{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T21:15:54Z","timestamp":1757625354624,"version":"3.44.0"},"reference-count":48,"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":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62405037"],"award-info":[{"award-number":["62405037"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Scientific Research Foundation of Chongqing University of Technology","award":["2021ZDZ018"],"award-info":[{"award-number":["2021ZDZ018"]}]},{"DOI":"10.13039\/501100005230","name":"Chongqing Natural Science Foundation","doi-asserted-by":"crossref","award":["CSTB2024NSCQ-MSX0581"],"award-info":[{"award-number":["CSTB2024NSCQ-MSX0581"]}],"id":[{"id":"10.13039\/501100005230","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Graduate Student Innovation Program of Chongqing University of Technology","award":["gzlcx20243536","gzlcx20243536"],"award-info":[{"award-number":["gzlcx20243536","gzlcx20243536"]}]},{"name":"Science and Technology Projects in Guangzhou"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cluster Comput"],"published-print":{"date-parts":[[2025,9]]},"DOI":"10.1007\/s10586-025-05137-y","type":"journal-article","created":{"date-parts":[[2025,7,31]],"date-time":"2025-07-31T12:51:49Z","timestamp":1753966309000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Using spatial-frequency features for visible and infrared image fusion"],"prefix":"10.1007","volume":"28","author":[{"given":"Fen","family":"Zhao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Guo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongan","family":"Pan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong","family":"Tang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,7,31]]},"reference":[{"key":"5137_CR1","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1016\/j.inffus.2018.02.004","volume":"45","author":"J Ma","year":"2019","unstructured":"Ma, J., Ma, Y., Li, C.: Infrared and visible image fusion methods and applications: a survey. Inf. Fus. 45, 153\u2013178 (2019)","journal-title":"Inf. Fus."},{"key":"5137_CR2","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1016\/j.inffus.2021.06.008","volume":"76","author":"H Zhang","year":"2021","unstructured":"Zhang, H., Xu, H., Tian, X., Jiang, J., Ma, J.: Image fusion meets deep learning: a survey and perspective. Inf. Fus. 76, 323\u2013336 (2021)","journal-title":"Inf. Fus."},{"key":"5137_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIM.2020.2986875","volume":"70","author":"Y Yang","year":"2020","unstructured":"Yang, Y., Zhang, Y., Huang, S., Zuo, Y., Sun, J.: Infrared and visible image fusion using visual saliency sparse representation and detail injection model. IEEE Trans. on Instrum. and Meas. 70, 1\u201315 (2020)","journal-title":"IEEE Trans. on Instrum. and Meas."},{"key":"5137_CR4","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1016\/j.inffus.2014.05.003","volume":"22","author":"X Bai","year":"2015","unstructured":"Bai, X., Zhang, Y., Zhou, F., Xue, B.: Quadtree-based multi-focus image fusion using a weighted focus-measure. Inf. Fus. 22, 105\u2013118 (2015)","journal-title":"Inf. Fus."},{"issue":"7","key":"5137_CR5","doi-asserted-by":"publisher","first-page":"2864","DOI":"10.1109\/TIP.2013.2244222","volume":"22","author":"S Li","year":"2013","unstructured":"Li, S., Kang, X., Hu, J.: Image fusion with guided filtering. IEEE Trans. on Image Process. 22(7), 2864\u20132875 (2013)","journal-title":"IEEE Trans. on Image Process."},{"key":"5137_CR6","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1016\/j.inffus.2021.02.023","volume":"73","author":"H Li","year":"2021","unstructured":"Li, H., Wu, X.-J., Kittler, J.: Rfn-nest: an end-to-end residual fusion network for infrared and visible images. Inf. Fus. 73, 72\u201386 (2021)","journal-title":"Inf. Fus."