{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T00:59:36Z","timestamp":1760057976384,"version":"build-2065373602"},"reference-count":61,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,3,12]],"date-time":"2025-03-12T00:00:00Z","timestamp":1741737600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>The discriminative correlation filtering target tracking algorithm can achieve a good balance between tracking accuracy and speed, and therefore has attracted much attention in the field of image tracking. The correlation of response maps can be efficiently calculated in the Fourier domain through the input discrete Fourier transform (DFT), where the DFT of the image has symmetry in the Fourier domain. However, most algorithms based on correlation filtering still have unsatisfactory performance in complex scenarios, especially in scenarios with similar background interference, background clutter, etc., where drift phenomena are prone to occur. To address these issues, this paper proposes a distortion-aware dynamic spatiotemporal regularized correlation filtering target tracking algorithm (DADSTRCF) based on Auto Track. Firstly, a dynamic spatial regularization term is constructed based on color histograms to alleviate the effects of similar background interference, background clutter, and boundary effects. Secondly, a distortion perception function is proposed to determine the degree of distortion of the current frame target, and the Kalman filter is integrated into the relevant filtering framework. When the target undergoes severe distortion, the Kalman filter is switched for tracking. Then, the alternating direction multiplier method (ADMM) is used to obtain the optimal filter solution, reducing computational complexity. Finally, comparative experiments were conducted with various correlated filtering target tracking algorithms on the four datasets of OTB-50, OTB-100, UAV123, and DTB70. The experimental results showed that the tracking precision of DADSTRCF improved by 6.3%, 8.4%, 2.0%, and 6.4%, respectively, compared to the baseline Auto Track, and the success rate improved by 9.3%, 9.3%, 2.5%, and 3.9%, respectively, fully demonstrating the effectiveness of DADSTRCF.<\/jats:p>","DOI":"10.3390\/sym17030422","type":"journal-article","created":{"date-parts":[[2025,3,12]],"date-time":"2025-03-12T04:55:44Z","timestamp":1741755344000},"page":"422","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Distortion-Aware Dynamic Spatial\u2013Temporal Regularized Correlation Filtering Target Tracking Algorithm"],"prefix":"10.3390","volume":"17","author":[{"given":"Weihua","family":"Wang","sequence":"first","affiliation":[{"name":"National Key Laboratory of Science and Technology on Automatic Target Recognition, College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China"}]},{"given":"Hanqing","family":"Wu","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Science and Technology on Automatic Target Recognition, College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5646-9970","authenticated-orcid":false,"given":"Gao","family":"Chen","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Science and Technology on Automatic Target Recognition, College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China"}]},{"given":"Xin","family":"Li","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Science and Technology on Automatic Target Recognition, College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,12]]},"reference":[{"key":"ref_1","first-page":"5603714","article-title":"Bidirectional Multiple Object Tracking Based on Trajectory Criteria in Satellite Videos","volume":"61","author":"Zhang","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"825","DOI":"10.1109\/TSUSC.2022.3177688","article-title":"A Fog-Assisted Framework for Intelligent Video Preprocessing in Cloud-based Video Surveillance as a Service","volume":"7","author":"Siddharth","year":"2022","journal-title":"IEEE Trans. Sustain. Comput."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1061","DOI":"10.1109\/TMC.2020.3017721","article-title":"RF-Dial: Rigid Motion Tracking and Touch Gesture Detection for Interaction via RFID Tags","volume":"21","author":"Bu","year":"2022","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.ogrm.2024.02.005","article-title":"Fast-Track, rapid-access pathways for the diagnosis of gynaecological cancers","volume":"34","author":"James","year":"2024","journal-title":"Obstet. Gynaecol. Reprod. Med."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1177\/1548512917712930","article-title":"Improvement in the model of cooperative aerial reconnaissance used in the tactical decision support system","volume":"14","author":"Stodola","year":"2017","journal-title":"J. Def. Model. Simul."