{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T21:15:24Z","timestamp":1757625324638,"version":"3.44.0"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2025,7,31]],"date-time":"2025-07-31T00:00:00Z","timestamp":1753920000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,7,31]],"date-time":"2025-07-31T00:00:00Z","timestamp":1753920000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"National Natural Science Foundation of China (NSFC) project","award":["61472256","61170277"],"award-info":[{"award-number":["61472256","61170277"]}]},{"name":"Technology Development Fund Project of Shanghai of University for Science and Technology","award":["16KJFZ035","2017KJFZ033"],"award-info":[{"award-number":["16KJFZ035","2017KJFZ033"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cluster Comput"],"published-print":{"date-parts":[[2025,9]]},"DOI":"10.1007\/s10586-024-05068-0","type":"journal-article","created":{"date-parts":[[2025,7,31]],"date-time":"2025-07-31T12:27:26Z","timestamp":1753964846000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Forecasting GPU type allocation via demand feature extraction in heterogeneous clusters"],"prefix":"10.1007","volume":"28","author":[{"given":"Sheng","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yumei","family":"Shi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shiping","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Meng","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,7,31]]},"reference":[{"key":"5068_CR1","doi-asserted-by":"publisher","first-page":"643","DOI":"10.1016\/j.comcom.2020.07.002","volume":"160","author":"M Abbasi","year":"2020","unstructured":"Abbasi, M., Shokrollahi, A., Khosravi, M.R., Menon, V.G.: High-performance flow classification using hybrid clusters in software defined mobile edge computing. Comput. Commun. 160, 643\u2013660 (2020)","journal-title":"Comput. Commun."},{"key":"5068_CR2","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1016\/j.jnca.2017.01.016","volume":"82","author":"M Amiri","year":"2017","unstructured":"Amiri, M., Mohammad-Khanli, L.: Survey on prediction models of applications for resources provisioning in cloud. J. Netw. Comput. Appl. 82, 93\u2013113 (2017)","journal-title":"J. Netw. Comput. Appl."},{"key":"5068_CR3","first-page":"1","volume":"2023","author":"S Asif","year":"2023","unstructured":"Asif, S., Zhao, M., Tang, F., Zhu, Y.: An enhanced deep learning method for multi-class brain tumor classification using deep transfer learning. Multimed. Tools Appl. 2023, 1\u201328 (2023)","journal-title":"Multimed. Tools Appl."},{"issue":"4","key":"5068_CR4","doi-asserted-by":"publisher","first-page":"1681","DOI":"10.1109\/TNSM.2019.2932840","volume":"16","author":"SUR Baig","year":"2019","unstructured":"Baig, S.U.R., Iqbal, W., Berral, J.L., Erradi, A., Carrera, D.: Adaptive prediction models for data center resources utilization estimation. IEEE Trans. Netw. Serv. Manag. 16(4), 1681\u20131693 (2019)","journal-title":"IEEE Trans. Netw. Serv. Manag."},{"key":"5068_CR5","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4842-4470-8","volume-title":"Building machine learning and deep learning models on Google cloud platform","author":"E Bisong","year":"2019","unstructured":"Bisong, E., et al.: Building machine learning and deep learning models on Google cloud platform. Springer, London (2019)"},{"issue":"1","key":"5068_CR6","doi-asserted-by":"publisher","first-page":"1390","DOI":"10.1038\/s41467-021-21740-0","volume":"12","author":"SM Brooks","year":"2021","unstructured":"Brooks, S.M., Alper, H.S.: Applications, challenges, and needs for employing synthetic biology beyond the lab. Nat. Commun. 12(1), 1390 (2021)","journal-title":"Nat. Commun."