},{"issue":"5","key":"5137_CR7","doi-asserted-by":"publisher","first-page":"1748","DOI":"10.1007\/s11263-023-01952-1","volume":"132","author":"J Liu","year":"2024","unstructured":"Liu, J., Lin, R., Wu, G., Liu, R., Luo, Z., Fan, X.: Coconet: coupled contrastive learning network with multi-level feature ensemble for multi-modality image fusion. Int. J. Computer Vision 132(5), 1748\u20131775 (2024)","journal-title":"Int. J. Computer Vision"},{"key":"5137_CR8","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1016\/j.inffus.2016.02.001","volume":"31","author":"J Ma","year":"2016","unstructured":"Ma, J., Chen, C., Li, C., Huang, J.: Infrared and visible image fusion via gradient transfer and total variation minimization. Inf. Fus. 31, 100\u2013109 (2016)","journal-title":"Inf. Fus."},{"key":"5137_CR9","doi-asserted-by":"crossref","unstructured":"Li, H., Wu, X.-J., Kittler, J.: Infrared and visible image fusion using a deep learning framework. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2705\u20132710 (2018). IEEE","DOI":"10.1109\/ICPR.2018.8546006"},{"key":"5137_CR10","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1016\/j.inffus.2020.11.009","volume":"69","author":"Y Long","year":"2021","unstructured":"Long, Y., Jia, H., Zhong, Y., Jiang, Y., Jia, Y.: Rxdnfuse: a aggregated residual dense network for infrared and visible image fusion. Inf. Fus. 69, 128\u2013141 (2021)","journal-title":"Inf. Fus."},{"key":"5137_CR11","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.inffus.2018.09.004","volume":"48","author":"J Ma","year":"2019","unstructured":"Ma, J., Yu, W., Liang, P., Li, C., Jiang, J.: Fusiongan: a generative adversarial network for infrared and visible image fusion. Inf. Fus. 48, 11\u201326 (2019)","journal-title":"Inf. Fus."},{"key":"5137_CR12","first-page":"1","volume":"70","author":"J Ma","year":"2020","unstructured":"Ma, J., Zhang, H., Shao, Z., Liang, P., Xu, H.: Ganmcc: a generative adversarial network with multiclassification constraints for infrared and visible image fusion. IEEE Trans. Instrum. and Meas. 70, 1\u201314 (2020)","journal-title":"IEEE Trans. Instrum. and Meas."},{"issue":"8","key":"5137_CR13","doi-asserted-by":"publisher","first-page":"10535","DOI":"10.1109\/TPAMI.2023.3261282","volume":"45","author":"X Zhang","year":"2023","unstructured":"Zhang, X., Demiris, Y.: Visible and infrared image fusion using deep learning. IEEE Trans. Pattern Anal. and Mach. Intell. 45(8), 10535\u201310554 (2023)","journal-title":"IEEE Trans. Pattern Anal. and Mach. Intell."},{"key":"5137_CR14","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1016\/j.inffus.2019.07.011","volume":"54","author":"Y Zhang","year":"2020","unstructured":"Zhang, Y., Liu, Y., Sun, P., Yan, H., Zhao, X., Zhang, L.: Ifcnn: a general image fusion framework based on convolutional neural network. Inf. Fus. 54, 99\u2013118 (2020)","journal-title":"Inf. Fus."},{"issue":"2","key":"5137_CR15","doi-asserted-by":"publisher","first-page":"599","DOI":"10.3390\/s23020599","volume":"23","author":"W Ma","year":"2023","unstructured":"Ma, W., Wang, K., Li, J., Yang, S.X., Li, J., Song, L., Li, Q.: Infrared and visible image fusion technology and application: a review. Sensors 23(2), 599 (2023)","journal-title":"Sensors"},{"issue":"10","key":"5137_CR16","doi-asserted-by":"publisher","first-page":"12148","DOI":"10.1109\/TPAMI.2023.3283682","volume":"45","author":"H Xu","year":"2023","unstructured":"Xu, H., Yuan, J., Ma, J.: Murf: mutually reinforcing multi-modal image registration and fusion. IEEE Trans. Pattern Anal. and Mach. Intell. 45(10), 12148\u201312166 (2023)","journal-title":"IEEE Trans. Pattern Anal. and Mach. Intell."},{"key":"5137_CR17","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1016\/j.inffus.2016.09.006","volume":"35","author":"Y Zhang","year":"2017","unstructured":"Zhang, Y., Bai, X., Wang, T.: Boundary finding based multi-focus image fusion through multi-scale morphological focus-measure. Inf. Fus. 35, 81\u2013101 (2017)","journal-title":"Inf. Fus."