{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T21:14:08Z","timestamp":1769634848528,"version":"3.49.0"},"reference-count":53,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2025,6,14]],"date-time":"2025-06-14T00:00:00Z","timestamp":1749859200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,6,14]],"date-time":"2025-06-14T00:00:00Z","timestamp":1749859200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100003787","name":"Natural Science Foundation of Hebei Province","doi-asserted-by":"publisher","award":["No. F2024201004"],"award-info":[{"award-number":["No. F2024201004"]}],"id":[{"id":"10.13039\/501100003787","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Science Research Project of Hebei Education Department","award":["BJK2024095"],"award-info":[{"award-number":["BJK2024095"]}]},{"name":"Advanced Talents Incubation Program of the Hebei University","award":["No. 521000981346"],"award-info":[{"award-number":["No. 521000981346"]}]},{"name":"Innovation Capacity Enhancement Program-Science and Technology Platform Project of Hebei Province","award":["22567638H"],"award-info":[{"award-number":["22567638H"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Autom Softw Eng"],"published-print":{"date-parts":[[2025,11]]},"DOI":"10.1007\/s10515-025-00532-6","type":"journal-article","created":{"date-parts":[[2025,6,13]],"date-time":"2025-06-13T20:47:14Z","timestamp":1749847634000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["DC-GAR: detecting vulnerabilities by utilizing graph properties and random walks to uncover richer features"],"prefix":"10.1007","volume":"32","author":[{"given":"Meng","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiao","family":"Han","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hong","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yiran","family":"Guo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiangfan","family":"Guo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,6,14]]},"reference":[{"key":"532_CR1","doi-asserted-by":"publisher","unstructured":"Bavelas., A.: Communication patterns in task-oriented groups. The Acoustical Society of America, 725\u2013730 (1950) https:\/\/doi.org\/10.1121\/1.1906679","DOI":"10.1121\/1.1906679"},{"key":"532_CR2","doi-asserted-by":"publisher","unstructured":"Cheng, J., Chen, Y., Cao, Y., Wang, H.: A vulnerability detection framework by focusing on critical execution paths. Inf. Softw. Technol. 174, 107517 (2024) https:\/\/doi.org\/10.1016\/J.INFSOF.2024.107517","DOI":"10.1016\/J.INFSOF.2024.107517"},{"key":"532_CR3","doi-asserted-by":"crossref","unstructured":"Cohen, J.: Statistical Power Analysis for the Behavioral Sciences, (2013)","DOI":"10.4324\/9780203771587"},{"key":"532_CR4","doi-asserted-by":"publisher","unstructured":"Cao, S., Sun, X., Bo, L., Wei, Y., Li, B.: Bgnn4vd: Constructing bidirectional graph neural-network for vulnerability detection. Inf. Softw. Technol. 136 (2021) https:\/\/doi.org\/10.1016\/j.infsof.2021.106576","DOI":"10.1016\/j.infsof.2021.106576"},{"key":"532_CR5","doi-asserted-by":"publisher","unstructured":"Cao, S., Sun, X., Wu, X., Lo, D., Bo, L., Li, B., Liu, W.: Coca: Improving and explaining graph neural network-based vulnerability detection systems. In: Proceedings of the 46th IEEE\/ACM International Conference on Software Engineering, ICSE 2024, Lisbon, Portugal, 14-20 April, 2024, pp. 155\u2013115513 (2024). https:\/\/doi.org\/10.1145\/3597503.3639168","DOI":"10.1145\/3597503.3639168"},{"key":"532_CR6","unstructured":"Defferrard, M., Bresson, X., Vandergheynst, P.: Convolutional neural networks on graphs with fast localized spectral filtering. In: Advances in Neural Information Processing Systems, pp. 3844\u20133852 (2016)"},{"key":"532_CR7","doi-asserted-by":"publisher","unstructured":"Duan, X., Wu, J., Ji, S., Rui, Z., Luo, T., Yang, M., Wu, Y.: Vulsniper: Focus your attention to shoot fine-grained vulnerabilities. In: IJCAI, pp. 4665\u20134671 (2019). https:\/\/doi.org\/10.24963\/ijcai.2019\/648","DOI":"10.24963\/ijcai.2019\/648"},{"issue":"2","key":"532_CR8","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1145\/3575637.3575646","volume":"24","author":"K Ding","year":"2022","unstructured":"Ding, K., Xu, Z., Tong, H., Liu, H.: Data augmentation for deep graph learning: A survey. ACM SIGKDD Explor. Newslett. 