},{"issue":"6","key":"5068_CR7","doi-asserted-by":"publisher","first-page":"1275","DOI":"10.1109\/TPDS.2018.2794343","volume":"29","author":"C Chen","year":"2018","unstructured":"Chen, C., Li, K., Ouyang, A., Zeng, Z., Li, K.: Gflink: an in-memory computing architecture on heterogeneous CPU\u2013GPU clusters for big data. IEEE Trans. Parallel Distrib. Syst. 29(6), 1275\u20131288 (2018)","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"issue":"6","key":"5068_CR8","doi-asserted-by":"publisher","first-page":"1890","DOI":"10.1109\/TSC.2019.2906901","volume":"14","author":"D Chen","year":"2019","unstructured":"Chen, D., Zhang, X., Wang, L., Han, Z.: Prediction of cloud resources demand based on hierarchical Pythagorean fuzzy deep neural network. IEEE Trans. Serv. Comput. 14(6), 1890\u20131901 (2019)","journal-title":"IEEE Trans. Serv. Comput."},{"key":"5068_CR9","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1016\/j.procs.2018.04.193","volume":"131","author":"J Chen","year":"2018","unstructured":"Chen, J., Wang, Y.: A resource demand prediction method based on EEMD in cloud computing. Proc. Comput. Sci. 131, 116\u2013123 (2018)","journal-title":"Proc. Comput. Sci."},{"issue":"2","key":"5068_CR10","doi-asserted-by":"publisher","first-page":"1871","DOI":"10.1109\/TCC.2022.3169157","volume":"11","author":"X Chen","year":"2023","unstructured":"Chen, X., Yang, L., Chen, Z., Min, G., Zheng, X., Rong, C.: Resource allocation with workload-time windows for cloud-based software services: a deep reinforcement learning approach. IEEE Trans. Cloud Comput. 11(2), 1871\u20131885 (2023)","journal-title":"IEEE Trans. Cloud Comput."},{"issue":"10","key":"5068_CR11","doi-asserted-by":"publisher","first-page":"8237","DOI":"10.37418\/amsj.9.10.53","volume":"9","author":"T Daniya","year":"2020","unstructured":"Daniya, T., Geetha, M., Kumar, K.S.: Classification and regression trees with gini index. Adv. Math. Sci. J. 9(10), 8237\u20138247 (2020)","journal-title":"Adv. Math. Sci. J."},{"key":"5068_CR12","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1016\/j.neucom.2023.01.061","volume":"527","author":"T Das","year":"2023","unstructured":"Das, T., Gohain, L., Kakoty, N.M., Malarvili, M., Widiyanti, P., Kumar, G.: Hierarchical approach for fusion of electroencephalography and electromyography for predicting finger movements and kinematics using deep learning. Neurocomputing 527, 184\u2013195 (2023)","journal-title":"Neurocomputing"},{"key":"5068_CR13","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1016\/j.comcom.2022.11.018","volume":"198","author":"J Dogani","year":"2023","unstructured":"Dogani, J., Khunjush, F., Seydali, M.: Host load prediction in cloud computing with discrete wavelet transformation (DWT) and bidirectional gated recurrent unit (BIGRU) network. Comput. Commun. 198, 157\u2013174 (2023)","journal-title":"Comput. Commun."},{"issue":"1","key":"5068_CR14","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1007\/s10489-022-03435-1","volume":"53","author":"R Haffar","year":"2023","unstructured":"Haffar, R., Sanchez, D., Domingo-Ferrer, J.: Explaining predictions and attacks in federated learning via random forests. Appl. Intell. 53(1), 169\u2013185 (2023)","journal-title":"Appl. Intell."},{"key":"5068_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2022.107981","volume":"100","author":"T Han","year":"2022","unstructured":"Han, T., Ma, T., Fang, Z., Zhang, Y., Han, C.: A BIM-IOT and intelligent compaction integrated framework for advanced road compaction quality monitoring and management. Comput. Electr. Eng. 100, 107981 (2022)","journal-title":"Comput. Electr. Eng."},{"key":"5068_CR16","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1016\/j.asoc.2018.05.012","volume":"70","author":"YL He","year":"2018","unstructured":"He, Y.L., Zhang, X.L., Ao, W., Huang, J.Z.: Determining the optimal temperature parameter for softmax function in reinforcement learning. Appl. Soft Comput. 70, 80\u201385 (2018)","journal-title":"Appl. Soft Comput."