{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T00:45:50Z","timestamp":1772844350215,"version":"3.50.1"},"reference-count":53,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2023,12,22]],"date-time":"2023-12-22T00:00:00Z","timestamp":1703203200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,22]],"date-time":"2023-12-22T00:00:00Z","timestamp":1703203200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cluster Comput"],"published-print":{"date-parts":[[2024,7]]},"DOI":"10.1007\/s10586-023-04205-5","type":"journal-article","created":{"date-parts":[[2023,12,22]],"date-time":"2023-12-22T07:02:43Z","timestamp":1703228563000},"page":"4779-4803","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["An ensemble clustering approach for modeling hidden categorization perspectives for cloud workloads"],"prefix":"10.1007","volume":"27","author":[{"given":"Mustafa","family":"Daraghmeh","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anjali","family":"Agarwal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yaser","family":"Jararweh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,12,22]]},"reference":[{"issue":"17","key":"4205_CR1","doi-asserted-by":"publisher","first-page":"5322","DOI":"10.3390\/en14175322","volume":"14","author":"S Jayaprakash","year":"2021","unstructured":"Jayaprakash, S., Nagarajan, M.D., de Prado, R.P., et al.: A systematic review of energy management strategies for resource allocation in the cloud: clustering, optimization and machine learning. Energies 14(17), 5322 (2021)","journal-title":"Energies"},{"issue":"3","key":"4205_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2856127","volume":"48","author":"MC Calzarossa","year":"2016","unstructured":"Calzarossa, M.C., Massari, L., Tessera, D.: Workload characterization: a survey revisited. ACM Comput. Surv. (CSUR) 48(3), 1\u201343 (2016)","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"4205_CR3","first-page":"111","volume":"1","author":"SB Kotsiantis","year":"2006","unstructured":"Kotsiantis, S.B., Kanellopoulos, D., Pintelas, P.E.: Data preprocessing for supervised leaning. Int. J. Electr. Comput. Eng. 1, 111\u2013117 (2006)","journal-title":"Int. J. Electr. Comput. Eng."},{"key":"4205_CR4","doi-asserted-by":"publisher","unstructured":"Satopaa, V., Albrecht, J., Irwin, D., et\u00a0al.: Finding a \u201cKneedle\u201d in a haystack: detecting knee points in system behavior. In: 2011 31st International Conference on Distributed Computing Systems Workshops, pp. 166\u2013171 (2011). https:\/\/doi.org\/10.1109\/ICDCSW.2011.20","DOI":"10.1109\/ICDCSW.2011.20"},{"issue":"4","key":"4205_CR5","first-page":"1504","volume":"8","author":"A Abdelsamea","year":"2014","unstructured":"Abdelsamea, A., Hemayed, E.E., Eldeeb, H., et al.: Virtual machine consolidation challenges: a review. Int. J. Innov. Appl. Stud. 8(4), 1504 (2014)","journal-title":"Int. J. Innov. Appl. Stud."},{"key":"4205_CR6","doi-asserted-by":"publisher","first-page":"100514","DOI":"10.1016\/j.cosrev.2022.100514","volume":"46","author":"N Thakur","year":"2022","unstructured":"Thakur, N., Singh, A., Sangal, A.: Cloud services selection: a systematic review and future research directions. Comput. Sci. Rev. 46, 100514 (2022)","journal-title":"Comput. Sci. Rev."},{"issue":"6","key":"4205_CR7","doi-asserted-by":"publisher","first-page":"652","DOI":"10.1504\/IJGUC.2022.128319","volume":"13","author":"K Zaman","year":"2022","unstructured":"Zaman, K., Hussain, A., Imran, M., et al.: Cost-effective data replication mechanism modelling for cloud storage. Int. J. Grid Util. Comput. 13(6), 652\u2013669 (2022)","journal-title":"Int. J. Grid Util. Comput."},{"issue":"12","key":"4205_CR8","doi-asserted-by":"publisher","first-page":"15222","DOI":"10.1007\/s10489-022-04164-1","volume":"53","author":"L Zhu","year":"2023","unstructured":"Zhu, L., Huang, K., Fu, K., et al.: A priority-aware scheduling framework for heterogeneous workloads in container-based cloud. Appl. Intell. 53(12), 15222\u201315245 (2023)","journal-title":"Appl. Intell."