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1996","DOI":"10.1109\/TAES.2021.3126567","article-title":"Robust Guidance for a Reusable Launch Vehicle in Terminal Phase","volume":"58","author":"Mu","year":"2022","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Li, Y., Fu, C., Ding, F., Huang, Z., and Lu, G. (2020, January 13). AutoTrack: Towards High-Performance Visual Tracking for UAV with Automatic Spatio-Temporal Regularization. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01194"},{"key":"ref_8","first-page":"176","article-title":"Infrared Ground Multi-object Tracking Method Based on Improved ByteTrack Algorithm","volume":"50","author":"Luo","year":"2023","journal-title":"Comput. Sci."},{"key":"ref_9","unstructured":"Lucas, B.D., and Kanade, T. (1981, January 24\u201328). An Iterative Image Registration Technique with an Application to Stereo Vision. Proceedings of the 7th International Joint Conference on Artificial Intelligence, Vancouver, BC, Canada."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"5581","DOI":"10.1109\/LRA.2021.3079806","article-title":"A Robust Optical Flow Tracking Method Based on Prediction Model for Visual-Inertial Odometry","volume":"6","author":"Zhu","year":"2021","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_11","first-page":"35","article-title":"A New Approach to Linear Filtering and Prediction Problems","volume":"82","author":"Kalman","year":"1960","journal-title":"J. Fluids Eng."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Sahoo, S.R., and Mannivanan, P.V. (2024). A Novel Method for Ground Vehicle Tracking with Error-State Kalman Filter Based Visual-LiDAR Odometry (ESKF-VLO), IEEE Access.","DOI":"10.1109\/ACCESS.2024.3424234"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Li, J., Xu, X., Jiang, Z., and Jiang, B. (2024). Adaptive Kalman Filter for Real-Time Visual Object Tracking Based on Autocovariance Least Square Estimation. Appl. Sci., 14.","DOI":"10.3390\/app14031045"},{"key":"ref_14","unstructured":"Comaniciu, D., Ramesh, V., and Meer, P. (2000, January 15). Real-Time tracking of non-rigid objects using mean shift. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Hilton Head Island, SC, USA."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1016\/j.patrec.2008.10.017","article-title":"CamShift guided particle filter for visual tracking","volume":"30","author":"Wang","year":"2009","journal-title":"Pattern Recognit. Lett."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Li, J., Tian, Y., Guo, M., Zuo, K., and Wang, X. (2023, January 21\u201323). Visual group target tracking algorithm based on MeanShift-PCA-PF. Proceedings of the 8th International Conference on Intelligent Computing and Signal Processing (ICSP), Xi\u2019an, China.","DOI":"10.1109\/ICSP58490.2023.10248590"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1023\/A:1007939232436","article-title":"EigenTracking: Robust matching and tracking of articulated objects using a view-based representation","volume":"26","author":"Black","year":"1998","journal-title":"Int. J. Comput. Vis."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Javed, S., Mahmood, A., Ullah, I., Bouwmans, T., Khonji, M., Dias, J.M.M., and Werghi, N. (2022). A Novel Algorithm Based on a Common Subspace Fusion for Visual Object Tracking, IEEE Access.","DOI":"10.1109\/ACCESS.2022.3155660"},{"key":"ref_19","unstructured":"Sui, Y., Wang, G., and Zhang, L. (2022). In Defense of Subspace Tracker: Orthogonal Embedding for Visual Tracking. arXiv."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Tao, R., Gavves, E., and Smeulders, A.W.M. (2016, January 27\u201330). Siamese Instance Search for Tracking. Proceedings of the EEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.158"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"111206","DOI":"10.1016\/j.knosys.2023.111206","article-title":"Joint spatio-temporal modeling for visual tracking","volume":"283","author":"Sun","year":"2024","journal-title":"Knowl.-Based Syst."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"104067","DOI":"10.1016\/j.jvcir.2024.104067","article-title":"UnifiedTT: Visual tracking with unified transformer","volume":"99","author":"Yu","year":"2024","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"106380","DOI":"10.1016\/j.neunet.2024.106380","article-title":"DeforT: Deformable transformer for visual tracking","volume":"176","author":"Yang","year":"2024","journal-title":"Neural Netw."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Wang, J., Ye, X., Wu, D., Gong, J., Tang, X., and Li, Z. (2024). Evolution of Siamese Visual Tracking with Slot Attention. Electronics, 13.","DOI":"10.3390\/electronics13030586"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"540","DOI":"10.1049\/cvi2.12263","article-title":"Spatial feature embedding for robust visual object tracking","volume":"18","author":"Liu","year":"2024","journal-title":"IET Comput. Vis."