24(2), 61\u201377 (2022). https:\/\/doi.org\/10.1145\/3575637.3575646","journal-title":"ACM SIGKDD Explor. Newslett."},{"key":"532_CR9","unstructured":"Freeman, L.C., et al.: Centrality in social networks: Conceptual clarification. Social network: critical concepts in sociology. Londres: Routledge 1, 238\u2013263 (2002)"},{"key":"532_CR10","doi-asserted-by":"publisher","unstructured":"Feng, Z., Guo, D., Tang, D., Duan, N., Feng, X., Gong, M., Shou, L., Qin, B., Liu, T., Jiang, D., Zhou, M.: Codebert: A pre-trained model for programming and natural languages. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 1536\u20131547 (2020). https:\/\/doi.org\/10.18653\/v1\/2020.findings-emnlp.139","DOI":"10.18653\/v1\/2020.findings-emnlp.139"},{"key":"532_CR11","doi-asserted-by":"crossref","unstructured":"Fan, J., Li, Y., Wang, S., Nguyen, T.N.: Ac\/c++ code vulnerability dataset with code changes and cve summaries. In: Proceedings of the 17th International Conference on Mining Software Repositories, pp. 508\u2013512 (2020)","DOI":"10.1145\/3379597.3387501"},{"issue":"3","key":"532_CR12","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1145\/24039.24041","volume":"9","author":"J Ferrante","year":"1987","unstructured":"Ferrante, J., Ottenstein, K.J., Warren, J.D.: The program dependence graph and its use in optimization. ACM Trans. Program. Language. Syst. (TOPLAS) 9(3), 319\u2013349 (1987). https:\/\/doi.org\/10.1145\/24039.24041","journal-title":"ACM Trans. Program. Language. Syst. (TOPLAS)"},{"key":"532_CR13","doi-asserted-by":"publisher","DOI":"10.2307\/3033543","author":"L Freeman","year":"1977","unstructured":"Freeman, L.: A set of measures of centrality based on betweenness. Sociomet. (1977). https:\/\/doi.org\/10.2307\/3033543","journal-title":"Sociomet."},{"key":"532_CR14","doi-asserted-by":"publisher","unstructured":"Freeman., L.C.: Centrality in social networks: Conceptual clarification. Soc. Netw., 215\u2013239 (1979) https:\/\/doi.org\/10.1016\/0378-8733(78)90021-7","DOI":"10.1016\/0378-8733(78)90021-7"},{"key":"532_CR15","doi-asserted-by":"publisher","unstructured":"Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM pp. 855\u2013864 (2016). https:\/\/doi.org\/10.1145\/2939672.2939754","DOI":"10.1145\/2939672.2939754"},{"key":"532_CR16","doi-asserted-by":"publisher","unstructured":"Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855\u2013864 (2016). https:\/\/doi.org\/10.1145\/2623330.2623732","DOI":"10.1145\/2623330.2623732"},{"key":"532_CR17","unstructured":"Guo, D., Ren, S., Lu, S., Feng, Z., Tang, D., Liu, S., Zhou, L., Duan, N., Svyatkovskiy, A., Fu, S., Tufano, M., Deng, S.K., Clement, C.B., Drain, D., Sundaresan, N., Yin, J., Jiang, D., Zhou, M.: Graphcodebert: Pre-training code representations with data flow. In: 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, 3-7 May, 2021 (2021)"},{"key":"532_CR18","doi-asserted-by":"publisher","unstructured":"Hanif, H., Nasir, M.H.N.M., Ab\u00a0Razak, M.F., Firdaus, A., Anuar, N.B.: The rise of software vulnerability: Taxonomy of software vulnerabilities detection and machine learning approaches. J. Netw. Comput. Appl. 179, 103009 (2021) https:\/\/doi.org\/10.1016\/j.jnca.2021.103009","DOI":"10.1016\/j.jnca.2021.103009"},{"key":"532_CR19","unstructured":"Kipf, T., Welling, M.: Semi-supervised classification with graph convolutional networks. ArXiv (2016)"},{"issue":"10","key":"532_CR20","doi-asserted-by":"publisher","first-page":"1825","DOI":"10.1109\/JPROC.2020.2993293","volume":"108","author":"G Lin","year":"2020","unstructured":"Lin, G., Wen, S., Han, Q.