{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T17:59:26Z","timestamp":1772042366915,"version":"3.50.1"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2025,6,16]],"date-time":"2025-06-16T00:00:00Z","timestamp":1750032000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,6,16]],"date-time":"2025-06-16T00:00:00Z","timestamp":1750032000000},"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":[[2025,9]]},"DOI":"10.1007\/s10586-025-05435-5","type":"journal-article","created":{"date-parts":[[2025,6,16]],"date-time":"2025-06-16T12:39:49Z","timestamp":1750077589000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Faddeer: a deep multi-agent reinforcement learning-based scheduling algorithm for aperiodic tasks in heterogeneous fog computing networks"],"prefix":"10.1007","volume":"28","author":[{"given":"Ganesan","family":"Nagabushnam","sequence":"first","affiliation":[]},{"given":"Kyong Hoon","family":"Kim","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,6,16]]},"reference":[{"issue":"7","key":"5435_CR1","doi-asserted-by":"publisher","first-page":"1645","DOI":"10.1016\/j.future.2013.01.010","volume":"29","author":"J Gubbi","year":"2013","unstructured":"Gubbi, J., Buyya, R., Marusic, S., Palaniswami, M.: Internet of things (iot): a vision, architectural elements, and future directions. Fut. Gen. Comput. Syst. 29(7), 1645\u20131660 (2013)","journal-title":"Fut. Gen. Comput. Syst."},{"key":"5435_CR2","doi-asserted-by":"crossref","unstructured":"Mahmud, R., Kotagiri, R., Buyya, R.: Fog computing: A taxonomy, survey and future directions. Internet of Everything: Algorithms, Methodologies, Technologies and Perspectives, 103\u2013130 (2018)","DOI":"10.1007\/978-981-10-5861-5_5"},{"key":"5435_CR3","doi-asserted-by":"publisher","first-page":"190","DOI":"10.1016\/j.jpdc.2018.03.004","volume":"132","author":"R Mahmud","year":"2019","unstructured":"Mahmud, R., Srirama, S.N., Ramamohanarao, K., Buyya, R.: Quality of experience (qoe)-aware placement of applications in fog computing environments. J. Paral. Distrib. Comput. 132, 190\u2013203 (2019)","journal-title":"J. Paral. Distrib. Comput."},{"key":"5435_CR4","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1007\/BF02341920","volume":"1","author":"B Sprunt","year":"1989","unstructured":"Sprunt, B., Sha, L., Lehoczky, J.: Aperiodic task scheduling for hard-real-time systems. Real Time Syst. 1, 27\u201360 (1989)","journal-title":"Real Time Syst."},{"key":"5435_CR5","doi-asserted-by":"publisher","first-page":"371","DOI":"10.1002\/9781119551713.ch14","volume":"132","author":"C-G Wu","year":"2020","unstructured":"Wu, C.-G., Wang, L.: An estimation of distribution algorithm to optimize the utility of task scheduling under fog computing systems. Fog Comput. Theor. Pract. 132, 371\u2013384 (2020)","journal-title":"Fog Comput. Theor. Pract."},{"key":"5435_CR6","doi-asserted-by":"crossref","unstructured":"Kato, S., Yamasaki, N.: Scheduling aperiodic tasks using total bandwidth server on multiprocessors. In: 2008 IEEE\/IFIP International Conference on Embedded and Ubiquitous Computing, vol. 1, pp. 82\u201389 (2008). IEEE","DOI":"10.1109\/EUC.2008.28"},{"key":"5435_CR7","doi-asserted-by":"publisher","first-page":"102708","DOI":"10.1016\/j.sysarc.2022.102708","volume":"131","author":"D Ramegowda","year":"2022","unstructured":"Ramegowda, D., Lin, M.: Energy efficient mixed task handling on real-time embedded systems using freertos. J. Syst. Arch. 131, 102708 (2022)","journal-title":"J. Syst. Arch."