},{"key":"5068_CR17","doi-asserted-by":"publisher","first-page":"4824","DOI":"10.1007\/s10489-020-02038-y","volume":"51","author":"A Hosseinalipour","year":"2021","unstructured":"Hosseinalipour, A., Gharehchopogh, F.S., Masdari, M., Khademi, A.: A novel binary farmland fertility algorithm for feature selection in analysis of the text psychology. Appl. Intell. 51, 4824\u20134859 (2021)","journal-title":"Appl. Intell."},{"key":"5068_CR18","doi-asserted-by":"crossref","unstructured":"Hu, Q., Sun, P., Yan, S., Wen, Y., Zhang, T.: Characterization and prediction of deep learning workloads in large-scale GPU datacenters. In: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, pp. 1\u201315 (2021)","DOI":"10.1145\/3458817.3476223"},{"key":"5068_CR19","unstructured":"Jiang, Y., Zhu, Y., Lan, C., Yi, B., Cui, Y., Guo, C.: A unified architecture for accelerating distributed DNN training in heterogeneous GPU\u2013CPU clusters. In: 14th USENIX Symposium on Operating Systems Design and Implementation (OSDI 20), pp. 463\u2013479 (2020)"},{"key":"5068_CR20","doi-asserted-by":"crossref","unstructured":"Jokhio, F., Ashraf, A., Lafond, S., Porres, I., Lilius, J.: Prediction-based dynamic resource allocation for video transcoding in cloud computing. In: 2013 21st Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, pp. 254\u2013261 (2013)","DOI":"10.1109\/PDP.2013.44"},{"key":"5068_CR21","doi-asserted-by":"publisher","first-page":"609","DOI":"10.1016\/j.ins.2021.11.036","volume":"585","author":"DSK Karunasingha","year":"2022","unstructured":"Karunasingha, D.S.K.: Root mean square error or mean absolute error? Use their ratio as well. Inf. Sci. 585, 609\u2013629 (2022)","journal-title":"Inf. Sci."},{"issue":"9","key":"5068_CR22","doi-asserted-by":"publisher","first-page":"25769","DOI":"10.1007\/s11042-023-16488-2","volume":"83","author":"Z Khodaverdian","year":"2024","unstructured":"Khodaverdian, Z., Sadr, H., Edalatpanah, S.A., Nazari, M.: An energy aware resource allocation based on combination of CNN and GRU for virtual machine selection. Multimed. Tools Appl. 83(9), 25769\u201325796 (2024)","journal-title":"Multimed. Tools Appl."},{"key":"5068_CR23","first-page":"1","volume":"2021","author":"AK Kulkarni","year":"2021","unstructured":"Kulkarni, A.K., Annappa, B.: GPU-aware resource management in heterogeneous cloud data centers. J. Supercomput. 2021, 1\u201328 (2021)","journal-title":"J. Supercomput."},{"key":"5068_CR24","doi-asserted-by":"crossref","unstructured":"Li, B., Arora, R., Samsi, S., Patel, T., Arcand, W., Bestor, D., Byun, C., Roy, RB., Bergeron, B., Holodnak, J., Houle, M., Hubbell, M., Jones, M., Kepner, J., Klein, A., Michaleas, P., McDonald, J., Milechin, L., Mullen, J., Prout, A., Price, B., Reuther, A., Rosa, A., Weiss, M., Yee, C., Edelman, D., Vanterpool, A., Cheng, A., Gadepally, V., Tiwari, D.: Ai-enabling workloads on large-scale GPU-accelerated system: characterization, opportunities, and implications. In: 2022 IEEE International Symposium on High-Performance Computer Architecture (HPCA), pp. 1224\u20131237 (2022)","DOI":"10.1109\/HPCA53966.2022.00093"},{"issue":"1","key":"5068_CR25","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1038\/s41377-022-00849-x","volume":"11","author":"J Li","year":"2022","unstructured":"Li, J., Hung, Y.C., Kulce, O., Mengu, D., Ozcan, A.: Polarization multiplexed diffractive computing: all-optical implementation of a group of linear transformations through a polarization-encoded diffractive network. Light Sci. Appl. 11(1), 153 (2022)","journal-title":"Light Sci. Appl."},{"key":"5068_CR26","first-page":"1","volume":"2023","author":"J Lou","year":"2023","unstructured":"Lou, J., Sun, Y., Zhang, J., Cao, H., Zhang, Y., Sun, N.: ARKGPU: enabling applications\u2019 high-goodput co-location execution on multitasking GPUS. CCF Trans. High Perform. Comput. 2023, 1\u201318 (2023)","journal-title":"CCF Trans. High Perform. Comput."