (2020). An integer programming method for the design of multi-criteria multi-action conservation plans. Omega, 92, 102147.","DOI":"10.1016\/j.omega.2019.102147"},{"issue":"1","key":"4119_CR5","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1016\/j.ijepes.2012.05.027","volume":"43","author":"M Amina","year":"2012","unstructured":"Amina, M., Kodogiannis, V. S., Petrounias, I., et al. (2012). A hybrid intelligent approach for the prediction of electricity consumption. International Journal of Electrical Power & Energy Systems, 43(1), 99\u2013108.","journal-title":"International Journal of Electrical Power & Energy Systems"},{"key":"4119_CR6","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1016\/j.apenergy.2014.03.054","volume":"125","author":"C Bartusch","year":"2014","unstructured":"Bartusch, C., & Alvehag, K. (2014). Further exploring the potential of residential demand response programs in electricity distribution. Applied Energy, 125, 39\u201359.","journal-title":"Applied Energy"},{"key":"4119_CR7","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1016\/j.apenergy.2016.02.090","volume":"170","author":"M Behl","year":"2016","unstructured":"Behl, M., Smarra, F., & Mangharam, R. (2016). DR-Advisor: A data-driven demand response recommender system. Applied Energy, 170, 30\u201346.","journal-title":"Applied Energy"},{"issue":"12","key":"4119_CR8","doi-asserted-by":"publisher","first-page":"7874","DOI":"10.1016\/j.enpol.2011.09.036","volume":"39","author":"JM Cayla","year":"2011","unstructured":"Cayla, J. M., Maizi, N., & Marchand, C. (2011). The role of income in energy consumption behaviour: Evidence from French households data. Energy Policy, 39(12), 7874\u20137883.","journal-title":"Energy Policy"},{"issue":"5","key":"4119_CR9","doi-asserted-by":"publisher","first-page":"2219","DOI":"10.1109\/TPWRS.2014.2307474","volume":"29","author":"C Chen","year":"2014","unstructured":"Chen, C., Wang, J., & Kishore, S. (2014). A Distributed Direct Load Control Approach for Large-Scale Residential Demand Response. Power Systems IEEE Transactions on, 29(5), 2219\u20132228.","journal-title":"Power Systems IEEE Transactions on"},{"key":"4119_CR10","doi-asserted-by":"publisher","first-page":"659","DOI":"10.1016\/j.apenergy.2017.03.034","volume":"195","author":"Y Chen","year":"2017","unstructured":"Chen, Y., Xu, P., Chu, Y., et al. (2017). Short-term electrical load forecasting using the Support Vector Regression (SVR) model to calculate the demand response baseline for office buildings [J]. Applied Energy, 195, 659\u2013670.","journal-title":"Applied Energy"},{"key":"4119_CR11","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1016\/j.rser.2015.12.130","volume":"59","author":"J Dong","year":"2016","unstructured":"Dong, J., Xue, G., & Li, R. (2016). Demand response in China: Regulations, pilot projects and recommendations \u2013 A review. Renewable and Sustainable Energy Reviews, 59, 13\u201327.","journal-title":"Renewable and Sustainable Energy Reviews"},{"key":"4119_CR12","doi-asserted-by":"publisher","first-page":"786","DOI":"10.1016\/j.energy.2015.01.090","volume":"82","author":"MA Fotouhi Ghazvini","year":"2015","unstructured":"Fotouhi Ghazvini, M. A., Faria, P., Ramos, S., et al. (2015). Incentive-based demand response programs designed by asset-light retail electricity providers for the day-ahead market. Energy, 82, 786\u2013799.","journal-title":"Energy"},{"issue":"2","key":"4119_CR13","doi-asserted-by":"publisher","first-page":"402","DOI":"10.1016\/j.ejor.2019.08.036","volume":"281","author":"L Gary","year":"2020","unstructured":"Gary, L., Amos, H. C., & Ng, T. A. (2020). A hybrid simulation-based optimization framework supporting strategic maintenance development to improve production performance. European Journal