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Tang, C., Wang, K., van de Weijer, J., Zhang, J., and Huang, Y. (2024). AViTMP: A Tracking-Specific Transformer for Single-Branch Visual Tracking. IEEE Trans. Intell. Veh., 1\u201314.","DOI":"10.1109\/TIV.2024.3422806"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"105107","DOI":"10.1016\/j.imavis.2024.105107","article-title":"Visual tracking based on spatiotemporal transformer and fusion sequences","volume":"148","author":"Wu","year":"2024","journal-title":"Image Vis. Comput."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Bolme, D.S., Beveridge, J.R., Draper, B.A., and Lui, Y.M. (2010, January 13\u201318). Visual object tracking using adaptive correlation filters. Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA.","DOI":"10.1109\/CVPR.2010.5539960"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Henriques, J.F., Caseiro, R., Martins, P., and Batista, J. (2012, January 7\u201313). Exploiting the Circulant Structure of Tracking-by-detection with Kernels. Proceedings of the European Conference on Computer Vision, Florence, Italy.","DOI":"10.1007\/978-3-642-33765-9_50"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1109\/TPAMI.2014.2345390","article-title":"High-Speed tracking with kernelized correlation filters","volume":"37","author":"Henriques","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_31","unstructured":"Martin, D., Shahbaz, K.F., Michael, F., and van de Weijer, J. (2014, January 23\u201328). Adaptive Color Attributes for Real-Time Visual Tracking. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Wang, N., Zhou, W.G., Tian, Q., Hong, R.C., Wang, M., and Li, H.Q. (2018, January 18\u201323). Multi-Cue Correlation Filters for Robust Visual Tracking. Proceedings of the 31st IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00509"},{"key":"ref_33","unstructured":"Yang, L., and Jianke, Z. (2014, January 6\u20137). A Scale Adaptive Kernel Correlation Filter Tracker with Feature Integration. Proceedings of the Computer Vision-ECCV 2014 Workshops, Zurich, Switzerland."},{"key":"ref_34","unstructured":"Martin, D., Gustav, H., Fahad, K., and Michael, F. (2014, January 1\u20135). Accurate Scale Estimation for Robust Visual Tracking. Proceedings of the British Machine Vision Conference 2014, Nottingham, UK."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Jiang, B., Luo, R., Mao, J., and Jiang, Y. (2018, January 8\u201314). Acquisition of localization confidence for accurate object detection. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01264-9_48"},{"key":"ref_36","unstructured":"Martin, D., Gustav, H., Fahad, K., and Michael, F. (2015, January 7\u201313). Learning Spatially Regularized Correlation Filters for Visual Tracking. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Galoogahi, H.K., Fagg, A., and Lucey, S. (2017, January 22\u201329). Learning Background-Aware Correlation Filters for Visual Tracking. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.129"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Li, F., Tian, C., Zuo, W., Zhang, L., and Yang, M.H. (2018). Learning Spatial-Temporal Regularized Correlation Filters for Visual Tracking. arXiv.","DOI":"10.1109\/CVPR.2018.00515"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1834","DOI":"10.1109\/TPAMI.2014.2388226","article-title":"Object Tracking Benchmark","volume":"37","author":"Wu","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Dai, K., Wang, D., Lu, H., Sun, C., and Li, J. (2019, January 15\u201320). Visual tracking via adaptive spatially-regularized correlation filters. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00480"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"104509","DOI":"10.1016\/j.infrared.2022.104509","article-title":"RGB-T object tracking via sparse response-consistency discriminative correlation filters","volume":"128","author":"Huang","year":"2023","journal-title":"Infrared Phys. Technol."},{"key":"ref_42","unstructured":"Huang, Z., Fu, C., Li, Y., Lin, F., and Lu, P. (November, January 27). Learning aberrance repressed correlation filters for real time uav tracking. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Republic of Korea."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"109765","DOI":"10.1016\/j.sigpro.2024.109765","article-title":"Learning feature-weighted regularization discriminative correlation filters for real-time UAV tracking","volume":"228","author":"Wang","year":"2025","journal-title":"Signal Process."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Yu, Y.