},{"key":"4205_CR9","doi-asserted-by":"crossref","unstructured":"Estrada, R., Valeriano, I., Aizaga, X.: CPU usage prediction model: a simplified VM clustering approach. In: Conference on Complex, Intelligent, and Software Intensive Systems, pp. 210\u2013221. Springer (2023)","DOI":"10.1007\/978-3-031-35734-3_21"},{"issue":"5","key":"4205_CR10","doi-asserted-by":"publisher","first-page":"2559","DOI":"10.47836\/pjst.31.5.27","volume":"31","author":"A Katal","year":"2023","unstructured":"Katal, A., Dahiya, S., Choudhury, T.: Workload characterization and classification: a step towards better resource utilization in a cloud data center. Pertanika J. Sci. Technol. 31(5), 2559\u20132575 (2023)","journal-title":"Pertanika J. Sci. Technol."},{"issue":"1","key":"4205_CR11","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1007\/s10586-020-03107-0","volume":"24","author":"A Shahidinejad","year":"2021","unstructured":"Shahidinejad, A., Ghobaei-Arani, M., Masdari, M.: Resource provisioning using workload clustering in cloud computing environment: a hybrid approach. Clust. Comput. 24(1), 319\u2013342 (2021)","journal-title":"Clust. Comput."},{"key":"4205_CR12","doi-asserted-by":"publisher","first-page":"1367","DOI":"10.1007\/s11277-018-6089-3","volume":"104","author":"M Askarizade Haghighi","year":"2019","unstructured":"Askarizade Haghighi, M., Maeen, M., Haghparast, M.: An energy-efficient dynamic resource management approach based on clustering and meta-heuristic algorithms in cloud computing IAAS platforms: energy efficient dynamic cloud resource management. Wirel. Pers. Commun. 104, 1367\u20131391 (2019)","journal-title":"Wirel. Pers. Commun."},{"key":"4205_CR13","doi-asserted-by":"crossref","unstructured":"Dezhabad, N., Ganti, S., Shoja, G.: Cloud workload characterization and profiling for resource allocation. In: 2019 IEEE 8th International Conference on Cloud Networking (CloudNet), pp. 1\u20134. IEEE (2019)","DOI":"10.1109\/CloudNet47604.2019.9064138"},{"issue":"5","key":"4205_CR14","doi-asserted-by":"publisher","first-page":"1876","DOI":"10.1109\/TII.2017.2757606","volume":"14","author":"P Neamatollahi","year":"2017","unstructured":"Neamatollahi, P., Abrishami, S., Naghibzadeh, M., et al.: Hierarchical clustering-task scheduling policy in cluster-based wireless sensor networks. IEEE Trans. Ind. Inf. 14(5), 1876\u20131886 (2017)","journal-title":"IEEE Trans. Ind. Inf."},{"key":"4205_CR15","doi-asserted-by":"crossref","unstructured":"Orzechowski, P., Proficz, J., Krawczyk, H., et\u00a0al.: Categorization of cloud workload types with clustering. In: Proceedings of the International Conference on Signal, Networks, Computing, and Systems: ICSNCS 2016, vol. 1, pp. 303\u2013313. Springer (2017)","DOI":"10.1007\/978-81-322-3592-7_31"},{"key":"4205_CR16","doi-asserted-by":"crossref","unstructured":"Jivrajani, A., Raghu, D., Apoorva, K., et\u00a0al.: Workload characterization and green scheduling on heterogeneous clusters. In: 2016 22nd Annual International Conference on Advanced Computing and Communication (ADCOM), pp. 3\u20138. IEEE (2016)","DOI":"10.1109\/ADCOM.2016.10"},{"key":"4205_CR17","doi-asserted-by":"crossref","unstructured":"Xia, Q., Lan, Y., Zhao, L., et\u00a0al.: Energy-saving analysis of cloud workload based on k-means clustering. In: 2014 IEEE Computers, Communications and IT Applications Conference, pp. 305\u2013309. IEEE (2014)","DOI":"10.1109\/ComComAp.2014.7017215"},{"key":"4205_CR18","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., Varoquaux, G., Gramfort, A., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011)","journal-title":"J. Mach. Learn. Res."