of Operational Research, 281(2), 402\u2013414.","journal-title":"European Journal of Operational Research"},{"key":"4119_CR14","doi-asserted-by":"crossref","unstructured":"Gillard R., Snell C., & Bevan M. (2017). Advancing an energy justice perspective of fuel poverty: Household vulnerability and domestic retrofit policy in the United Kingdom. Energy Research and Social Science, pp. 53\u201361.","DOI":"10.1016\/j.erss.2017.05.012"},{"key":"4119_CR15","doi-asserted-by":"crossref","unstructured":"Godin, C., & Lockwood, P. (1989). Dtw schemes for continuous speech recognition: A unified view. Computer Speech and Language, 3(2), 169\u2013198.","DOI":"10.1016\/0885-2308(89)90028-4"},{"issue":"1","key":"4119_CR16","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1016\/j.energy.2012.08.046","volume":"47","author":"Y He","year":"2012","unstructured":"He, Y., Wang, B., Wang, J., et al. (2012). Residential demand response behavior analysis based on Monte Carlo simulation: The case of Yinchuan in China. Energy, 47(1), 230\u2013236.","journal-title":"Energy"},{"key":"4119_CR17","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1016\/j.apenergy.2018.03.036","volume":"219","author":"M Hu","year":"2018","unstructured":"Hu, M., & Xiao, F. (2018). Price-responsive model-based optimal demand response control of inverter air conditioners using genetic algorithm. Applied Energy, 219, 151\u2013164.","journal-title":"Applied Energy"},{"issue":"2","key":"4119_CR18","doi-asserted-by":"publisher","first-page":"537","DOI":"10.1257\/aer.104.2.537","volume":"104","author":"K Ito","year":"2014","unstructured":"Ito, K. (2014). Do consumers respond to marginal or average price? Evidence from nonlinear electricity pricing. American Economic Review, 104(2), 537\u2013563.","journal-title":"American Economic Review"},{"issue":"1","key":"4119_CR19","doi-asserted-by":"publisher","first-page":"368","DOI":"10.1016\/j.apenergy.2010.07.021","volume":"88","author":"K Kavaklioglu","year":"2011","unstructured":"Kavaklioglu, K. (2011). Modeling and prediction of turkey\u2019s electricity consumption using support vector regression. Applied Energy, 88(1), 368\u2013375.","journal-title":"Applied Energy"},{"key":"4119_CR20","doi-asserted-by":"publisher","first-page":"1065","DOI":"10.1016\/j.apenergy.2016.09.051","volume":"183","author":"EAM Klaassen","year":"2016","unstructured":"Klaassen, E. A. M., Kobus, C. B. A., Frunt, J., et al. (2016). Responsiveness of residential electricity demand to dynamic tariffs: Experiences from a large field test in the Netherlands. Applied Energy, 183, 1065\u20131074.","journal-title":"Applied Energy"},{"issue":"2","key":"4119_CR21","doi-asserted-by":"publisher","first-page":"472","DOI":"10.1073\/pnas.1804667115","volume":"116","author":"Y Li","year":"2019","unstructured":"Li, Y., Pizer, W. A., & Wu, L. (2019). Climate change and residential electricity consumption in the Yangtze River Delta, China. Proceedings of the National Academy of Sciences, 116(2), 472\u2013477.","journal-title":"Proceedings of the National Academy of Sciences"},{"key":"4119_CR22","doi-asserted-by":"publisher","first-page":"796","DOI":"10.1016\/j.energy.2018.11.079","volume":"168","author":"J Mahmoudimehr","year":"2019","unstructured":"Mahmoudimehr, J., & Sebghati, P. (2019). A novel multi-objective dynamic programming optimization method: performance management of a solar thermal power plant as a case study. Energy, 168, 796\u2013814.","journal-title":"Energy"},{"issue":"2","key":"4119_CR23","doi-asserted-by":"publisher","first-page":"1108","DOI":"10.1109\/TPWRS.2015.2414880","volume":"31","author":"M Muratori","year":"2015","unstructured":"Muratori, M., & Rizzoni, G. (2015). Residential demand response: Dynamic energy management and time-varying electricity