},{"key":"5137_CR18","doi-asserted-by":"crossref","unstructured":"Zhang, X., Ye, P., Qiao, D., Zhao, J., Peng, S., Xiao, G.: Object fusion tracking based on visible and infrared images using fully convolutional siamese networks. In: 2019 22th International Conference on Information Fusion (FUSION), pp. 1\u20138 (2019). IEEE","DOI":"10.23919\/FUSION43075.2019.9011253"},{"key":"5137_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.dsp.2023.104020","volume":"137","author":"S Singh","year":"2023","unstructured":"Singh, S., Singh, H., Bueno, G., Deniz, O., Singh, S., Monga, H., Hrisheekesha, P., Pedraza, A.: A review of image fusion: methods, applications and performance metrics. Digit. Sign. Process. 137, 104020 (2023)","journal-title":"Digit. Sign. Process."},{"issue":"10","key":"5137_CR20","doi-asserted-by":"publisher","first-page":"1664","DOI":"10.1109\/PROC.1967.5957","volume":"55","author":"WT Cochran","year":"1967","unstructured":"Cochran, W.T., Cooley, J.W., Favin, D.L., Helms, H.D., Kaenel, R.A., Lang, W.W., Maling, G.C., Nelson, D.E., Rader, C.M., Welch, P.D.: What is the fast fourier transform? Proc. IEEE 55(10), 1664\u20131674 (1967)","journal-title":"Proc. IEEE"},{"key":"5137_CR21","first-page":"4479","volume":"33","author":"L Chi","year":"2020","unstructured":"Chi, L., Jiang, B., Mu, Y.: Fast fourier convolution. Adv. Neural Inf. Process. Syst. 33, 4479\u20134488 (2020)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"5137_CR22","doi-asserted-by":"crossref","unstructured":"Oppenheim, A., Lim, J., Kopec, G., Pohlig, S.: Phase in speech and pictures. In: ICASSP\u201979. IEEE International Conference on Acoustics, Speech, and Signal Processing, 4, 632\u2013637 (1979). IEEE","DOI":"10.1109\/ICASSP.1979.1170798"},{"key":"5137_CR23","doi-asserted-by":"crossref","unstructured":"Tan, J., Huang, J., Zheng, N., Zhou, M., Yan, K., Hong, D., Zhao, F.: Revisiting spatial-frequency information integration from a hierarchical perspective for panchromatic and multi-spectral image fusion. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 25922\u201325931 (2024)","DOI":"10.1109\/CVPR52733.2024.02449"},{"key":"5137_CR24","doi-asserted-by":"publisher","first-page":"105042","DOI":"10.1016\/j.cageo.2022.105042","volume":"160","author":"D Wang","year":"2022","unstructured":"Wang, D., Zhang, C., Han, M.: Mlfc-net: a multi-level feature combination attention model for remote sensing scene classification. Comput. & Geosci. 160, 105042 (2022)","journal-title":"Comput. & Geosci."},{"key":"5137_CR25","doi-asserted-by":"publisher","first-page":"477","DOI":"10.1016\/j.inffus.2022.10.034","volume":"91","author":"L Tang","year":"2023","unstructured":"Tang, L., Xiang, X., Zhang, H., Gong, M., Ma, J.: Divfusion: darkness-free infrared and visible image fusion. Inf. Fusi. 91, 477\u2013493 (2023)","journal-title":"Inf. Fusi."},{"key":"5137_CR26","first-page":"1","volume":"72","author":"J Wang","year":"2023","unstructured":"Wang, J., Xi, X., Li, D., Li, F.: Fusiongram: an infrared and visible image fusion framework based on gradient residual and attention mechanism. IEEE Trans. Instrum. and Meas. 72, 1\u201312 (2023)","journal-title":"IEEE Trans. Instrum. and Meas."},{"key":"5137_CR27","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1016\/j.inffus.2022.03.007","volume":"83","author":"L Tang","year":"2022","unstructured":"Tang, L., Yuan, J., Zhang, H., Jiang, X., Ma, J.: Piafusion: a progressive infrared and visible image fusion network based on illumination aware. Inf. Fus. 83, 79\u201392 (2022)","journal-title":"Inf. Fus."