},{"key":"5435_CR8","doi-asserted-by":"crossref","unstructured":"Nascimento, F.M.S., Lima, G.: A flexible framework to schedule soft aperiodic tasks in hard real-time systems. In: 2019 IX Brazilian Symposium on Computing Systems Engineering (SBESC), pp. 1\u20138 (2019). IEEE","DOI":"10.1109\/SBESC49506.2019.9046046"},{"key":"5435_CR9","doi-asserted-by":"crossref","unstructured":"Fox, G., Glazier, J.A., Kadupitiya, J., Jadhao, V., Kim, M., Qiu, J., Sluka, J.P., Somogyi, E., Marathe, M., Adiga, A.: Learning everywhere: Pervasive machine learning for effective high-performance computation. In: 2019 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), pp. 422\u2013429 (2019). IEEE","DOI":"10.1109\/IPDPSW.2019.00081"},{"key":"5435_CR10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10462-021-10118-9","volume":"10","author":"A Wong","year":"2022","unstructured":"Wong, A., B\u00e4ck, T., Kononova, A.V., Plaat, A.: Deep multiagent reinforcement learning: challenges and directions. Artif. Intell. Rev. 10, 1\u201334 (2022)","journal-title":"Artif. Intell. Rev."},{"key":"5435_CR11","unstructured":"Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928\u20131937 (2016). PMLR"},{"key":"5435_CR12","first-page":"17","volume":"30","author":"R Lowe","year":"2017","unstructured":"Lowe, R., Wu, Y.I., Tamar, A., Harb, J., Pieter Abbeel, O., Mordatch, I.: Multi-agent actor-critic for mixed cooperative-competitive environments. Adv. Neural Inf. Process. Syst. 30, 17 (2017)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"5435_CR13","doi-asserted-by":"publisher","first-page":"132306","DOI":"10.1016\/j.physd.2019.132306","volume":"404","author":"A Sherstinsky","year":"2020","unstructured":"Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Phys. D Nonlinear Phen. 404, 132306 (2020)","journal-title":"Phys. D Nonlinear Phen."},{"issue":"1","key":"5435_CR14","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1109\/TPDS.2021.3087349","volume":"33","author":"S Tuli","year":"2021","unstructured":"Tuli, S., Poojara, S.R., Srirama, S.N., Casale, G., Jennings, N.R.: Cosco: Container orchestration using co-simulation and gradient based optimization for fog computing environments. IEEE Trans. Parall. Distrib Syst 33(1), 101\u2013116 (2021)","journal-title":"IEEE Trans. Parall. Distrib Syst"},{"key":"5435_CR15","doi-asserted-by":"publisher","first-page":"387","DOI":"10.1007\/s00500-016-2474-6","volume":"22","author":"D Wang","year":"2018","unstructured":"Wang, D., Tan, D., Liu, L.: Particle swarm optimization algorithm: an overview. Soft Comput. 22, 387\u2013408 (2018)","journal-title":"Soft Comput."},{"key":"5435_CR16","doi-asserted-by":"crossref","unstructured":"Bu\u015foniu, L., Babu\u0161ka, R., Schutter, De., B..: In: Multi-agent Reinforcement Learning: An Overview, pp. 183\u2013221. Springer, Berlin (2010)","DOI":"10.1007\/978-3-642-14435-6_7"},{"key":"5435_CR17","unstructured":"Noureddine, D.B., Gharbi, A., Ahmed, S.B.: Multi-agent deep reinforcement learning for task allocation in dynamic environment. In: ICSOFT, pp. 17\u201326 (2017)"},{"key":"5435_CR18","doi-asserted-by":"crossref","unstructured":"Fellir, F., El\u00a0Attar, A., Nafil, K., Chung, L.: A multi-agent based model for task scheduling in cloud-fog computing platform. In: 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT), pp. 377\u2013382 (2020). IEEE","DOI":"10.1109\/ICIoT48696.2020.9089625"},{"key":"5435_CR19","doi-asserted-by":"crossref","unstructured":"Shyalika, C., Silva, T.: Reinforcement learning based an integrated approach for uncertainty scheduling in adaptive environments using marl. In: 2021 6th International Conference on Inventive Computation Technologies (ICICT), pp. 1204\u20131211 (2021). IEEE","DOI":"10.1109\/ICICT50816.2021.9358727"},{"key":"5435_CR20","doi-asserted-by":"publisher","first-page":"487","DOI":"10.1016\/j.jmsy.2021.07.015","volume":"60","author":"T Yu","year":"2021","unstructured":"Yu, T., Huang, J., Chang, Q.: Optimizing task scheduling in human-robot collaboration with deep multi-agent reinforcement learning. J. Manuf. Syst. 60, 487\u2013499 (2021)","journal-title":"J. Manuf. Syst."