pricing. IEEE Transactions on Power Systems, 31(2), 1108\u20131117.","journal-title":"IEEE Transactions on Power Systems"},{"key":"4119_CR24","doi-asserted-by":"publisher","first-page":"1280","DOI":"10.1016\/j.apenergy.2017.06.066","volume":"210","author":"S Nan","year":"2017","unstructured":"Nan, S., Zhou, M., & Li, G. (2017). Optimal residential community demand response scheduling in smart grid. Applied Energy, 210, 1280\u20131289.","journal-title":"Applied Energy"},{"key":"4119_CR25","doi-asserted-by":"publisher","first-page":"963","DOI":"10.1016\/j.enconman.2014.11.001","volume":"89","author":"NI Nwulu","year":"2015","unstructured":"Nwulu, N. I., & Xia, X. (2015). Multi-objective dynamic economic emission dispatch of electric power generation integrated with game theory based demand response programs. Energy Conversion and Management, 89, 963\u2013974.","journal-title":"Energy Conversion and Management"},{"issue":"7","key":"4119_CR26","doi-asserted-by":"publisher","first-page":"1268","DOI":"10.1109\/JSAC.2013.130710","volume":"31","author":"LP Qian","year":"2013","unstructured":"Qian, L. P., Zhang, Y. J. A., Huang, J., et al. (2013). Demand response management via real-time electricity price control in smart grids. IEEE Journal on Selected Areas in Communications, 31(7), 1268\u20131280.","journal-title":"IEEE Journal on Selected Areas in Communications"},{"key":"4119_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.enbuild.2016.03.061","volume":"121","author":"S Rotger-Griful","year":"2016","unstructured":"Rotger-Griful, S., Jacobsen, R. H., Nguyen, D., et al. (2016). Demand response potential of ventilation systems in residential buildings. Energy and Buildings, 121, 1\u201310.","journal-title":"Energy and Buildings"},{"key":"4119_CR28","doi-asserted-by":"crossref","unstructured":"Soares, A., Gomes, A., Antunes, C. H. (2014). Categorization of residential electricity consumption as a basis for the assessment of the impacts of demand response actions. Renewable and Sustainable Energy Reviews, 30, 490\u2013503","DOI":"10.1016\/j.rser.2013.10.019"},{"key":"4119_CR29","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1016\/j.energy.2016.11.142","volume":"126","author":"D Srinivasan","year":"2017","unstructured":"Srinivasan, D., Rajgarhia, S., Radhakrishnan, B. M., Sharma, A., & Khincha, H. P. (2017). Game-Theory based dynamic pricing strategies for demand side management in smart grids. Energy, 126, 132\u2013143.","journal-title":"Energy"},{"key":"4119_CR30","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1016\/j.apenergy.2019.04.177","volume":"250","author":"R Tang","year":"2019","unstructured":"Tang, R., Wang, S., & Li, H. (2019). Game theory based interactive demand side management responding to dynamic pricing in price-based demand response of smart grids. Applied Energy, 250, 118\u2013130.","journal-title":"Applied Energy"},{"key":"4119_CR31","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1016\/j.jpdc.2017.06.007","volume":"117","author":"C Tong","year":"2018","unstructured":"Tong, C., Li, J., Lang, C., Kong, F., Niu, J., & Rodrigues, J. J. P. C. (2018). An efficient deep model for day-ahead electricity load forecasting with stacked denoising auto-encoders. Journal of Parallel and Distributed Computing, 117, 267\u2013273.","journal-title":"Journal of Parallel and Distributed Computing"},{"issue":"12","key":"4119_CR32","doi-asserted-by":"publisher","first-page":"3941","DOI":"10.1109\/JSAC.2016.2611958","volume":"34","author":"NH Tran","year":"2016","unstructured":"Tran, N. H., Oo, T. Z., Ren, S., Han, Z., Huh, E. N., & Hong, C. S. (2016). Reward-to-reduce: An incentive mechanism for economic demand response of colocation datacenters. IEEE Journal on Selected Areas in Communications, 34(12), 