},{"key":"4205_CR19","unstructured":"Yousif, S.A., Al-Dulaimy, A.: Clustering cloud workload traces to improve the performance of cloud data centers. In: Proceedings of the World Congress on Engineering, pp. 7\u201310 (2017)"},{"key":"4205_CR20","doi-asserted-by":"crossref","unstructured":"Gu, Z., Tang, S., Jiang, B., et\u00a0al.: Characterizing job-task dependency in cloud workloads using graph learning. In: 2021 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), pp. 288\u2013297. IEEE (2021)","DOI":"10.1109\/IPDPSW52791.2021.00052"},{"key":"4205_CR21","doi-asserted-by":"crossref","unstructured":"Gao, J., Wang, H., Shen, H.: Machine learning based workload prediction in cloud computing. In: 2020 29th International Conference on Computer Communications and Networks (ICCCN), pp. 1\u20139. IEEE (2020)","DOI":"10.1109\/ICCCN49398.2020.9209730"},{"key":"4205_CR22","doi-asserted-by":"crossref","unstructured":"Ismaeel, S., Al-Khazraji, A., Miri, A.: An efficient workload clustering framework for large-scale data centers. In: 2019 8th International Conference on Modeling Simulation and Applied Optimization (ICMSAO), pp. 1\u20135. IEEE (2019)","DOI":"10.1109\/ICMSAO.2019.8880305"},{"key":"4205_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11704-019-9059-3","volume":"15","author":"P Bhattacharjee","year":"2021","unstructured":"Bhattacharjee, P., Mitra, P.: A survey of density based clustering algorithms. Front. Comput. Sci. 15, 1\u201327 (2021)","journal-title":"Front. Comput. Sci."},{"issue":"1","key":"4205_CR24","doi-asserted-by":"publisher","first-page":"711","DOI":"10.1007\/s11227-020-03296-w","volume":"77","author":"M Ghobaei-Arani","year":"2021","unstructured":"Ghobaei-Arani, M., Shahidinejad, A.: An efficient resource provisioning approach for analyzing cloud workloads: a metaheuristic-based clustering approach. J. Supercomput. 77(1), 711\u2013750 (2021)","journal-title":"J. Supercomput."},{"key":"4205_CR25","doi-asserted-by":"publisher","first-page":"579","DOI":"10.1109\/ACCESS.2021.3134704","volume":"10","author":"M Tareq","year":"2021","unstructured":"Tareq, M., Sundararajan, E.A., Harwood, A., et al.: A systematic review of density grid-based clustering for data streams. IEEE Access 10, 579\u2013596 (2021)","journal-title":"IEEE Access"},{"key":"4205_CR26","first-page":"102613","volume":"53","author":"S Bharany","year":"2022","unstructured":"Bharany, S., Badotra, S., Sharma, S., et al.: Energy efficient fault tolerance techniques in green cloud computing: a systematic survey and taxonomy. Sustain. Energy Technol. Assess. 53, 102613 (2022)","journal-title":"Sustain. Energy Technol. Assess."},{"key":"4205_CR27","doi-asserted-by":"publisher","first-page":"751","DOI":"10.1007\/s00607-014-0407-8","volume":"98","author":"A Hameed","year":"2016","unstructured":"Hameed, A., Khoshkbarforoushha, A., Ranjan, R., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98, 751\u2013774 (2016)","journal-title":"Computing"},{"key":"4205_CR28","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1016\/j.jnca.2016.12.017","volume":"80","author":"C Liu","year":"2017","unstructured":"Liu, C., Liu, C., Shang, Y., et al.: An adaptive prediction approach based on workload pattern discrimination in the cloud. J. Netw. Comput. Appl. 80, 35\u201344 (2017)","journal-title":"J. Netw. Comput. Appl."},{"key":"4205_CR29","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1016\/j.future.2022.11.014","volume":"141","author":"Y Liang","year":"2023","unstructured":"Liang, Y., Chen, K., Yi, L., et al.: DeGTeC: a deep graph-temporal clustering framework for data-parallel job characterization in data centers. Future Gener. Comput. Syst. 141, 81\u201395 (2023)","journal-title":"Future Gener. Comput. Syst."},{"issue":"12","key":"4205_CR30","doi-asserted-by":"publisher","first-page":"11565","DOI":"10.1016\/j.aej.2022.05.017","volume":"61","author":"H Ikhlasse","year":"2022","unstructured":"Ikhlasse, H., Benjamin, D., Vincent, C., et al.: Multimodal cloud resources utilization forecasting using a bidirectional gated recurrent unit predictor based on a power efficient stacked denoising autoencoders. Alex. Eng. J. 61(12), 11565\u201311577 (2022)","journal-title":"Alex. Eng. J."},{"key":"4205_CR31","doi-asserted-by":"publisher","first-page":"110596","DOI":"10.1016\/j.jss.2020.110596","volume":"166","author":"SS Gill","year":"2020","unstructured":"Gill, S.S., Tuli, S., Toosi, A.N., et al.: ThermoSim: deep learning based framework for modeling and simulation of thermal-aware resource management for cloud computing environments. J. Syst. Softw. 166, 110596 (2020)","journal-title":"J. Syst. Softw."