{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T06:55:51Z","timestamp":1781765751919,"version":"3.54.5"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,5,23]],"date-time":"2026-05-23T00:00:00Z","timestamp":1779494400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,5,23]],"date-time":"2026-05-23T00:00:00Z","timestamp":1779494400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100005950","name":"Hong Kong University of Science and Technology","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100005950","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Auton Agent Multi-Agent Syst"],"published-print":{"date-parts":[[2026,6]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Vehicle Routing Problems (VRPs) involve multi-agent route optimization, with the objective of targeting optimal routes for a fleet of vehicles to serve a set of customers. Existing neural solvers based on the divide-and-conquer approach for VRPs in general, and capacitated VRP (CVRP) in particular, integrate the global partition of an instance with the local construction for each resulting subinstance to enhance generalization. However, during the global partition phase, misclusterings within subgraphs have a tendency to progressively compound throughout the multi-step decoding process of the learning-based partition policy. This suboptimal behavior of the partition policy, in turn, may lead to a dramatic deterioration in the performance of the overall decomposition-based system, despite using optimal local constructions. To address these challenges, we propose a versatile Hierarchical Learning-based Graph Partition (HLGP) framework, which is tailored to benefit the partition of CVRP instances by synergistically integrating global and local partition policies. Specifically, the global partition policy is tasked with creating a coarse multi-way partition to generate a sequence of simpler two-way partition subtasks. These subtasks mark the initiation of the subsequent K local partition levels. At each local partition level, subtasks exclusive to this level are assigned to the local partition policy which benefits from the insensitive local topological features to incrementally alleviate the compounded errors. This framework is versatile in the sense that it optimizes the involved partition policies towards a unified objective, which is harmoniously compatible with both reinforcement learning (RL) and supervised learning (SL) paradigms. Additionally, we decouple the synchronized training into individual training of each component to circumvent the instability issue. Furthermore, we point out the importance of treating the subproblems encountered during the partition process as individual training instances. Extensive experiments conducted on various CVRP benchmarks demonstrate the effectiveness and generalization capabilities of the HLGP framework under both scale and distribution shifts. The source code is available at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/panyxy\/hlgp_cvrp\" ext-link-type=\"uri\">https:\/\/github.com\/panyxy\/hlgp_cvrp<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1007\/s10458-026-09755-7","type":"journal-article","created":{"date-parts":[[2026,5,23]],"date-time":"2026-05-23T07:32:00Z","timestamp":1779521520000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multi-level graph partition via hierarchical learning for large-scale vehicle routing problems"],"prefix":"10.1007","volume":"40","author":[{"given":"Yuxin","family":"Pan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruohong","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yize","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiguang","family":"Cao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fangzhen","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,23]]},"reference":[{"key":"9755_CR1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9780898718515","volume-title":"The Vehicle Routing Problem","author":"P Toth","year":"2002","unstructured":"Toth, P., & Vigo, D. (2002). The Vehicle Routing Problem. Philadelphia, PA, USA: Society for Industrial and Applied Mathematics."