-L., Zhang, J., Xiang, Y.: Software vulnerability detection using deep neural networks: a survey. Proceed. IEEE. 108(10), 1825\u20131848 (2020). https:\/\/doi.org\/10.1109\/JPROC.2020.2993293","journal-title":"Proceed. IEEE."},{"key":"532_CR21","doi-asserted-by":"publisher","unstructured":"Liu, X., Xiao, T., Davis, U., Cao, Q.: How does noise help robustness? explanation and exploration under the neural sde framework. 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 279\u2013287 (2020). https:\/\/doi.org\/10.1109\/cvpr42600.2020.00036","DOI":"10.1109\/cvpr42600.2020.00036"},{"key":"532_CR22","doi-asserted-by":"publisher","unstructured":"Li, Z., Zou, D., Xu, S., Ou, X., Jin, H., Wang, S., Deng, Z., Zhong, Y.: Vuldeepecker: A deep learning-based system for vulnerability detection. 25th Annual Netw. Distrib. Syst. Secur. Symp., NDSS (2018). https:\/\/doi.org\/10.14722\/ndss.2018.23158","DOI":"10.14722\/ndss.2018.23158"},{"key":"532_CR23","doi-asserted-by":"publisher","unstructured":"Li, Z., Zou, D., Xu, S., Jin, H., Zhu, Y., Chen, Z.: Sysevr: A framework for using deep learning to detect software vulnerabilities. IEEE Trans. Depend. Sec. Comput. 19, 2244\u20132258 (2022). https:\/\/doi.org\/10.1109\/tdsc.2021.3051525","DOI":"10.1109\/tdsc.2021.3051525"},{"key":"532_CR24","unstructured":"Mikolov, T.: Efficient estimation of word representations in vector space 3781 (2013)"},{"key":"532_CR25","doi-asserted-by":"publisher","unstructured":"Nguyen, V., Nguyen, D.Q., Nguyen, V., Le, T., Tran, Q.H., Phung, D.: Regvd: Revisiting graph neural networks for vulnerability detection. In: 44th IEEE\/ACM International Conference on Software Engineering: Companion Proceedings, ICSE Companion 2022, Pittsburgh, PA, USA, 22-24 May, 2022, pp. 178\u2013182 (2022). https:\/\/doi.org\/10.1145\/3510454.3516865","DOI":"10.1145\/3510454.3516865"},{"key":"532_CR26","volume-title":"The pagerank citation ranking: Bringing order to the web","author":"L Page","year":"1999","unstructured":"Page, L.: The pagerank citation ranking: Bringing order to the web. Technical report, Technical Report (1999)"},{"key":"532_CR27","doi-asserted-by":"publisher","unstructured":"Pearson, K.: The problem of the random walk. Nature 72, 294 (1905). https:\/\/doi.org\/10.1038\/072342a0","DOI":"10.1038\/072342a0"},{"issue":"8","key":"532_CR28","doi-asserted-by":"publisher","first-page":"2178","DOI":"10.1109\/TSE.2024.3427815","volume":"50","author":"F Qiu","year":"2024","unstructured":"Qiu, F., Liu, Z., Hu, X., Xia, X., Chen, G., Wang, X.: Vulnerability detection via multiple-graph-based code representation. IEEE Trans. Softw. Eng. 50(8), 2178\u20132199 (2024). https:\/\/doi.org\/10.1109\/TSE.2024.3427815","journal-title":"IEEE Trans. Softw. Eng."},{"key":"532_CR29","unstructured":"CWE-127: Buffer Under-read. https:\/\/cwe.mitre.org\/data\/definiti ons\/127.html (2024)"},{"key":"532_CR30","unstructured":"Software Assurance Reference Dataset (SARD). https:\/\/samate.nist.gov\/SARD (2024)"},{"key":"532_CR31","unstructured":"NetworkX: Software for Complex Networks. https:\/\/networkx.github.io\/ (2024)"},{"key":"532_CR32","unstructured":"Automatic differentiation in PyTorch. https:\/\/pytorch.org\/ (2024)"},{"key":"532_CR33","doi-asserted-by":"publisher","unstructured":"Rani, S., Kataria, A., Chauhan, M.: Cyber Security Techniques, Architectures, and Design, pp. 41\u201366 (2022). https:\/\/doi.org\/10.1201\/9781003296034-3","DOI":"10.1201\/9781003296034-3"},{"key":"532_CR34","doi-asserted-by":"publisher","unstructured":"Saccente, N., Dehlinger, J., Deng, L., Chakraborty, S., Xiong, Y.: Project achilles: A prototype tool for static method-level vulnerability detection of java source code using a recurrent neural network. In: 2019 34th IEEE\/ACM International Conference on Automated Software Engineering Workshop (ASEW), IEEE pp. 114\u2013121 (2019). https:\/\/doi.org\/10.1109\/asew.2019.00040","DOI":"10.1109\/asew.2019.00040"},{"issue":"1","key":"532_CR35","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1109\/tnn.2008.2005605","volume":"20","author":"F Scarselli","year":"2008","unstructured":"Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE Trans. Neural. Netw. 20(1), 61\u201380 (2008). https:\/\/doi.org\/10.1109\/tnn.2008.2005605","journal-title":"IEEE Trans. Neural. Netw."