3941\u20133953.","journal-title":"IEEE Journal on Selected Areas in Communications"},{"key":"4119_CR33","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1016\/j.energy.2013.10.011","volume":"63","author":"Y Wang","year":"2013","unstructured":"Wang, Y., & Li, L. (2013). Time-of-use based electricity demand response for sustainable manufacturing systems. Energy, 63, 233\u2013244.","journal-title":"Energy"},{"issue":"1","key":"4119_CR34","doi-asserted-by":"publisher","first-page":"510","DOI":"10.1109\/TSG.2015.2409121","volume":"7","author":"Y Wang","year":"2015","unstructured":"Wang, Y., Ai, X., Tan, Z., Yan, L., & Liu, S. (2015). Interactive dispatch modes and bidding strategy of multiple virtual power plants based on demand response and game theory[J]. IEEE Transactions on Smart Grid, 7(1), 510\u2013519.","journal-title":"IEEE Transactions on Smart Grid"},{"key":"4119_CR35","doi-asserted-by":"crossref","unstructured":"Wang. Z., Zhao. W., Deng. N., Zhang. B.,Wang. B. (2020). Mixed data-driven decision-making in demand response management: An empirical evidence from dynamic time-warping based nonparametric-matching DID. Omega, p. 102233.","DOI":"10.1016\/j.omega.2020.102233"},{"issue":"1","key":"4119_CR36","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1038\/s41560-019-0507-y","volume":"5","author":"LV White","year":"2020","unstructured":"White, L. V., & Sintov, N. D. (2020). Health and financial impacts of demand-side response measures differ across sociodemographic groups. Nature Energy, 5(1), 50\u201360.","journal-title":"Nature Energy"},{"issue":"1\u20133","key":"4119_CR37","doi-asserted-by":"publisher","first-page":"366","DOI":"10.1016\/j.neucom.2009.08.005","volume":"73","author":"M Woellmer","year":"2009","unstructured":"Woellmer, M., Al-Hames, M., Eyben, F., Schuller, B., & Rigoll, G. (2009). A multidimensional dynamic time warping algorithm for efficient multimodal fusion of asynchronous data streams. Neurocomputing, 73(1\u20133), 366\u2013380.","journal-title":"Neurocomputing"},{"key":"4119_CR38","doi-asserted-by":"publisher","first-page":"116632","DOI":"10.1016\/j.energy.2019.116632","volume":"192","author":"Y Xiang","year":"2020","unstructured":"Xiang, Y., Cai, H. H., Gu, C. H., & Shen, X. D. (2020). Cost-benefit analysis of integrated energy system planning considering demand response. Energy, 192, 116632.","journal-title":"Energy"},{"issue":"8","key":"4119_CR39","doi-asserted-by":"publisher","first-page":"1910","DOI":"10.1049\/iet-gtd.2016.1066","volume":"11","author":"Q Yang","year":"2017","unstructured":"Yang, Q., & Fang, X. (2017). Demand response under real-time pricing for domestic households with renewable DGs and storage. Generation, Transmission and Distribution, IET, 2017, 11(8), 1910\u20131918.","journal-title":"Generation, Transmission and Distribution, IET, 2017"},{"key":"4119_CR40","doi-asserted-by":"crossref","unstructured":"Yazar, A., & Arslan, H. (2018). A flexibility metric and optimization methods for mixed numerologies in 5G and beyond. IEEE Access, pp. 1\u20131.","DOI":"10.1109\/ACCESS.2018.2795752"},{"key":"4119_CR41","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1016\/j.apenergy.2017.06.010","volume":"203","author":"M Yu","year":"2017","unstructured":"Yu, M., & Hong, S. H. (2017). Incentive-based demand response considering hierarchical electricity market: A Stackelberg game approach. Applied Energy, 203, 267\u2013279.","journal-title":"Applied Energy"},{"issue":"1","key":"4119_CR42","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-019-13993-7","volume":"11","author":"H Zhang","year":"2020","unstructured":"Zhang, H., Wu, K., Qiu, Y., Chan, G., Wang, S., Zhou, D., & Ren, X. (2020). Solar photovoltaic interventions have reduced rural poverty in China. Nature Communications, 