},{"issue":"11","key":"5435_CR21","doi-asserted-by":"publisher","first-page":"4099","DOI":"10.3390\/s22114099","volume":"22","author":"J Rosenberger","year":"2022","unstructured":"Rosenberger, J., Urlaub, M., Rauterberg, F., Lutz, T., Selig, A., B\u00fchren, M., Schramm, D.: Deep reinforcement learning multi-agent system for resource allocation in industrial internet of things. Sensors 22(11), 4099 (2022)","journal-title":"Sensors"},{"key":"5435_CR22","doi-asserted-by":"crossref","unstructured":"Chauhan, A., Singh, S., Negi, S., Verma, S.K.: Algorithm for deadline based task scheduling in heterogeneous grid environment. In: 2016 2nd International Conference on Next Generation Computing Technologies (NGCT), pp. 219\u2013222 (2016). IEEE","DOI":"10.1109\/NGCT.2016.7877418"},{"issue":"1","key":"5435_CR23","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1016\/j.future.2008.07.001","volume":"25","author":"VV Korkhov","year":"2009","unstructured":"Korkhov, V.V., Moscicki, J.T., Krzhizhanovskaya, V.V.: Dynamic workload balancing of parallel applications with user-level scheduling on the grid. Fut. Gen. Comput. Syst. 25(1), 28\u201334 (2009)","journal-title":"Fut. Gen. Comput. Syst."},{"key":"5435_CR24","volume-title":"Predictable scheduling algorithms and applications","author":"GC Buttazzo","year":"2011","unstructured":"Buttazzo, G.C.: Predictable scheduling algorithms and applications. Springer, Cham (2011)"},{"key":"5435_CR25","doi-asserted-by":"publisher","first-page":"27859","DOI":"10.1109\/ACCESS.2019.2901411","volume":"7","author":"AA Khan","year":"2019","unstructured":"Khan, A.A., Ali, A., Zakarya, M., Khan, R., Khan, M., Rahman, I.U., Abd Rahman, M.A.: A migration aware scheduling technique for real-time aperiodic tasks over multiprocessor systems. IEEE Access 7, 27859\u201327873 (2019)","journal-title":"IEEE Access"},{"key":"5435_CR26","doi-asserted-by":"crossref","unstructured":"Goubaa, A., Kahlgui, M., Georg, F., Li, Z.: Efficient scheduling of periodic, aperiodic, and sporadic real-time tasks with deadline constraints. In: Software Technologies: 15th International Conference, ICSOFT 2020, Online Event, July 7\u20139, 2020, Revised Selected Papers 15, pp. 25\u201343 (2021). Springer","DOI":"10.1007\/978-3-030-83007-6_2"},{"issue":"2","key":"5435_CR27","doi-asserted-by":"publisher","first-page":"282","DOI":"10.1016\/j.jksuci.2018.10.009","volume":"34","author":"SC Nayak","year":"2022","unstructured":"Nayak, S.C., Parida, S., Tripathy, C., Pattnaik, P.K.: An enhanced deadline constraint based task scheduling mechanism for cloud environment. J. King Saud Univ. Comput. Inf. Sci. 34(2), 282\u2013294 (2022)","journal-title":"J. King Saud Univ. Comput. Inf. Sci."},{"issue":"4","key":"5435_CR28","doi-asserted-by":"publisher","first-page":"626","DOI":"10.1109\/TC.2020.2993561","volume":"70","author":"K Gai","year":"2020","unstructured":"Gai, K., Qin, X., Zhu, L.: An energy-aware high performance task allocation strategy in heterogeneous fog computing environments. IEEE Trans. Comput. 