},{"key":"532_CR36","doi-asserted-by":"publisher","unstructured":"Sarkar, P., Moore, A.W.: Random walks in social networks and their applications: a survey. Soc. Netw. Data. Anal., 43\u201377 (2011) https:\/\/doi.org\/10.1007\/978-1-4419-8462-3_3","DOI":"10.1007\/978-1-4419-8462-3_3"},{"issue":"11","key":"532_CR37","doi-asserted-by":"publisher","first-page":"2673","DOI":"10.1109\/78.650093","volume":"45","author":"M Schuster","year":"1997","unstructured":"Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE Trans. Signal. Process. 45(11), 2673\u20132681 (1997). https:\/\/doi.org\/10.1109\/78.650093","journal-title":"IEEE Trans. Signal. Process."},{"key":"532_CR38","doi-asserted-by":"publisher","unstructured":"Tian, Z., Li, H., Sun, H., Chen, Y., Chen, L.: Hardvd: High-capacity cross-modal adversarial reprogramming for data-efficient vulnerability detection. Inf. Sci. 686, 121370 (2025). https:\/\/doi.org\/10.1016\/j.ins.2024.121370","DOI":"10.1016\/j.ins.2024.121370"},{"key":"532_CR39","doi-asserted-by":"publisher","unstructured":"Tang, W., Tang, M., Ban, M., Zhao, Z., Feng, M.: Csgvd: A deep learning approach combining sequence and graph embedding for source code vulnerability detection. J. Syst. Softw. 199, 111623 (2023). https:\/\/doi.org\/10.1016\/j.jss.2023.111623","DOI":"10.1016\/j.jss.2023.111623"},{"key":"532_CR40","doi-asserted-by":"publisher","unstructured":"Wen, X., Chen, Y., Gao, C., Zhang, H., Zhang, J.M., Liao, Q.: Vulnerability detection with graph simplification and enhanced graph representation learning. In: 45th IEEE\/ACM International Conference on Software Engineering, ICSE 2023, Melbourne, Australia, 14-20 May, 2023, pp. 2275\u20132286 (2023). https:\/\/doi.org\/10.1109\/ICSE48619.2023.00191","DOI":"10.1109\/ICSE48619.2023.00191"},{"key":"532_CR41","unstructured":"Wheeler, D.A.: Flawfinder. https:\/\/dwheeler.com\/flawfinder\/ (2024)"},{"key":"532_CR42","doi-asserted-by":"publisher","unstructured":"Watts, D.J., Strogatz, S.H.: Collective dynamics of \u201csmall-world\u201d networks. Nature 393(6684), 440\u2013442 (1998). https:\/\/doi.org\/10.1038\/30918","DOI":"10.1038\/30918"},{"key":"532_CR43","doi-asserted-by":"publisher","unstructured":"Wu, Y., Zou, D., Dou, S., Yang, W., Xu, D., Jin, H.: Vulcnn: An image-inspired scalable vulnerability detection system. In: Proceedings of the 44th International Conference on Software Engineering, pp. 2365\u20132376 (2022). https:\/\/doi.org\/10.1145\/3510003.3510229","DOI":"10.1145\/3510003.3510229"},{"key":"532_CR44","doi-asserted-by":"publisher","unstructured":"Xiao, P., Xiao, Q., Zhang, X., Wu, Y., Yang, F.: Vulnerability detection based on enhanced graph representation learning. IEEE Trans. Inf. Forensics Secur. 19, 5120\u20135135 (2024). https:\/\/doi.org\/10.1109\/TIFS.2024.3392536","DOI":"10.1109\/TIFS.2024.3392536"},{"key":"532_CR45","unstructured":"Yamaguchi, F.: Joern: A Robust Code Analysis Platform. https:\/\/joern.io (2014)"},{"key":"532_CR46","doi-asserted-by":"publisher","DOI":"10.1109\/tdsc.2022.3199769","author":"D Zou","year":"2022","unstructured":"Zou, D., Hu, Y., Li, W., Wu, Y., Zhao, H., Jin, H.: mvulpreter: A multi-granularity vulnerability detection system with interpretations. IEEE Trans Depend Secur Comput (2022). https:\/\/doi.org\/10.1109\/tdsc.2022.3199769","journal-title":"IEEE Trans Depend Secur Comput"},{"key":"532_CR47","doi-asserted-by":"publisher","unstructured":"Zhang, H., Kou, G., Peng, Y., Zhang, B.: Role-aware random walk for network embedding. Inf Sci 652, 119765 (2024). https:\/\/doi.org\/10.1016\/j.ins.2023.119765","DOI":"10.1016\/j.ins.2023.119765"},{"issue":"8","key":"532_CR48","doi-asserted-by":"publisher","first-page":"4196","DOI":"10.1109\/TSE.2023.3286586","volume":"49","author":"J Zhang","year":"2023","unstructured":"Zhang, J., Liu, Z., Hu, X., Xia, X., Li, S.: Vulnerability detection by learning from syntax-based execution paths of code. IEEE Trans. Software Eng. 49(8), 4196\u20134212 (2023). https:\/\/doi.org\/10.1109\/TSE.2023.3286586","journal-title":"IEEE Trans. Software Eng."