11(1), 1\u201310.","journal-title":"Nature Communications"},{"key":"4119_CR43","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1016\/j.ins.2017.02.018","volume":"393","author":"Z Zhang","year":"2017","unstructured":"Zhang, Z., Tavenard, R., Bailly, A., Tang, X., Tang, P., & Corpetti, T. (2017). Dynamic time warping under limited warping path length. Information Sciences, 393, 91\u2013107.","journal-title":"Information Sciences"},{"issue":"2","key":"4119_CR44","doi-asserted-by":"publisher","first-page":"593","DOI":"10.1093\/qje\/qjr009","volume":"126","author":"N Nunn","year":"2011","unstructured":"Nunn, N., & Qian, N. (2011). The potato\u2019s contribution to population and urbanization: evidence from a historical experiment. The Quarterly Journal of Economics, 126(2), 593\u2013650.","journal-title":"The Quarterly Journal of Economics"},{"issue":"3","key":"4119_CR45","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10018-017-0205-6","volume":"20","author":"S Ratnasiri","year":"2018","unstructured":"Ratnasiri, S., Wilson, C., Athukorala, W., Garcia-Vali\u00f1as, M. A., Torgler, B., & Gifford, R. (2018). Effectiveness of two pricing structures on urban water use and conservation: a quasi-experimental investigation. Environmental Economics and Policy Studies, 20(3), 1\u201314.","journal-title":"Environmental Economics and Policy Studies"},{"key":"4119_CR46","doi-asserted-by":"publisher","first-page":"308","DOI":"10.1016\/j.rser.2018.12.054","volume":"103","author":"AR Jordehi","year":"2019","unstructured":"Jordehi, A. R. (2019). Optimisation of demand response in electric power systems, a review. Renewable and Sustainable Energy Reviews, 103, 308\u2013319.","journal-title":"Renewable and Sustainable Energy Reviews"},{"issue":"1","key":"4119_CR47","first-page":"240","volume":"10","author":"K Ito","year":"2018","unstructured":"Ito, K., Ida, T., & Tanaka, M. (2018). Moral suasion and economic incentives: field experimental evidence from energy demand. American Economic Journal: Economic Policy, 10(1), 240\u2013267.","journal-title":"American Economic Journal: Economic Policy"},{"issue":"3","key":"4119_CR48","first-page":"83","volume":"101","author":"FA Wolak","year":"2011","unstructured":"Wolak, F. A. (2011). Do residential customers respond to hourly prices? evidence from a dynamic pricing experiment. The American Rconomic Review, 101(3), 83\u201387.","journal-title":"The American Rconomic Review"},{"issue":"12","key":"4119_CR49","doi-asserted-by":"publisher","first-page":"1309","DOI":"10.1080\/00036846.2017.1361008","volume":"50","author":"J Yang","year":"2018","unstructured":"Yang, J., & Gao, M. (2018). The impact of education expansion on wage inequality. Applied Economics, 50(12), 1309\u20131323.","journal-title":"Applied Economics"}],"container-title":["Annals of Operations Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10479-021-04119-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10479-021-04119-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10479-021-04119-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,2]],"date-time":"2023-02-02T13:59:01Z","timestamp":1675346341000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10479-021-04119-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,22]]},"references-count":49,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,9]]}},"alternative-id":["4119"],"URL":"https:\/\/doi.org\/10.1007\/s10479-021-04119-8","relation":{},"ISSN":["0254-5330","1572-9338"],"issn-type":[{"type":"print","value":"0254-5330"},{"type":"electronic","value":"1572-9338"}],"subject":[],"published":{"date-parts":[[2021,5,22]]},"assertion":[{"value":"11 May 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 May 2021","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}