70(4), 626\u2013639 (2020)","journal-title":"IEEE Trans. Comput."},{"key":"5435_CR29","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2024.3459020","author":"Z Chen","year":"2024","unstructured":"Chen, Z., Zhang, J., Min, G., Ning, Z., Li, J.: Traffic-aware lightweight hierarchical offloading towards adaptive slicing-enabled sagin. IEEE J. Sel. Areas Commun. (2024). https:\/\/doi.org\/10.1109\/JSAC.2024.3459020","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"5435_CR30","doi-asserted-by":"crossref","unstructured":"Hicham, B., Said, B., Touhafi, A., Ezzati, A.: Deadline and energy aware task scheduling in cloud computing. In: 2018 4th International Conference on Cloud Computing Technologies and Applications (Cloudtech), pp. 1\u20138 (2018). IEEE","DOI":"10.1109\/CloudTech.2018.8713338"},{"issue":"7","key":"5435_CR31","doi-asserted-by":"publisher","first-page":"1100","DOI":"10.3390\/math10071100","volume":"10","author":"I Attiya","year":"2022","unstructured":"Attiya, I., Abualigah, L., Elsadek, D., Chelloug, S.A., Abd Elaziz, M.: An intelligent chimp optimizer for scheduling of iot application tasks in fog computing. Mathematics 10(7), 1100 (2022)","journal-title":"Mathematics"},{"key":"5435_CR32","doi-asserted-by":"publisher","first-page":"539","DOI":"10.1016\/j.future.2019.09.039","volume":"111","author":"RO Aburukba","year":"2020","unstructured":"Aburukba, R.O., AliKarrar, M., Landolsi, T., El-Fakih, K.: Scheduling internet of things requests to minimize latency in hybrid fog-cloud computing. Fut. Gen. Comput. Syst. 111, 539\u2013551 (2020)","journal-title":"Fut. Gen. Comput. Syst."},{"issue":"4","key":"5435_CR33","doi-asserted-by":"publisher","first-page":"1162","DOI":"10.3390\/pr11041162","volume":"11","author":"Q Liu","year":"2023","unstructured":"Liu, Q., Kosarirad, H., Meisami, S., Alnowibet, K.A., Hoshyar, A.N.: An optimal scheduling method in iot-fog-cloud network using combination of aquila optimizer and African vultures optimization. Processes 11(4), 1162 (2023)","journal-title":"Processes"},{"key":"5435_CR34","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1016\/j.procs.2021.07.012","volume":"191","author":"S Swarup","year":"2021","unstructured":"Swarup, S., Shakshuki, E.M., Yasar, A.: Energy efficient task scheduling in fog environment using deep reinforcement learning approach. Proced. Comput. Sci. 191, 65\u201375 (2021)","journal-title":"Proced. Comput. Sci."},{"key":"5435_CR35","doi-asserted-by":"crossref","unstructured":"Nagabushnam, G., Choi, Y., Kim, K.H.: Fodas: A novel reinforcement learning approach for efficient task scheduling in fog computing network. In: 2024 9th International Conference on Fog and Mobile Edge Computing (FMEC), pp. 46\u201353 (2024). IEEE","DOI":"10.1109\/FMEC62297.2024.10710250"},{"key":"5435_CR36","doi-asserted-by":"crossref","unstructured":"Mattia, G.P., Beraldi, R.: Leveraging reinforcement learning for online scheduling of real-time tasks in the edge\/fog-to-cloud computing continuum. In: 2021 IEEE 20th International Symposium on Network Computing and Applications (NCA), pp. 1\u20139 (2021). IEEE","DOI":"10.1109\/NCA53618.2021.9685413"},{"key":"5435_CR37","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2024.3396511","author":"Z Chen","year":"2024","unstructured":"Chen, Z., Xiong, B., Chen, X., Min, G., Li, J.: Joint computation offloading and resource allocation in multi-edge smart communities with personalized federated deep reinforcement learning. IEEE Trans. Mobile Comput. (2024). https:\/\/doi.org\/10.1109\/TMC.2024.3396511","journal-title":"IEEE Trans. Mobile Comput."