{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,18]],"date-time":"2026-04-18T06:40:11Z","timestamp":1776494411207,"version":"3.51.2"},"reference-count":68,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T00:00:00Z","timestamp":1773014400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T00:00:00Z","timestamp":1773014400000},"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":["Int J Comput Vis"],"published-print":{"date-parts":[[2026,4]]},"DOI":"10.1007\/s11263-026-02730-5","type":"journal-article","created":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T17:27:35Z","timestamp":1773077255000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Towards Generalized Image Coding for Machine Through Meta Adversarial Adaptation"],"prefix":"10.1007","volume":"134","author":[{"given":"Xuelin","family":"Shen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kangsheng","family":"Yin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenhan","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,3,9]]},"reference":[{"key":"2730_CR1","unstructured":"Ball\u00e9, J., Laparra, V., & Simoncelli, E.P. (2017) End-to-end optimized image compression. In: International Conference on Learning Representations"},{"key":"2730_CR2","unstructured":"Ball\u00e9, J., Minnen, D., Singh, S., et al. (2018). Variational image compression with a scale hyperprior. In: International Conference on Learning Representations"},{"key":"2730_CR3","doi-asserted-by":"crossref","unstructured":"Blau, Y., & Michaeli, T. (2018). The perception-distortion tradeoff. In: IEEE Conference on Computer Vision and Pattern Recognition","DOI":"10.1109\/CVPR.2018.00652"},{"issue":"10","key":"2730_CR4","doi-asserted-by":"publisher","first-page":"3736","DOI":"10.1109\/TCSVT.2021.3101953","volume":"31","author":"B Bross","year":"2021","unstructured":"Bross, B., Wang, Y., Ye, Y., et al. (2021). Overview of the versatile video coding (VVC) standard and its applications. IEEE Transactions on Circuits and Systems for Video Technology, 31(10), 3736\u20133764.","journal-title":"IEEE Transactions on Circuits and Systems for Video Technology"},{"key":"2730_CR5","doi-asserted-by":"crossref","unstructured":"Chamain, D., Racap\u00e9, F.J. B. et al. (2021a) End-to-end optimized image compression for machines, a study. In: Data Compression Conference, pp 163\u2013172","DOI":"10.1109\/DCC50243.2021.00024"},{"key":"2730_CR6","doi-asserted-by":"crossref","unstructured":"Chamain, L., Racap\u00e9, F., B\u00e9gaint, J., et al. (2021b). End-to-end optimized image compression for machines, a study. In: Data Compression Conference, pp 163\u2013172","DOI":"10.1109\/DCC50243.2021.00024"},{"key":"2730_CR7","doi-asserted-by":"crossref","unstructured":"Chen, F., Xu, Y., Wang, L. (2022). Two-stage octave residual network for end-to-end image compression. In: AAAI Conference on Artificial Intelligence, pp 3922\u20133929","DOI":"10.1609\/aaai.v36i4.20308"},{"key":"2730_CR8","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Zhu, Y., Papandreou, G., et al. (2018). Encoder-decoder with atrous separable convolution for semantic image segmentation. In: European conference on computer vision, pp 801\u2013818","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"2730_CR9","doi-asserted-by":"crossref","unstructured":"Chen, Y.H., Weng, Y.C., Kao, C.H., et al. (2023). Transtic: Transferring transformer-based image compression from human perception to machine perception. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp 23297\u201323307","DOI":"10.1109\/ICCV51070.2023.02129"},{"key":"2730_CR10","doi-asserted-by":"crossref","unstructured":"Chen, Z., Fan, K., Wang, S., et al. (2019). Lossy intermediate deep learning feature compression and evaluation. In: Proceedings of the 27th ACM International Conference on Multimedia, pp 2414\u20132422","DOI":"10.1145\/3343031.3350849"},{"key":"2730_CR11","doi-asserted-by":"publisher","first-page":"2230","DOI":"10.1109\/TIP.2019.2941660","volume":"29","author":"Z Chen","year":"2020","unstructured":"Chen, Z., Fan, K., Wang, S., et al. (2020). Toward intelligent sensing: Intermediate deep feature compression. IEEE Transactions on Image Processing, 29, 2230\u20132243.","journal-title":"IEEE Transactions on Image Processing"},{"key":"2730_CR12","unstructured":"Cheng, M., Le, T., Chen, P.Y., et al. (2018). Query-efficient hard-label black-box attack: An