},{"issue":"7","key":"4205_CR32","doi-asserted-by":"publisher","first-page":"3170","DOI":"10.1109\/TII.2018.2808910","volume":"14","author":"Q Zhang","year":"2018","unstructured":"Zhang, Q., Yang, L.T., Yan, Z., et al.: An efficient deep learning model to predict cloud workload for industry informatics. IEEE Trans. Ind. Inf. 14(7), 3170\u20133178 (2018)","journal-title":"IEEE Trans. Ind. Inf."},{"key":"4205_CR33","doi-asserted-by":"crossref","unstructured":"Gupta, S., Muthiyan, N., Kumar, S., et\u00a0al.: A supervised deep learning framework for proactive anomaly detection in cloud workloads. In: 2017 14th IEEE India Council International Conference (INDICON), pp. 1\u20136. IEEE (2017)","DOI":"10.1109\/INDICON.2017.8488109"},{"key":"4205_CR34","doi-asserted-by":"publisher","first-page":"3037","DOI":"10.1007\/s11227-015-1426-8","volume":"71","author":"Q Yang","year":"2015","unstructured":"Yang, Q., Zhou, Y., Yu, Y., et al.: Multi-step-ahead host load prediction using autoencoder and echo state networks in cloud computing. J. Supercomput. 71, 3037\u20133053 (2015)","journal-title":"J. Supercomput."},{"issue":"Dec","key":"4205_CR35","first-page":"583","volume":"3","author":"A Strehl","year":"2002","unstructured":"Strehl, A., Ghosh, J.: Cluster ensembles\u2013a knowledge reuse framework for combining multiple partitions. J. Mach. Learn. Res. 3(Dec), 583\u2013617 (2002)","journal-title":"J. Mach. Learn. Res."},{"key":"4205_CR36","doi-asserted-by":"crossref","unstructured":"Topchy, A., Jain, A.K., Punch, W.: A mixture model for clustering ensembles. In: Proceedings of the 2004 SIAM International Conference on Data Mining, pp. 379\u2013390. SIAM (2004)","DOI":"10.1137\/1.9781611972740.35"},{"key":"4205_CR37","doi-asserted-by":"crossref","unstructured":"Caruana, R., Elhawary, M., Nguyen, N., et\u00a0al.: Meta clustering. In: Sixth International Conference on Data Mining (ICDM\u201906), pp. 107\u2013118. IEEE (2006)","DOI":"10.1109\/ICDM.2006.103"},{"key":"4205_CR38","doi-asserted-by":"publisher","DOI":"10.1080\/01969722.2022.2110682","author":"B Zhou","year":"2022","unstructured":"Zhou, B., Lu, B., Saeidlou, S.: A hybrid clustering method based on the several diverse basic clustering and meta-clustering aggregation technique. Cybern. Syst. (2022). https:\/\/doi.org\/10.1080\/01969722.2022.2110682","journal-title":"Cybern. Syst."},{"issue":"4","key":"4205_CR39","first-page":"3375","volume":"56","author":"K Li","year":"2020","unstructured":"Li, K., Cao, X., Ge, X., et al.: Meta-heuristic optimization-based two-stage residential load pattern clustering approach considering intra-cluster compactness and inter-cluster separation. IEEE Trans. Ind. Appl. 56(4), 3375\u20133384 (2020)","journal-title":"IEEE Trans. Ind. Appl."},{"key":"4205_CR40","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1007\/s11704-019-8208-z","volume":"14","author":"X Dong","year":"2020","unstructured":"Dong, X., Yu, Z., Cao, W., et al.: A survey on ensemble learning. Front. Comput. Sci. 14, 241\u2013258 (2020)","journal-title":"Front. Comput. Sci."},{"issue":"03","key":"4205_CR41","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1142\/S0218001411008683","volume":"25","author":"S Vega-Pons","year":"2011","unstructured":"Vega-Pons, S., Ruiz-Shulcloper, J.: A survey of clustering ensemble algorithms. Int. J. Pattern Recognit. Artif. Intell. 25(03), 337\u2013372 (2011)","journal-title":"Int. J. Pattern Recognit. Artif. Intell."},{"key":"4205_CR42","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/0377-0427(87)90125-7","volume":"20","author":"PJ Rousseeuw","year":"1987","unstructured":"Rousseeuw, P.J.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53\u201365 (1987). https:\/\/doi.org\/10.1016\/0377-0427(87)90125-7","journal-title":"J. Comput. Appl. Math."