},{"key":"532_CR49","unstructured":"Zhou, Y., Liu, S., Siow, J., Du, X., Liu, Y.: Devign: Effective vulnerability identification by learning comprehensive program semantics via graph neural networks. Adv Neural Inf Process Syst 32 (2019)"},{"issue":"2","key":"532_CR50","doi-asserted-by":"publisher","first-page":"293","DOI":"10.3390\/e25020293","volume":"25","author":"K Zelenkovski","year":"2023","unstructured":"Zelenkovski, K., Sandev, T., Metzler, R., Kocarev, L., Basnarkov, L.: Random walks on networks with centrality-based stochastic resetting. Entropy 25(2), 293 (2023). https:\/\/doi.org\/10.3390\/e25020293","journal-title":"Entropy"},{"key":"532_CR51","doi-asserted-by":"publisher","unstructured":"Zou, D., Wang, S., Xu, S., Li, Z., Jin, H.:\u00a0\u03bcvuldeepecker: A deep learning-based system for multiclass vulnerability detection. IEEE Trans. Depend. Secur. Comput. (2019). https:\/\/doi.org\/10.1109\/tdsc.2019.2942930","DOI":"10.1109\/tdsc.2019.2942930"},{"issue":"2","key":"532_CR52","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1162\/coli_a_00476","volume":"49","author":"J Zeng","year":"2023","unstructured":"Zeng, J., Xu, J., Zheng, X., Huang, X.: Certified robustness to text adversarial attacks by randomized [mask]. Computation. Linguist. 49(2), 395\u2013427 (2023). https:\/\/doi.org\/10.1162\/coli_a_00476","journal-title":"Computation. Linguist."},{"key":"532_CR53","first-page":"20321","volume":"34","author":"W Zhang","year":"2021","unstructured":"Zhang, W., Yang, M., Sheng, Z., Li, Y., Ouyang, W., Tao, Y., Yang, Z., Cui, B.: Node dependent local smoothing for scalable graph learning. Adv. Neural Inf. Process. Syst. 34, 20321\u201320332 (2021)","journal-title":"Adv. Neural Inf. Process. Syst."}],"container-title":["Automated Software Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10515-025-00532-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10515-025-00532-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10515-025-00532-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,10]],"date-time":"2025-09-10T13:57:27Z","timestamp":1757512647000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10515-025-00532-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,14]]},"references-count":53,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,11]]}},"alternative-id":["532"],"URL":"https:\/\/doi.org\/10.1007\/s10515-025-00532-6","relation":{},"ISSN":["0928-8910","1573-7535"],"issn-type":[{"value":"0928-8910","type":"print"},{"value":"1573-7535","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,14]]},"assertion":[{"value":"15 January 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 June 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 June 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 June 2025","order":4,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Update","order":5,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The Acknowledgements section was missing from this article and should have read 'This research is supported by Hebei Natural Science Foundation under grant No. F2024201004, Science Research Project of Hebei Education Department under grant BJK2024095, Advanced Talents Incubation Program of the Hebei University under grant No. 521000981346 and the Innovation Capacity Enhancement Program-Science and Technology Platform Project of Hebei Province under grant 22567638H'.","order":6,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"61"}}