},{"key":"5435_CR38","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2025.3540407","author":"Z Chen","year":"2025","unstructured":"Chen, Z., Huang, S., Min, G., Ning, Z., Li, J., Zhang, Y.: Mobility-aware seamless service migration and resource allocation in multi-edge iov systems. IEEE Trans. Mobile Comput. (2025). https:\/\/doi.org\/10.1109\/TMC.2025.3540407","journal-title":"IEEE Trans. Mobile Comput."},{"key":"5435_CR39","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1016\/j.future.2023.10.002","volume":"151","author":"H Hou","year":"2024","unstructured":"Hou, H., Jawaddi, S.N.A., Ismail, A.: Energy efficient task scheduling based on deep reinforcement learning in cloud environment: a specialized review. Fut. Gen. Comput. Syst. 151, 214\u2013231 (2024)","journal-title":"Fut. Gen. Comput. Syst."},{"issue":"4","key":"5435_CR40","first-page":"375","volume":"7","author":"J Xu","year":"2020","unstructured":"Xu, J., Sun, X., Zhang, R., Liang, H., Duan, Q.: Fog-cloud task scheduling of energy consumption optimisation with deadline consideration. Int. J. Internet Manuf. Serv. 7(4), 375\u2013392 (2020)","journal-title":"Int. J. Internet Manuf. Serv."},{"key":"5435_CR41","first-page":"100355","volume":"24","author":"S Sharma","year":"2019","unstructured":"Sharma, S., Saini, H.: A novel four-tier architecture for delay aware scheduling and load balancing in fog environment. Sustainable Comput. Inf. Syst. 24, 100355 (2019)","journal-title":"Sustainable Comput. Inf. Syst."},{"issue":"3","key":"5435_CR42","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1007\/s10586-024-04828-2","volume":"28","author":"W Shen","year":"2025","unstructured":"Shen, W., Lin, W., Wu, W., Wu, H., Li, K.: Reinforcement learning-based task scheduling for heterogeneous computing in end-edge-cloud environment. Clust. Comput. 28(3), 179 (2025)","journal-title":"Clust. Comput."},{"key":"5435_CR43","doi-asserted-by":"publisher","DOI":"10.1109\/TSC.2024.3520869","author":"H Wu","year":"2024","unstructured":"Wu, H., Lin, W., Shen, W., Wang, X., Chen, C.P., Li, K.: Prediction of heterogeneous device task runtime based on edge server-oriented deep neuro-fuzzy system. IEEE Trans. Serv. Comput. (2024). https:\/\/doi.org\/10.1109\/TSC.2024.3520869","journal-title":"IEEE Trans. Serv. Comput."},{"issue":"13","key":"5435_CR44","doi-asserted-by":"publisher","first-page":"2580","DOI":"10.3390\/electronics13132580","volume":"13","author":"A Ben Sada","year":"2024","unstructured":"Ben Sada, A., Khelloufi, A., Naouri, A., Ning, H., Aung, N., Dhelim, S.: Multi-agent deep reinforcement learning-based inference task scheduling and offloading for maximum inference accuracy under time and energy constraints. Electronics 13(13), 2580 (2024)","journal-title":"Electronics"},{"key":"5435_CR45","first-page":"342","volume":"6","author":"M Merluzzi","year":"2020","unstructured":"Merluzzi, M., Di Lorenzo, P., Barbarossa, S., Frascolla, V.: Dynamic computation offloading in multi-access edge computing via ultra-reliable and low-latency communications. IEEE Trans. Signal Inf. Process. Over Netw. 6, 342\u2013356 (2020)","journal-title":"IEEE Trans. Signal Inf. Process. Over Netw."},{"key":"5435_CR46","doi-asserted-by":"publisher","DOI":"10.1109\/TCAD.2024.3437344","author":"K Cao","year":"2024","unstructured":"Cao, K., Chen, M., Karnouskos, S., Hu, S.: Reliability-aware personalized deployment of approximate computation iot applications in serverless mobile edge computing. IEEE Trans. Comput. Aided Des. Integr. Circ. Syst. (2024). https:\/\/doi.org\/10.1109\/TCAD.2024.3437344","journal-title":"IEEE Trans. Comput. Aided Des. Integr. Circ. Syst."