},{"key":"4205_CR43","unstructured":"Tabak, J.: Geometry: The Language of Space and Form. Facts on File Math Library. Infobase Publishing (2014). https:\/\/books.google.ca\/books?id=r0HuPiexnYwC"},{"issue":"1","key":"4205_CR44","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/03610927408827101","volume":"3","author":"T Cali\u0144ski","year":"1974","unstructured":"Cali\u0144ski, T., Harabasz, J.: A dendrite method for cluster analysis. Commun. Stat. Theory Methods 3(1), 1\u201327 (1974)","journal-title":"Commun. Stat. Theory Methods"},{"key":"4205_CR45","doi-asserted-by":"crossref","unstructured":"Karo, I.M.K., Maulana Adhinugraha, K., Huda, A.F.: A cluster validity for spatial clustering based on Davies Bouldin index and polygon dissimilarity function. In: 2017 Second International Conference on Informatics and Computing (ICIC), pp. 1\u20136. IEEE (2017)","DOI":"10.1109\/IAC.2017.8280572"},{"key":"4205_CR46","doi-asserted-by":"crossref","unstructured":"Kotas, C., Naughton, T., Imam, N.: A comparison of Amazon web services and Microsoft Azure cloud platforms for high performance computing. In: 2018 IEEE International Conference on Consumer Electronics (ICCE), pp. 1\u20134. IEEE (2018)","DOI":"10.1109\/ICCE.2018.8326349"},{"key":"4205_CR47","doi-asserted-by":"publisher","unstructured":"Cortez, E., Bonde, A., Muzio, A., et\u00a0al.: Resource central: understanding and predicting workloads for improved resource management in large cloud platforms. In: Proceedings of the 26th Symposium on Operating Systems Principles, pp. 153\u2013167. ACM (2017). https:\/\/doi.org\/10.1145\/3132747.3132772","DOI":"10.1145\/3132747.3132772"},{"key":"4205_CR48","unstructured":"Ali, M.: PyCaret: an open source, low-code machine learning library in Python. PyCaret version 1.0. https:\/\/www.pycaret.org (2020)"},{"key":"4205_CR49","doi-asserted-by":"crossref","unstructured":"Sculley, D.: Web-scale k-means clustering. In: Proceedings of the 19th International Conference on World Wide Web, pp. 1177\u20131178 (2010)","DOI":"10.1145\/1772690.1772862"},{"issue":"6","key":"4205_CR50","doi-asserted-by":"publisher","first-page":"e1219","DOI":"10.1002\/widm.1219","volume":"7","author":"F Murtagh","year":"2017","unstructured":"Murtagh, F., Contreras, P.: Algorithms for hierarchical clustering: an overview, II. WIREs Data Min. Knowl. Discov. 7(6), e1219 (2017). https:\/\/doi.org\/10.1002\/widm.1219","journal-title":"WIREs Data Min. Knowl. Discov."},{"issue":"5","key":"4205_CR51","doi-asserted-by":"publisher","first-page":"603","DOI":"10.1109\/34.1000236","volume":"24","author":"D Comaniciu","year":"2002","unstructured":"Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 24(5), 603\u2013619 (2002). https:\/\/doi.org\/10.1109\/34.1000236","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"21","key":"4205_CR52","doi-asserted-by":"publisher","first-page":"501","DOI":"10.21105\/joss.00501","volume":"3","author":"WD McGinnis","year":"2018","unstructured":"McGinnis, W.D., Siu, C., Andre, S., et al.: Category encoders: a scikit-learn-contrib package of transformers for encoding categorical data. J. Open Source Softw. 3(21), 501 (2018)","journal-title":"J. Open Source Softw."},{"key":"4205_CR53","doi-asserted-by":"publisher","unstructured":"Bengfort, B., Bilbro, R.: Yellowbrick: visualizing the scikit-learn model selection process. J. Open Source Softw. (2019). https:\/\/doi.org\/10.21105\/joss.01075","DOI":"10.21105\/joss.01075"}],"container-title":["Cluster Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-023-04205-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10586-023-04205-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-023-04205-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,6]],"date-time":"2024-11-06T17:49:09Z","timestamp":1730915349000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10586-023-04205-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,22]]},"references-count":53,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,7]]}},"alternative-id":["4205"],"URL":"https:\/\/doi.org\/10.1007\/s10586-023-04205-5","relation":{},"ISSN":["1386-7857","1573-7543"],"issn-type":[{"value":"1386-7857","type":"print"},{"value":"1573-7543","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,22]]},"assertion":[{"value":"8 August 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 October 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 November 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 December 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have not disclosed any competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}