},{"issue":"4","key":"5068_CR27","doi-asserted-by":"publisher","first-page":"327","DOI":"10.1016\/j.ajoms.2022.12.010","volume":"35","author":"N Maruta","year":"2023","unstructured":"Maruta, N., Ki, Morita, Harazono, Y., Anzai, E., Akaike, Y., Yamazaki, K., Tonouchi, E., Yoda, T.: Automatic machine learning-based classification of mandibular third molar impaction status. J. Oral Maxillofac. Surg. Med. Pathol. 35(4), 327\u2013334 (2023)","journal-title":"J. Oral Maxillofac. Surg. Med. Pathol."},{"issue":"4","key":"5068_CR28","doi-asserted-by":"publisher","first-page":"2399","DOI":"10.1007\/s10586-019-03010-3","volume":"23","author":"M Masdari","year":"2020","unstructured":"Masdari, M., Khoshnevis, A.: A survey and classification of the workload forecasting methods in cloud computing. Clust. Comput. 23(4), 2399\u20132424 (2020)","journal-title":"Clust. Comput."},{"key":"5068_CR29","doi-asserted-by":"crossref","unstructured":"Qi, W., Yao, J., Li, J., Wu, W.: Performer: a resource demand forecasting method for data centers. In: International Conference on Green, Pervasive, and Cloud Computing, Springer, pp. 204\u2013214 (2022)","DOI":"10.1007\/978-3-031-26118-3_16"},{"key":"5068_CR30","doi-asserted-by":"publisher","first-page":"472","DOI":"10.1016\/j.neunet.2023.03.014","volume":"162","author":"AM Roy","year":"2023","unstructured":"Roy, A.M., Bose, R., Sundararaghavan, V., Arr\u00f3yave, R.: Deep learning-accelerated computational framework based on physics informed neural network for the solution of linear elasticity. Neural Netw. 162, 472\u2013489 (2023)","journal-title":"Neural Netw."},{"key":"5068_CR31","doi-asserted-by":"crossref","unstructured":"Shu, W., Zeng, F., Ling, Z., Liu, J., Lu, T., Chen, G.: Resource demand prediction of cloud workloads using an attention-based GRU model. In: 2021 17th International Conference on Mobility, pp. 428\u2013437. Sensing and Networking (MSN), IEEE (2021)","DOI":"10.1109\/MSN53354.2021.00071"},{"issue":"4","key":"5068_CR32","doi-asserted-by":"publisher","first-page":"2822","DOI":"10.1109\/COMST.2016.2558203","volume":"18","author":"X Sun","year":"2016","unstructured":"Sun, X., Ansari, N., Wang, R.: Optimizing resource utilization of a data center. IEEE Commun. Surv. Tutor. 18(4), 2822\u20132846 (2016)","journal-title":"IEEE Commun. Surv. Tutor."},{"issue":"12","key":"5068_CR33","doi-asserted-by":"publisher","first-page":"13675","DOI":"10.1007\/s10489-022-03175-2","volume":"52","author":"T Swathi","year":"2022","unstructured":"Swathi, T., Kasiviswanath, N., Rao, A.A.: An optimal deep learning-based LSTM for stock price prediction using Twitter sentiment analysis. Appl. Intell. 52(12), 13675\u201313688 (2022)","journal-title":"Appl. Intell."},{"issue":"6","key":"5068_CR34","first-page":"1344","volume":"71","author":"Z Tang","year":"2021","unstructured":"Tang, Z., Du, L., Zhang, X., Yang, L., Li, K.: AEML: an acceleration engine for multi-GPU load-balancing in distributed heterogeneous environment. IEEE Trans. Comput. 71(6), 1344\u20131357 (2021)","journal-title":"IEEE Trans. Comput."},{"key":"5068_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.jss.2021.111124","volume":"184","author":"S Tuli","year":"2022","unstructured":"Tuli, S., Gill, S.S., Xu, M., Garraghan, P., Bahsoon, R., Dustdar, S., Sakellariou, R., Rana, O., Buyya, R., Casale, G., et al.: Hunter: AI based holistic resource management for sustainable cloud computing. J. Syst. Softw. 184, 111124 (2022)","journal-title":"J. Syst. Softw."},{"key":"5068_CR36","doi-asserted-by":"publisher","first-page":"849","DOI":"10.1016\/j.future.2017.09.020","volume":"79","author":"B Varghese","year":"2018","unstructured":"Varghese, B., Buyya, R.: Next generation cloud computing: new trends and research directions. Futur. Gener. Comput. Syst. 79, 849\u2013861 (2018)","journal-title":"Futur. Gener. Comput. Syst."