optimization-based approach. In: International Conference on Learning Representations"},{"key":"2730_CR13","unstructured":"Cheng, S., Dong, Y., Pang, T., et al. (2019). Improving black-box adversarial attacks with a transfer-based prior. Advances in neural information processing systems 32"},{"key":"2730_CR14","doi-asserted-by":"crossref","unstructured":"Cheng, Z., Sun, H., Takeuchi, M., et al. (2020). Learned image compression with discretized gaussian mixture likelihoods and attention modules. In: IEEE\/CVF conference on computer vision and pattern recognition, pp 7939\u20137948","DOI":"10.1109\/CVPR42600.2020.00796"},{"key":"2730_CR15","doi-asserted-by":"publisher","first-page":"2739","DOI":"10.1109\/TIP.2022.3160602","volume":"31","author":"H Choi","year":"2022","unstructured":"Choi, H., & Baji\u0107, I. V. (2022). Scalable image coding for humans and machines. IEEE Transactions on Image Processing, 31, 2739\u20132754.","journal-title":"IEEE Transactions on Image Processing"},{"key":"2730_CR16","doi-asserted-by":"crossref","unstructured":"Choi, J. & Han, B. (2020). Task-aware quantization network for jpeg image compression. In: IEEE European Conf. Computer Vision, pp 309\u2013324","DOI":"10.1007\/978-3-030-58565-5_19"},{"key":"2730_CR17","doi-asserted-by":"crossref","unstructured":"Dong, Y., Su, H., Wu, B., et al. (2019). Efficient decision-based black-box adversarial attacks on face recognition. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 7714\u20137722","DOI":"10.1109\/CVPR.2019.00790"},{"key":"2730_CR18","doi-asserted-by":"crossref","unstructured":"Duan, K., Bai, S., Xie, L., et al. (2019). Centernet: Keypoint triplets for object detection. In: IEEE\/CVF international conference on computer vision, pp 6569\u20136578","DOI":"10.1109\/ICCV.2019.00667"},{"key":"2730_CR19","doi-asserted-by":"publisher","first-page":"8680","DOI":"10.1109\/TIP.2020.3016485","volume":"29","author":"L Duan","year":"2020","unstructured":"Duan, L., Liu, J., Yang, W., et al. (2020). Video coding for machines: A paradigm of collaborative compression and intelligent analytics. IEEE Transactions on Image Processing, 29, 8680\u20138695.","journal-title":"IEEE Transactions on Image Processing"},{"key":"2730_CR20","unstructured":"Dupont, E., Golinski, A., Alizadeh, M. et al. (2021). Coin: Compression with implicit neural representations. In: The International Conference on Learning Representations (ICLR)"},{"key":"2730_CR21","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1007\/s11263-014-0733-5","volume":"111","author":"M Everingham","year":"2015","unstructured":"Everingham, M., Eslami, S., Gool, L. V., et al. (2015). The pascal visual object classes challenge: A retrospective. International journal of computer vision, 111, 98\u2013136.","journal-title":"International journal of computer vision"},{"key":"2730_CR22","unstructured":"Farhadi, A. & Redmon, J. (2018). Yolov3: An incremental improvement. In: IEEE conference on computer vision and pattern recognition, pp 1\u20136"},{"key":"2730_CR23","doi-asserted-by":"crossref","unstructured":"Gao, H., Gan, W., Sun, Z. et al. (2023). Sinco: A novel structural regularizer for image compression using implicit neural representations. In: IEEE International Conference on Acoustics, Speech and Signal Processing, pp 1\u20135","DOI":"10.1109\/ICASSP49357.2023.10095531"},{"key":"2730_CR24","first-page":"20","volume":"1050","author":"IJ Goodfellow","year":"2015","unstructured":"Goodfellow, I. J., Shlens, J., & Szegedy, C. (2015). Explaining and harnessing adversarial examples. stat, 1050, 20.","journal-title":"Explaining and harnessing adversarial examples. stat"},{"issue":"4","key":"2730_CR25","doi-asserted-by":"publisher","first-page":"2329","DOI":"10.1109\/TCSVT.2021.3089491","volume":"32","author":"Z Guo","year":"2021","unstructured":"Guo, Z., Zhang, Z., Feng, R., et al. (2021). Causal contextual prediction for learned image compression. IEEE Transactions on Circuits and Systems for Video Technology, 32(4), 2329\u20132341.","journal-title":"IEEE Transactions on Circuits and Systems for Video Technology"},{"key":"2730_CR26","doi-asserted-by":"crossref","unstructured":"He, D., Zheng, Y., Sun, B. et al. (2021). Checkerboard context model for efficient learned image compression. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 14771\u201314780","DOI":"10.1109\/CVPR46437.2021.01453"},{"key":"2730_CR27","doi-asserted-by":"crossref","unstructured":"He, D., Yang, Z., Peng, W. et al. (2022). Elic: Efficient learned image compression with unevenly grouped space-channel contextual adaptive coding. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 5718\u20135727","DOI":"10.1109\/CVPR52688.2022.00563"},{"key":"2730_CR28","doi-asserted-by":"crossref","unstructured":"Huang, Z., Wang, X., Huang, L. et al. (2019). Ccnet: Criss-cross attention for semantic segmentation. In: IEEE\/CVF international conference on computer vision, pp 603\u2013612","DOI":"10.1109\/ICCV.2019.00069"},{"key":"2730_CR29","doi-asserted-by":"crossref","unstructured":"Huang, Z., Jia, C., Wang, S. et al. (2021). Visual analysis motivated rate-distortion model for image coding. In: IEEE International Conference on Multimedia and Expo, pp 1\u20136","DOI":"10.1109\/ICME51207.2021.9428417"},{"key":"2730_CR30","doi-asserted-by":"crossref","unstructured":"Jiang, W. & Wang, R. (2023). Mlic++: Linear complexity multi-reference entropy modeling for learned image compression. arXiv preprint arXiv:2307.15421","DOI":"10.1145\/3581783.3611694"},{"key":"2730_CR31","doi-asserted-by":"crossref","unstructured":"Jiang, W., Yang, J., Zhai, Y., et al. (2023). Mlic: Multi-reference entropy model for learned image compression. In: ACM International Conference on Multimedia, pp 7618\u20137627","DOI":"10.1145\/3581783.3611694"},{"key":"2730_CR32","doi-asserted-by":"crossref","unstructured":"Kirillov, A., Mintun, E., Ravi, N. et al. (2023). Segment anything. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 4015\u20134026","DOI":"10.1109\/ICCV51070.2023.00371"},{"issue":"7","key":"2730_CR33","doi-asserted-by":"publisher","first-page":"1956","DOI":"10.1007\/s11263-020-01316-z","volume":"128","author":"A Kuznetsova","year":"2020","unstructured":"Kuznetsova, A., Rom, H., Alldrin, N., et al. (2020). The open images dataset v4: Unified image classification, object detection, and visual relationship detection at scale. International journal of computer vision, 128(7), 1956\u20131981.","journal-title":"International journal of computer vision"},{"key":"2730_CR34","doi-asserted-by":"crossref","unstructured":"Le, N., Zhang, H., Cricri, F. et al. (2021). Image coding for machines: an end-to-end learned approach. In: IEEE International Conference on Acoustics, Speech and Signal Processing, pp 1590\u20131594","DOI":"10.1109\/ICASSP39728.2021.9414465"},{"key":"2730_CR35","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1016\/j.neucom.2022.05.047","volume":"500","author":"B Li","year":"2022","unstructured":"Li, B., Ye, L., Liang, J., et al. (2022). Region-of-interest and channel attention-based joint optimization of image compression and computer vision. Neurocomputing, 500, 13\u201325.","journal-title":"Neurocomputing"},{"key":"2730_CR36","doi-asserted-by":"crossref","unstructured":"Li, H., Li, S., Ding, S. et al. (2024). Image compression for machine and human vision with spatial-frequency adaptation. In: European Conference on Computer Vision, pp 382\u2013399","DOI":"10.1007\/978-3-031-72983-6_22"},{"key":"2730_CR37","unstructured":"Li, Y., Li, L., Wang, L. et al. (2019). Nattack: Learning the distributions of adversarial examples for an improved black-box attack on deep neural networks. In: International Conference on Machine Learning, pp 3866\u20133876"},{"key":"2730_CR38","doi-asserted-by":"crossref","unstructured":"Lin, H., Chen, B., Zhang, Z. et al. (2023). Deepsvc: Deep scalable video coding for both machine and human vision. In: ACM International Conference on Multimedia, pp 9205\u20139214","DOI":"10.1145\/3581783.3612500"},{"key":"2730_CR39","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Maire, M., Belongie, S. et al. (2014). Microsoft COCO: Common objects in context. In: European Conference on Computer Vision, pp 740\u2013755","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"2730_CR40","unstructured":"Liu, D., Zhang, H. & Xiong, Z. (2019). On the classification-distortion-perception tradeoff. In: Proceedings of the 33rd International Conference on Neural Information Processing Systems, pp 1206\u20131215"},{"key":"2730_CR41","unstructured":"Madry, A., Makelov, A., Schmidt, L. et al. (2017). Towards deep learning models resistant to adversarial attacks. stat 1050:9"},{"key":"2730_CR42","doi-asserted-by":"crossref","unstructured":"Mao, Q., Wang, C., Wang, M. et