},{"key":"5137_CR28","doi-asserted-by":"crossref","unstructured":"Liu, J., Fan, X., Huang, Z., Wu, G., Liu, R., Zhong, W., Luo, Z.: Target-aware dual adversarial learning and a multi-scenario multi-modality benchmark to fuse infrared and visible for object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5802\u20135811 (2022)","DOI":"10.1109\/CVPR52688.2022.00571"},{"key":"5137_CR29","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1016\/j.inffus.2022.09.030","volume":"91","author":"J Liu","year":"2023","unstructured":"Liu, J., Dian, R., Li, S., Liu, H.: Sgfusion: a saliency guided deep-learning framework for pixel-level image fusion. Inf. Fus. 91, 205\u2013214 (2023)","journal-title":"Inf. Fus."},{"issue":"10","key":"5137_CR30","doi-asserted-by":"publisher","first-page":"2761","DOI":"10.1007\/s11263-021-01501-8","volume":"129","author":"H Zhang","year":"2021","unstructured":"Zhang, H., Ma, J.: Sdnet: a versatile squeeze-and-decomposition network for real-time image fusion. Int. J. Comput. Vision 129(10), 2761\u20132785 (2021)","journal-title":"Int. J. Comput. Vision"},{"key":"5137_CR31","doi-asserted-by":"publisher","first-page":"575","DOI":"10.1007\/s10044-020-00919-z","volume":"24","author":"S Budhiraja","year":"2021","unstructured":"Budhiraja, S., Sharma, R., Agrawal, S., Sohi, B.S.: Infrared and visible image fusion using modified spatial frequency-based clustered dictionary. Pattern Anal. and Appl. 24, 575\u2013589 (2021)","journal-title":"Pattern Anal. and Appl."},{"key":"5137_CR32","unstructured":"Zhou, M., Huang, J., Guo, C.-L., Li, C.: Fourmer: An efficient global modeling paradigm for image restoration. In: International Conference on Machine Learning, pp. 42589\u201342601 (2023). PMLR"},{"key":"5137_CR33","unstructured":"Li, C., Guo, C.-L., Zhou, M., Liang, Z., Zhou, S., Feng, R., Loy, C.C.: Embedding fourier for ultra-high-definition low-light image enhancement. arXiv preprint arXiv:2302.11831 (2023)"},{"key":"5137_CR34","first-page":"980","volume":"34","author":"Y Rao","year":"2021","unstructured":"Rao, Y., Zhao, W., Zhu, Z., Lu, J., Zhou, J.: Global filter networks for image classification. Adv. Neural Inf. Process. Syst. 34, 980\u2013993 (2021)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"5137_CR35","doi-asserted-by":"crossref","unstructured":"Suvorov, R., Logacheva, E., Mashikhin, A., Remizova, A., Ashukha, A., Silvestrov, A., Kong, N., Goka, H., Park, K., Lempitsky, V.: Resolution-robust large mask inpainting with fourier convolutions. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 2149\u20132159 (2022)","DOI":"10.1109\/WACV51458.2022.00323"},{"key":"5137_CR36","doi-asserted-by":"crossref","unstructured":"Wang, C., Wu, H., Jin, Z.: Fourllie: Boosting low-light image enhancement by fourier frequency information. In: Proceedings of the 31st ACM International Conference on Multimedia, pp. 7459\u20137469 (2023)","DOI":"10.1145\/3581783.3611909"},{"key":"5137_CR37","unstructured":"Huang, J., Zhou, M., Li, D., Li, B., Guo, C.-L., Li, C.: Revitalizing Channel-dimension Fourier Transform for Image Enhancement (2024). https:\/\/openreview.net\/forum?id=3tjTJeXyA7"},{"key":"5137_CR38","doi-asserted-by":"publisher","first-page":"5501","DOI":"10.1109\/JSTARS.2021.3074508","volume":"14","author":"T Tian","year":"2021","unstructured":"Tian, T., Li, L., Chen, W., Zhou, H.: Semsdnet: a multiscale dense network with attention for remote sensing scene classification. IEEE J. Sel. Top. Appl. Earth Obs. and Remote Sens. 14, 5501\u20135514 (2021)","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. and Remote Sens."},{"issue":"24","key":"5137_CR39","doi-asserted-by":"publisher","first-page":"5076","DOI":"10.3390\/rs13245076","volume":"13","author":"D Wang","year":"2021","unstructured":"Wang, D., Lan, J.: A deformable convolutional neural network with spatial-channel attention for remote sensing scene classification. Remote Sens. 13(24), 5076 (2021)","journal-title":"Remote Sens."