},{"issue":"1","key":"9755_CR2","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1016\/j.ejor.2016.02.045","volume":"254","author":"S Martin","year":"2016","unstructured":"Martin, S., Ouelhadj, D., Beullens, P., Ozcan, E., Juan, A. A., & Burke, E. K. (2016). A multi-agent based cooperative approach to scheduling and routing. European Journal of Operational Research, 254(1), 169\u2013178.","journal-title":"European Journal of Operational Research"},{"key":"9755_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2020.102861","volume":"121","author":"K Zhang","year":"2020","unstructured":"Zhang, K., He, F., Zhang, Z., Lin, X., & Li, M. (2020). Multi-vehicle routing problems with soft time windows: A multi-agent reinforcement learning approach. Transportation Research Part C: Emerging Technologies, 121, Article 102861.","journal-title":"Transportation Research Part C: Emerging Technologies"},{"issue":"12","key":"9755_CR4","doi-asserted-by":"publisher","first-page":"7804","DOI":"10.1109\/TITS.2020.3009289","volume":"22","author":"G Bono","year":"2020","unstructured":"Bono, G., Dibangoye, J. S., Simonin, O., Matignon, L., & Pereyron, F. (2020). Solving multi-agent routing problems using deep attention mechanisms. IEEE Transactions on Intelligent Transportation Systems, 22(12), 7804\u20137813.","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"issue":"9","key":"9755_CR5","doi-asserted-by":"publisher","first-page":"16410","DOI":"10.1109\/TITS.2022.3150151","volume":"23","author":"L Ren","year":"2022","unstructured":"Ren, L., Fan, X., Cui, J., Shen, Z., Lv, Y., & Xiong, G. (2022). A multi-agent reinforcement learning method with route recorders for vehicle routing in supply chain management. IEEE Transactions on Intelligent Transportation Systems, 23(9), 16410\u201316420.","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"issue":"1","key":"9755_CR6","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1016\/j.ejor.2009.10.002","volume":"204","author":"T Garaix","year":"2010","unstructured":"Garaix, T., Artigues, C., Feillet, D., & Josselin, D. (2010). Vehicle routing problems with alternative paths: An application to on-demand transportation. European Journal of Operational Research, 204(1), 62\u201375.","journal-title":"European Journal of Operational Research"},{"issue":"1","key":"9755_CR7","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1007\/s13676-014-0074-0","volume":"6","author":"D Cattaruzza","year":"2017","unstructured":"Cattaruzza, D., Absi, N., Feillet, D., & Gonz\u00e1lez-Feliu, J. (2017). Vehicle routing problems for city logistics. EURO Journal on Transportation and Logistics, 6(1), 51\u201379.","journal-title":"EURO Journal on Transportation and Logistics"},{"key":"9755_CR8","doi-asserted-by":"crossref","unstructured":"Laporte, G., & Nobert, Y. (1983). A branch and bound algorithm for the capacitated vehicle routing problem. Operations-Research-Spektrum, 5(2), 77\u201385.","DOI":"10.1007\/BF01720015"},{"key":"9755_CR9","unstructured":"Helsgaun, K. (2017). An extension of the Lin-Kernighan-Helsgaun TSP solver for constrained traveling salesman and vehicle routing problems. Roskilde: Roskilde University,\u00a012, 966\u2013980."},{"issue":"3","key":"9755_CR10","doi-asserted-by":"publisher","first-page":"611","DOI":"10.1287\/opre.1120.1048","volume":"60","author":"T Vidal","year":"2012","unstructured":"Vidal, T., Crainic, T. G., Gendreau, M., Lahrichi, N., & Rei, W. (2012). A hybrid genetic algorithm for multidepot and periodic vehicle routing problems. Operations Research, 60(3), 611\u2013624.","journal-title":"Operations Research"},{"key":"9755_CR11","unstructured":"Vinyals, O., Fortunato, M., & Jaitly, N. (2015). Pointer networks. In Advances in Neural Information Processing Systems,\u00a028, 2692\u20132700."},{"key":"9755_CR12","unstructured":"Nazari, M., Oroojlooy, A., Snyder, L., & Tak\u00e1c, M. (2018). Reinforcement learning for solving the vehicle routing problem. In Advances in Neural Information Processing Systems, 31, 9861\u20139871."