al. (2023). Scalable face image coding via stylegan prior: Towards compression for human-machine collaborative vision. IEEE Transactions on Image Processing","DOI":"10.1109\/TIP.2023.3343912"},{"key":"2730_CR43","doi-asserted-by":"crossref","unstructured":"Minnen, D. & Singh, S. (2020). Channel-wise autoregressive entropy models for learned image compression. In: IEEE International Conference on Image Processing, pp 3339\u20133343","DOI":"10.1109\/ICIP40778.2020.9190935"},{"key":"2730_CR44","unstructured":"Minnen, D., Ball\u00e9, J. & Toderici, G.D. (2018). Joint autoregressive and hierarchical priors for learned image compression. Advances in neural information processing systems 31"},{"key":"2730_CR45","doi-asserted-by":"crossref","unstructured":"Moosav, S., Fawzi, A. & Frossard, P. (2016). Deepfool: a simple and accurate method to fool deep neural networks. In: IEEE conference on computer vision and pattern recognition, pp 2574\u20132582","DOI":"10.1109\/CVPR.2016.282"},{"key":"2730_CR46","doi-asserted-by":"crossref","unstructured":"Nam, L., Zhang, H., Cricri, F., et al. (2021). Learned image coding for machines: A content-adaptive approach. In: IEEE International Conference on Multimedia and Expo, pp 1\u20136","DOI":"10.1109\/ICME51207.2021.9428224"},{"key":"2730_CR47","doi-asserted-by":"crossref","unstructured":"Pang, J., Chen, K., Shi, J. et al. (2019). Libra r-cnn: Towards balanced learning for object detection. In: IEEE\/CVF conference on computer vision and pattern recognition, pp 821\u2013830","DOI":"10.1109\/CVPR.2019.00091"},{"key":"2730_CR48","unstructured":"Qian, Y., Sun, X., Lin, M. et al. (2021). Entroformer: A transformer-based entropy model for learned image compression. In: International Conference on Learning Representations"},{"key":"2730_CR49","unstructured":"Radford, A., Kim, J.W., Hallacy, C. et al. (2021). Learning transferable visual models from natural language supervision. In: International conference on machine learning, pp 8748\u20138763"},{"issue":"06","key":"2730_CR50","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2017","unstructured":"Ren, S., He, K., Girshick, R., et al. (2017). Faster r-cnn: Towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(06), 1137\u20131149.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"2730_CR51","doi-asserted-by":"crossref","unstructured":"Suzuki, S., Motohiro, T., Kazuya, H. et al. (2019). Image pre-transformation for recognition-aware image compression. In: IEEE International Conference on Image Processing, pp 2686\u20132690","DOI":"10.1109\/ICIP.2019.8803275"},{"key":"2730_CR52","doi-asserted-by":"crossref","unstructured":"Saurabh, S., Sami, A., Nick, J. et al. (2020). End-to-end learning of compressible features. In: IEEE International Conference on Image Processing, pp 3349\u20133353","DOI":"10.1109\/ICIP40778.2020.9190860"},{"key":"2730_CR53","doi-asserted-by":"crossref","unstructured":"Shah, M.A. & Raj, B. (2020). Deriving compact feature representations via annealed contraction. In: IEEE International Conference on Acoustics, Speech and Signal Processing, pp 2068\u20132072","DOI":"10.1109\/ICASSP40776.2020.9054527"},{"key":"2730_CR54","doi-asserted-by":"crossref","unstructured":"Shen, X., Yin, K., Wang, X. et al. (2024). Image coding for analytics via adversarially augmented adaptation. In: IEEE International Conference on Acoustics, Speech and Signal Processing, pp 3605\u20133609","DOI":"10.1109\/ICASSP48485.2024.10447491"},{"key":"2730_CR55","doi-asserted-by":"crossref","unstructured":"Shindo, T., Yamada, K., Watanabe, T., et al. (2024). Image coding for machines with edge information learning using segment anything. In: 2024 IEEE International Conference on Image Processing, pp 3702\u20133708","DOI":"10.1109\/ICIP51287.2024.10647785"},{"key":"2730_CR56","doi-asserted-by":"crossref","unstructured":"Str\u00fampler, Y., Postels, J., Yang, R. et al. (2022). Implicit neural representations for image compression. In: European Conference on Computer Vision, pp 74\u201391","DOI":"10.1007\/978-3-031-19809-0_5"},{"issue":"12","key":"2730_CR57","doi-asserted-by":"publisher","first-page":"1649","DOI":"10.1109\/TCSVT.2012.2221191","volume":"22","author":"GJ Sullivan","year":"2012","unstructured":"Sullivan, G. J., Ohm, J. R., Han, W. J., et al. (2012). Overview of the high efficiency video coding (HEVC) standard. IEEE Transactions on circuits and systems for video technology, 22(12), 1649\u20131668.","journal-title":"IEEE Transactions on circuits and systems for video technology"},{"key":"2730_CR58","unstructured":"Szegedy, C., Zaremba, W., Sutskever, I., et al. (2013). Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199"},{"key":"2730_CR59","doi-asserted-by":"crossref","unstructured":"Wang, C.Y., Bochkovskiy, A., Liao, H.Y.M. (2023). Yolov7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 7464\u20137475","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"2730_CR60","doi-asserted-by":"crossref","unstructured":"Wang, S., Wang, S., Yang, W., et al. (2021a). Teacher-student learning with multi-granularity constraint towards compact facial feature representation. In: IEEE International Conference on Acoustics, Speech and Signal Processing, pp 8503\u20138507","DOI":"10.1109\/ICASSP39728.2021.9413506"},{"key":"2730_CR61","doi-asserted-by":"publisher","first-page":"3169","DOI":"10.1109\/TMM.2021.3094300","volume":"24","author":"S Wang","year":"2021","unstructured":"Wang, S., Wang, S., Yang, W., et al. (2021). Towards analysis-friendly face representation with scalable feature and texture compression. IEEE Transactions on Multimedia, 24, 3169\u20133181.","journal-title":"IEEE Transactions on Multimedia"},{"key":"2730_CR62","doi-asserted-by":"crossref","unstructured":"Xie, C., Zhang, Z., Zhou, Y., et al. (2019). Improving transferability of adversarial examples with input diversity. In: IEEE\/CVF conference on computer vision and pattern recognition, pp 2730\u20132739","DOI":"10.1109\/CVPR.2019.00284"},{"key":"2730_CR63","doi-asserted-by":"crossref","unstructured":"Xie, Y., Cheng, K. & Chen, Q. (2021). Enhanced invertible encoding for learned image compression. In: ACM international conference on multimedia, pp 162\u2013170","DOI":"10.1145\/3474085.3475213"},{"key":"2730_CR64","doi-asserted-by":"crossref","unstructured":"Yin, K., Liu, Q., Shen, X., et al. (2025). Unified coding for both human perception and generalized machine analytics with clip supervision. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 9517\u20139525","DOI":"10.1609\/aaai.v39i9.33031"},{"key":"2730_CR65","doi-asserted-by":"crossref","unstructured":"Zamir, A., Sax, A., Shen, W., et al. (2018). Taskonomy: Disentangling task transfer learning. In: IEEE conference on computer vision and pattern recognition, pp 3712\u20133722","DOI":"10.1109\/CVPR.2018.00391"},{"key":"2730_CR66","doi-asserted-by":"crossref","unstructured":"Zhang, R., Isola, P., Efros, A. et al. (2018). The unreasonable effectiveness of deep features as a perceptual metric. In: IEEE conference on computer vision and pattern recognition, pp 586\u2013595","DOI":"10.1109\/CVPR.2018.00068"},{"key":"2730_CR67","doi-asserted-by":"crossref","unstructured":"Zou, J., Pan, Z., Qiu, J., et al. (2020). Improving the transferability of adversarial examples with resized-diverse-inputs, diversity-ensemble and region fitting. In: European Conference on Computer Vision, pp 563\u2013579","DOI":"10.1007\/978-3-030-58542-6_34"},{"key":"2730_CR68","doi-asserted-by":"crossref","unstructured":"Zou, R., Song, C. & Zhang, Z. (2022). The devil is in the details: Window-based attention for image compression. In: IEEE\/CVF conference on computer vision and pattern recognition, pp 17492\u201317501","DOI":"10.1109\/CVPR52688.2022.01697"}],"container-title":["International Journal of Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-026-02730-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11263-026-02730-5","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-026-02730-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,18]],"date-time":"2026-04-18T05:43:56Z","timestamp":1776491036000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11263-026-02730-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,9]]},"references-count":68,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2026,4]]}},"alternative-id":["2730"],"URL":"https:\/\/doi.org\/10.1007\/s11263-026-02730-5","relation":{},"ISSN":["0920-5691","1573-1405"],"issn-type":[{"value":"0920-5691","type":"print"},{"value":"1573-1405","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,9]]},"assertion":[{"value":"5 April 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 January 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 March 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"177"}}