},{"issue":"9","key":"5137_CR40","doi-asserted-by":"publisher","first-page":"2042","DOI":"10.3390\/rs14092042","volume":"14","author":"J Shen","year":"2022","unstructured":"Shen, J., Yu, T., Yang, H., Wang, R., Wang, Q.: An attention cascade global-local network for remote sensing scene classification. Remote Sens. 14(9), 2042 (2022)","journal-title":"Remote Sens."},{"key":"5137_CR41","doi-asserted-by":"crossref","unstructured":"Hou, Q., Zhou, D., Feng, J.: Coordinate attention for efficient mobile network design. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 13713\u201313722 (2021)","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"5137_CR42","doi-asserted-by":"crossref","unstructured":"Frigo, M., Johnson, S.G.: Fftw: An adaptive software architecture for the fft. In: Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP\u201998 (Cat. No. 98CH36181), 3, 1381\u20131384 (1998). IEEE","DOI":"10.1109\/ICASSP.1998.681704"},{"key":"5137_CR43","doi-asserted-by":"crossref","unstructured":"Zhou, M., Huang, J., Yan, K., Yu, H., Fu, X., Liu, A., Wei, X., Zhao, F.: Spatial-frequency domain information integration for pan-sharpening. In: European Conference on Computer Vision, pp. 274\u2013291 (2022). Springer","DOI":"10.1007\/978-3-031-19797-0_16"},{"key":"5137_CR44","doi-asserted-by":"crossref","unstructured":"Xu, H., Ma, J., Le, Z., Jiang, J., Guo, X.: Fusiondn: A unified densely connected network for image fusion. In: Proceedings of the AAAI Conference on Artificial Intelligence, 34, 12484\u201312491 (2020)","DOI":"10.1609\/aaai.v34i07.6936"},{"key":"5137_CR45","doi-asserted-by":"crossref","unstructured":"Liang, P., Jiang, J., Liu, X., Ma, J.: Fusion from decomposition: A self-supervised decomposition approach for image fusion. In: European Conference on Computer Vision, pp. 719\u2013735 (2022). Springer","DOI":"10.1007\/978-3-031-19797-0_41"},{"key":"5137_CR46","doi-asserted-by":"crossref","unstructured":"Zhao, W., Xie, S., Zhao, F., He, Y., Lu, H.: Metafusion: Infrared and visible image fusion via meta-feature embedding from object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 13955\u201313965 (2023)","DOI":"10.1109\/CVPR52729.2023.01341"},{"issue":"9","key":"5137_CR47","doi-asserted-by":"publisher","first-page":"11040","DOI":"10.1109\/TPAMI.2023.3268209","volume":"45","author":"H Li","year":"2023","unstructured":"Li, H., Xu, T., Wu, X.-J., Lu, J., Kittler, J.: Lrrnet: a novel representation learning guided fusion network for infrared and visible images. IEEE Trans. on Pattern Anal. and Mach. Intell. 45(9), 11040\u201311052 (2023)","journal-title":"IEEE Trans. on Pattern Anal. and Mach. Intell."},{"key":"5137_CR48","doi-asserted-by":"crossref","unstructured":"Liu, J., Liu, Z., Wu, G., Ma, L., Liu, R., Zhong, W., Luo, Z., Fan, X.: Multi-interactive feature learning and a full-time multi-modality benchmark for image fusion and segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 8115\u20138124 (2023)","DOI":"10.1109\/ICCV51070.2023.00745"}],"container-title":["Cluster Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-025-05137-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10586-025-05137-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-025-05137-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T17:43:01Z","timestamp":1757439781000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10586-025-05137-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,31]]},"references-count":48,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2025,9]]}},"alternative-id":["5137"],"URL":"https:\/\/doi.org\/10.1007\/s10586-025-05137-y","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":"18 September 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 January 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 January 2025","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 that they have no Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"444"}}