},{"key":"5435_CR47","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2025.3528404","author":"Z Chen","year":"2025","unstructured":"Chen, Z., Jiang, Q., Chen, L., Chen, X., Li, J., Min, G.: Mc-2pf: a multi-edge cooperative universal framework for load prediction with personalized federated deep learning. IEEE Trans. Mobile Comput. (2025). https:\/\/doi.org\/10.1109\/TMC.2025.3528404","journal-title":"IEEE Trans. Mobile Comput."},{"issue":"8","key":"5435_CR48","doi-asserted-by":"publisher","first-page":"1911","DOI":"10.1109\/TPDS.2021.3132422","volume":"33","author":"Z Chen","year":"2021","unstructured":"Chen, Z., Hu, J., Min, G., Luo, C., El-Ghazawi, T.: Adaptive and efficient resource allocation in cloud datacenters using actor-critic deep reinforcement learning. IEEE Trans. Paral. Distrib. Syst. 33(8), 1911\u20131923 (2021)","journal-title":"IEEE Trans. Paral. Distrib. Syst."},{"key":"5435_CR49","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2024.3359297","author":"M Sharma","year":"2024","unstructured":"Sharma, M., Tomar, A., Hazra, A.: Edge computing for industry 50: fundamental, applications and research challenges. IEEE Internet Things J. (2024). https:\/\/doi.org\/10.1109\/JIOT.2024.3359297","journal-title":"IEEE Internet Things J."},{"key":"5435_CR50","doi-asserted-by":"crossref","unstructured":"Sharma, M., Tomar, A., Hazra, A.: From connectivity to intelligence: The game-changing role of ai and iot in industry 5.0. IEEE Consumer Electronics Magazine (2024)","DOI":"10.1109\/MCE.2024.3470340"}],"container-title":["Cluster Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-025-05435-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10586-025-05435-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-025-05435-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,3]],"date-time":"2025-09-03T20:21:41Z","timestamp":1756930901000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10586-025-05435-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,16]]},"references-count":50,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2025,9]]}},"alternative-id":["5435"],"URL":"https:\/\/doi.org\/10.1007\/s10586-025-05435-5","relation":{},"ISSN":["1386-7857","1573-7543"],"issn-type":[{"value":"1386-7857","type":"print"},{"value":"1573-7543","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,16]]},"assertion":[{"value":"7 November 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 May 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 May 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 June 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 July 2025","order":5,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Update","order":6,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The original online version of this article was revised: The missing biography and photo of the authors Ganesan Nagabushnam and Kyong Hoon Kim have been included.","order":7,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no relevant financial or non-financial interests to disclose.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"All authors provided written consent for the publication of this paper.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval and Consent to Participate"}},{"value":"All authors provided written consent for the publication of this paper.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for Publication"}}],"article-number":"385"}}