},{"key":"5068_CR37","doi-asserted-by":"crossref","unstructured":"Wang, M., Meng, C., Long, G., Wu, C., Yang, J., Lin, W., Jia, Y.: Characterizing deep learning training workloads on ALIBABA-PAI. In: 2019 IEEE International Symposium on Workload Characterization (IISWC). IEEE, pp. 189\u2013202 (2019)","DOI":"10.1109\/IISWC47752.2019.9042047"},{"issue":"2","key":"5068_CR38","first-page":"1769","volume":"44","author":"S Wang","year":"2023","unstructured":"Wang, S., Shi, Y., Hu, C., Yu, C., Chen, S.: Prediction poverty levels of needy college students using RF-PCA model. J. Intell. Fuzzy Syst. 44(2), 1769\u20131779 (2023)","journal-title":"J. Intell. Fuzzy Syst."},{"issue":"4","key":"5068_CR39","doi-asserted-by":"publisher","first-page":"1041","DOI":"10.1109\/TC.2022.3191733","volume":"72","author":"X Wang","year":"2023","unstructured":"Wang, X., Cao, J., Buyya, R.: Adaptive cloud bundle provisioning and multi-workflow scheduling via coalition reinforcement learning. IEEE Trans. Comput. 72(4), 1041\u20131054 (2023)","journal-title":"IEEE Trans. Comput."},{"key":"5068_CR40","first-page":"1","volume":"2019","author":"Z Wang","year":"2019","unstructured":"Wang, Z., Liu, K., Li, J., Zhu, Y., Zhang, Y.: Various frameworks and libraries of machine learning and deep learning: a survey. Arch. Comput. Methods Eng. 2019, 1\u201324 (2019)","journal-title":"Arch. Comput. Methods Eng."},{"key":"5068_CR41","unstructured":"Weng, Q., Xiao, W., Yu, Y., Wang, W., Wang, C., He, J., Li, Y., Zhang, L., Lin, W., Ding, Y.: Mlaas in the wild: workload analysis and scheduling in large-scale heterogeneous GPU clusters. In: 19th USENIX Symposium on Networked Systems Design and Implementation (NSDI 22), pp. 945\u2013960 (2022)"},{"issue":"4","key":"5068_CR42","doi-asserted-by":"publisher","first-page":"944","DOI":"10.1109\/TSC.2018.2804916","volume":"14","author":"B Xia","year":"2018","unstructured":"Xia, B., Li, T., Zhou, Q., Li, Q., Zhang, H.: An effective classification-based framework for predicting cloud capacity demand in cloud services. IEEE Trans. Serv. Comput. 14(4), 944\u2013956 (2018)","journal-title":"IEEE Trans. Serv. Comput."},{"issue":"6","key":"5068_CR43","doi-asserted-by":"publisher","first-page":"1107","DOI":"10.1109\/TPDS.2012.283","volume":"24","author":"Z Xiao","year":"2013","unstructured":"Xiao, Z., Song, W., Chen, Q.: Dynamic resource allocation using virtual machines for cloud computing environment. IEEE Trans. Parallel Distrib. Syst. 24(6), 1107\u20131117 (2013)","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"5068_CR44","first-page":"1","volume":"2024","author":"Z Yang","year":"2024","unstructured":"Yang, Z., Wang, X., Li, R., Liu, Y.: HMM-CPM: a cloud instance resource prediction method tracing the workload trends via hidden Markov model. Clust. Comput. 2024, 1\u201316 (2024)","journal-title":"Clust. Comput."},{"key":"5068_CR45","doi-asserted-by":"crossref","unstructured":"Zhang, Z.: Improved adam optimizer for deep neural networks. In: 2018 IEEE\/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1\u20132 (2018)","DOI":"10.1109\/IWQoS.2018.8624183"}],"container-title":["Cluster Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-024-05068-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10586-024-05068-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-024-05068-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T17:42:14Z","timestamp":1757439734000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10586-024-05068-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,31]]},"references-count":45,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2025,9]]}},"alternative-id":["5068"],"URL":"https:\/\/doi.org\/10.1007\/s10586-024-05068-0","relation":{},"ISSN":["1386-7857","1573-7543"],"issn-type":[{"type":"print","value":"1386-7857"},{"type":"electronic","value":"1573-7543"}],"subject":[],"published":{"date-parts":[[2025,7,31]]},"assertion":[{"value":"15 June 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 December 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 December 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 July 2025","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 declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"418"}}