-F., Chen, Z., Zhang, Y., Zhang, C., and Ding, W. (2025, January 21\u201328). Learning Dynamic-Sensitivity Enhanced Correlation Filter with Adaptive Second-Order Difference Spatial Regularization for UAV Tracking. Proceedings of the IEEE Transactions on Intelligent Transportation Systems, Gold Coast, Australia.","DOI":"10.1109\/TITS.2025.3533953"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"7867","DOI":"10.1109\/JSTARS.2024.3380574","article-title":"Adaptive Spatial Regularization Correlation Filters for UAV Tracking","volume":"17","author":"Cao","year":"2024","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"8940","DOI":"10.1109\/TGRS.2020.2992301","article-title":"Object Saliency-Aware Dual Regularized Correlation Filter for Real-Time Aerial Tracking","volume":"58","author":"Fu","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"116655","DOI":"10.1016\/j.image.2022.116655","article-title":"Co-saliency-regularized correlation filter for object tracking","volume":"103","author":"Yang","year":"2022","journal-title":"Signal Process. Image Commun."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Bertinetto, L., Valmadre, J., Golodetz, S., Miksik, O., and Torr, P.H. (2016, January 27\u201330). Staple: Complementary learners for real-time tracking. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.156"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1007\/s11263-017-1061-3","article-title":"Discriminative correlation filter tracker with channel and spatial reliability","volume":"126","author":"Zajc","year":"2018","journal-title":"Int. J. Comput. Vis."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"4065","DOI":"10.1007\/s00371-022-02573-4","article-title":"Color-saliency-aware correlation filters with approximate affine transform for visual tracking","volume":"39","author":"Ma","year":"2023","journal-title":"Vis. Comput."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1007\/s11042-023-15614-4","article-title":"Robust cascaded-parallel visual tracking using collaborative color and correlation filter models","volume":"83","author":"Zhaohui","year":"2024","journal-title":"Multimed. Tools Appl."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Yue, W., Xu, F., and Yang, J. (2024). Tracking-by-Detection Algorithm for Underwater Target Based on Improved Multi-Kernel Correlation Filter. Remote Sens., 16.","DOI":"10.3390\/rs16020323"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"103825","DOI":"10.1016\/j.jvcir.2023.103825","article-title":"Real-Time and robust visual tracking with scene-perceptual memory","volume":"93","author":"Shao","year":"2023","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"7749","DOI":"10.1109\/JSTARS.2023.3306273","article-title":"UAV Tracking Based on Correlation Filters with Dynamic Aberrance-Repressed Temporal Regularizations","volume":"16","author":"Hong","year":"2023","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"37053","DOI":"10.1007\/s11042-023-16707-w","article-title":"Visual tracking via confidence template updating spatial-temporal regularized correlation filters","volume":"83","author":"Liang","year":"2023","journal-title":"Multimed. Tools Appl."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1007\/s11554-023-01306-7","article-title":"Learning discriminative correlation filters via saliency-aware channel selection for robust visual object tracking","volume":"20","author":"Ma","year":"2023","journal-title":"J. Real-Time Image Process."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Wu, Y., Lim, J., and Yang, H.W. (2013, January 23\u201328). Online Object Tracking: A Benchmark. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Portland, OR, USA.","DOI":"10.1109\/CVPR.2013.312"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Mueller, M., Smith, N., and Ghanem, B. (2016, January 11\u201314). A benchmark and simulator for UAV tracking. Proceedings of the 14th European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_27"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Li, S., and Yeung, D.-Y. (2017, January 4\u20139). Visual Object Tracking for Unmanned Aerial Vehicles: A Benchmark and New Motion Models. Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-2017), San Fransico, CA, USA.","DOI":"10.1609\/aaai.v31i1.11205"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.neunet.2023.01.003","article-title":"Enhanced robust spatial feature selection and correlation filter learning for UAV tracking","volume":"161","author":"Wen","year":"2023","journal-title":"Neural Netw."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"122131","DOI":"10.1016\/j.eswa.2023.122131","article-title":"SOCF: A correlation filter for real-time UAV tracking based on spatial disturbance suppression and object saliency-aware","volume":"238","author":"Ma","year":"2024","journal-title":"Expert Syst. Appl."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/3\/422\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T16:50:59Z","timestamp":1760028659000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/3\/422"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,12]]},"references-count":61,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025,3]]}},"alternative-id":["sym17030422"],"URL":"https:\/\/doi.org\/10.3390\/sym17030422","relation":{},"ISSN":["2073-8994"],"issn-type":[{"type":"electronic","value":"2073-8994"}],"subject":[],"published":{"date-parts":[[2025,3,12]]}}}