},{"key":"9755_CR13","unstructured":"Kool, W., Hoof, H., & Welling, M. (2019). Attention, learn to solve routing problems! In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=ByxBFsRqYm."},{"issue":"7","key":"9755_CR14","doi-asserted-by":"publisher","first-page":"4861","DOI":"10.1109\/TII.2020.3031409","volume":"17","author":"L Xin","year":"2020","unstructured":"Xin, L., Song, W., Cao, Z., & Zhang, J. (2020). Step-wise deep learning models for solving routing problems. IEEE Transactions on Industrial Informatics, 17(7), 4861\u20134871.","journal-title":"IEEE Transactions on Industrial Informatics"},{"key":"9755_CR15","unstructured":"Kwon, Y.-D., Choo, J., Kim, B., Yoon, I., Gwon, Y., & Min, S. (2020). POMO: Policy optimization with multiple optima for reinforcement learning. In Advances in Neural Information Processing Systems,\u00a033, 21188\u201321198."},{"key":"9755_CR16","doi-asserted-by":"crossref","unstructured":"Kim, M., Park, J., & Park, J. (2022). Sym-NCO: Leveraging symmetricity for neural combinatorial optimization. In Advances in Neural Information Processing Systems,\u00a035, 1936\u20131949.","DOI":"10.52202\/068431-0141"},{"key":"9755_CR17","unstructured":"Lu, H., Zhang, X., & Yang, S. (2020). A learning-based iterative method for solving vehicle routing problems. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=BJe1334YDH."},{"key":"9755_CR18","unstructured":"Chen, X., & Tian, Y. (2019) Learning to perform local rewriting for combinatorial optimization. In Advances in Neural Information Processing Systems,\u00a032, 6281\u20136292."},{"key":"9755_CR19","unstructured":"Hottung, A., & Tierney, K. (2020). Neural large neighborhood search for the capacitated vehicle routing problem. In 24th European Conference on Artificial Intelligence"},{"key":"9755_CR20","unstructured":"Ma, Y., Li, J., Cao, Z., Song, W., Zhang, L., Chen, Z., & Tang, J. (2021). Learning to iteratively solve routing problems with dual-aspect collaborative transformer. In Advances in Neural Information Processing Systems,\u00a034, 11096\u201311107."},{"key":"9755_CR21","unstructured":"Xin, L., Song, W., Cao, Z., & Zhang, J. (2021). NeuroLKH: Combining deep learning model with Lin-Kernighan-Helsgaun heuristic for solving the traveling salesman problem. In Advances in Neural Information Processing Systems,\u00a034, 7472\u20137483."},{"key":"9755_CR22","doi-asserted-by":"crossref","unstructured":"Ma, Y., Cao, Z., & Chee, Y.M. (2023). Learning to search feasible and infeasible regions of routing problems with flexible neural k-Opt. In Advances in Neural Information Processing Systems,\u00a036, 49555\u201349578.","DOI":"10.52202\/075280-2157"},{"issue":"2","key":"9755_CR23","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1002\/net.3230110205","volume":"11","author":"ML Fisher","year":"1981","unstructured":"Fisher, M. L., & Jaikumar, R. (1981). A generalized assignment heuristic for vehicle routing. Networks, 11(2), 109\u2013124.","journal-title":"Networks"},{"key":"9755_CR24","doi-asserted-by":"crossref","unstructured":"Fu, Z.-H., Qiu, K.-B., & Zha, H. (2021). Generalize a small pre-trained model to arbitrarily large TSP instances. In Proceedings of the AAAI Conference on Artificial Intelligence,\u00a035, 7474\u20137482.","DOI":"10.1609\/aaai.v35i8.16916"},{"key":"9755_CR25","unstructured":"Kim, M., Park, J., & Kim, J. (2021). Learning collaborative policies to solve NP-hard routing problems. In Advances in Neural Information Processing Systems,\u00a034, 10418\u201310430."},{"key":"9755_CR26","unstructured":"Li, S., Yan, Z., & Wu, C. (2021). Learning to delegate for large-scale vehicle routing. In Advances in Neural Information Processing Systems,\u00a034, 26198\u201326211."},{"key":"9755_CR27","doi-asserted-by":"crossref","unstructured":"Zong, Z., Wang, H., Wang, J., Zheng, M., & Li, Y. (2022). RBG: Hierarchically solving large-scale routing problems in logistic systems via reinforcement learning. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 4648\u20134658.","DOI":"10.1145\/3534678.3539037"},{"key":"9755_CR28","unstructured":"Cheng, H., Zheng, H., Cong, Y., Jiang, W., & Pu, S. (2023). Select and optimize: Learning to solve large-scale TSP instances. In International Conference on Artificial Intelligence and Statistics, pp. 1219\u20131231. PMLR."},{"key":"9755_CR29","doi-asserted-by":"crossref","unstructured":"Pan, X., Jin, Y., Ding, Y., Feng, M., Zhao, L., Song, L., & Bian, J. (2023). H-TSP: Hierarchically solving the large-scale traveling salesman problem. In Proceedings of the AAAI Conference on Artificial Intelligence,\u00a037, 9345\u20139353.","DOI":"10.1609\/aaai.v37i8.26120"},{"key":"9755_CR30","unstructured":"Hou, Q., Yang, J., Su, Y., Wang, X., & Deng, Y. (2023). Generalize learned heuristics to solve large-scale vehicle routing problems in real-time. In The 11th International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=6ZajpxqTlQ."},{"key":"9755_CR31","doi-asserted-by":"crossref","unstructured":"Ye, H., Wang, J., Liang, H., Cao, Z., Li, Y., & Li, F. (2024). GLOP: Learning global partition and local construction for solving large-scale routing problems in real-time. In Proceedings of the AAAI Conference on Artificial Intelligence,\u00a038, 20284\u201320292.","DOI":"10.1609\/aaai.v38i18.30009"},{"key":"9755_CR32","doi-asserted-by":"crossref","unstructured":"Zheng, Z., Zhou, C., Tong, X., Yuan, M., & Wang, Z. (2024). UDC: A unified neural divide-and-conquer framework for large-scale combinatorial optimization problems. In Advances in Neural Information Processing Systems,\u00a037, 6081\u20136125.","DOI":"10.52202\/079017-0197"},{"key":"9755_CR33","unstructured":"Jiang, Y., Cao, Z., Wu, Y., & Zhang, J. (2023). Multi-view graph contrastive learning for solving vehicle routing problems. In Uncertainty in Artificial Intelligence, pp. 984\u2013994. PMLR"},{"key":"9755_CR34","unstructured":"Gao, C., Shang, H., Xue, K., Li, D., & Qian, C. (2024). Towards generalizable neural solvers for vehicle routing problems via ensemble with transferrable local policy. In The 33rd International Joint Conference on Artificial Intelligence, pp. 6914\u20136922."},{"key":"9755_CR35","unstructured":"Fang, H., Song, Z., Weng, P., & Ban, Y. (2024). INViT: A generalizable routing problem solver with invariant nested view transformer. In International Conference on Machine Learning, pp. 12973\u201312992. PMLR."},{"issue":"5","key":"9755_CR36","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3453160","volume":"54","author":"S Pateria","year":"2021","unstructured":"Pateria, S., Subagdja, B., Tan, A.-H., & Quek, C. (2021). Hierarchical reinforcement learning: A comprehensive survey. ACM Computing Surveys, 54(5), 1\u201335.","journal-title":"ACM Computing Surveys"},{"key":"9755_CR37","unstructured":"Levy, A., Platt, R., & Saenko, K. (2019). Hierarchical reinforcement learning with hindsight. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=ryzECoAcY7."},{"key":"9755_CR38","doi-asserted-by":"crossref","unstructured":"Drakulic, D., Michel, S., Mai, F., Sors, A., & Andreoli, J.-M. (2023). BQ-NCO: Bisimulation quotienting for efficient neural combinatorial optimization. In Advances in Neural Information Processing Systems,\u00a036, 77416\u201377429.","DOI":"10.52202\/075280-3385"},{"key":"9755_CR39","doi-asserted-by":"crossref","unstructured":"Luo, F., Lin, X., Liu, F., Zhang, Q., & Wang, Z. (2023). Neural combinatorial optimization with heavy decoder: Toward large scale generalization. In Advances in Neural Information Processing Systems,\u00a036, 8845\u20138864.","DOI":"10.52202\/075280-0387"},{"key":"9755_CR40","unstructured":"Luo, F., Lin, X., Wu, Y., Wang, Z., Xialiang, T., Yuan, M., & Zhang, Q. (2025). Boosting neural combinatorial optimization for large-scale vehicle routing problems. In The 13th International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=TbTJJNjumY."},{"key":"9755_CR41","unstructured":"Son, J., Kim, M., Kim, H., & Park, J. (2023). Meta-SAGE: Scale meta-learning scheduled adaptation with guided exploration for mitigating scale shift on combinatorial optimization. In International Conference on Machine Learning, pp. 32194\u201332210. PMLR."},{"key":"9755_CR42","unstructured":"Zhou, J., Wu, Y., Song, W., Cao, Z., & Zhang, J. (2023). Towards omni-generalizable neural methods for vehicle routing problems. In International Conference on Machine Learning, pp. 42769\u201342789. PMLR."},{"key":"9755_CR43","doi-asserted-by":"crossref","unstructured":"Manchanda, S., Michel, S., Drakulic, D., & Andreoli, J.-M. (2022). On the generalization of neural combinatorial optimization heuristics. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 426\u2013442. Springer","DOI":"10.1007\/978-3-031-26419-1_26"},{"key":"9755_CR44","doi-asserted-by":"crossref","unstructured":"Qiu, R., Sun, Z., & Yang, Y. (2022). DIMES: A differentiable meta solver for combinatorial optimization problems. In Advances in Neural Information Processing Systems,\u00a035, 25531\u201325546.","DOI":"10.52202\/068431-1851"},{"key":"9755_CR45","doi-asserted-by":"crossref","unstructured":"Bi, J., Ma, Y., Wang, J., Cao, Z., Chen, J., Sun, Y., & Chee, Y.M. (2022). Learning generalizable models for vehicle routing problems via knowledge distillation. In Advances in Neural Information Processing Systems,\u00a035, 31226\u201331238.","DOI":"10.52202\/068431-2264"},{"key":"9755_CR46","doi-asserted-by":"crossref","unstructured":"Jiang, Y., Cao, Z., Wu, Y., Song, W., & Zhang, J. (2023). Ensemble-based deep reinforcement learning for vehicle routing problems under distribution shift. In Advances in Neural Information Processing Systems,\u00a036, 53112\u201353125.","DOI":"10.52202\/075280-2311"},{"key":"9755_CR47","doi-asserted-by":"crossref","unstructured":"Grinsztajn, N., Furelos-Blanco, D., Surana, S., Bonnet, C., & Barrett, T. (2023). Winner takes it all: Training performant RL populations for combinatorial optimization. In Advances in Neural Information Processing Systems,\u00a036, 48485\u201348509.","DOI":"10.52202\/075280-2105"},{"key":"9755_CR48","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1023\/A:1022672621406","volume":"8","author":"RJ Williams","year":"1992","unstructured":"Williams, R. J. (1992). Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine Learning, 8, 229\u2013256.","journal-title":"Machine Learning"},{"key":"9755_CR49","doi-asserted-by":"crossref","unstructured":"Zhang, J., Kim, J., O\u2019Donoghue, B., & Boyd, S. (2021). Sample efficient reinforcement learning with REINFORCE. In Proceedings of the AAAI Conference on Artificial Intelligence,\u00a035, 10887\u201310895.","DOI":"10.1609\/aaai.v35i12.17300"},{"key":"9755_CR50","doi-asserted-by":"crossref","unstructured":"Choo, J., Kwon, Y.-D., Kim, J., Jae, J., Hottung, A., Tierney, K., & Gwon, Y. (2022). Simulation-guided beam search for neural combinatorial optimization. In Advances in Neural Information Processing Systems,\u00a035, 8760\u20138772.","DOI":"10.52202\/068431-0637"},{"key":"9755_CR51","unstructured":"Perron, L., & Furnon, V.: OR-Tools. Google. https:\/\/developers.google.com\/optimization\/."}],"container-title":["Autonomous Agents and Multi-Agent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10458-026-09755-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10458-026-09755-7","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10458-026-09755-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T06:29:45Z","timestamp":1781764185000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10458-026-09755-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,23]]},"references-count":51,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,6]]}},"alternative-id":["9755"],"URL":"https:\/\/doi.org\/10.1007\/s10458-026-09755-7","relation":{},"ISSN":["1387-2532","1573-7454"],"issn-type":[{"value":"1387-2532","type":"print"},{"value":"1573-7454","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,23]]},"assertion":[{"value":"17 June 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 May 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 May 2026","order":3,"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 no conflicts of interest to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"27"}}