{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,31]],"date-time":"2025-03-31T04:17:01Z","timestamp":1743394621547,"version":"3.40.3"},"reference-count":62,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2024,11,26]],"date-time":"2024-11-26T00:00:00Z","timestamp":1732579200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,26]],"date-time":"2024-11-26T00:00:00Z","timestamp":1732579200000},"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":"publisher","award":["62067002, 61967006, 62062033","62067002, 61967006, 62062033","62067002, 61967006, 62062033","62067002, 61967006, 62062033"],"award-info":[{"award-number":["62067002, 61967006, 62062033","62067002, 61967006, 62062033","62067002, 61967006, 62062033","62067002, 61967006, 62062033"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Science and Technology Programme, Jiangxi Provincial Department of Transportation","award":["2022X0040","2022X0040","2022X0040","2022X0040"],"award-info":[{"award-number":["2022X0040","2022X0040","2022X0040","2022X0040"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cluster Comput"],"published-print":{"date-parts":[[2025,4]]},"DOI":"10.1007\/s10586-024-04836-2","type":"journal-article","created":{"date-parts":[[2024,11,26]],"date-time":"2024-11-26T19:27:55Z","timestamp":1732649275000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Filter pruning via annealing decaying for deep convolutional neural networks acceleration"],"prefix":"10.1007","volume":"28","author":[{"given":"Jiawen","family":"Huang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liyan","family":"Xiong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaohui","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qingsen","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peng","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,11,26]]},"reference":[{"key":"4836_CR1","unstructured":"Chen, T., Moreau, T., Jiang, Z., Zheng, L., Yan, E., Cowan, M., Shen, H., Wang, L., Hu, Y., Ceze, L., et\u00a0al.: Tvm: an automated end-to-end optimizing compiler for deep learning. In: Proceedings of the USENIX Conference on Operating Systems Design and Implementation, pp. 579\u2013594 (2018)"},{"key":"4836_CR2","unstructured":"Jiang, X., Wang, H., Chen, Y., Wu, Z., Wang, L., Zou, B., Yang, Y., Cui, Z., Cai, Y., Yu, T., et\u00a0al.: Mnn: A universal and efficient inference engine. In: Proceedings of Machine Learning and Systems, pp. 1\u201313 (2020)"},{"key":"4836_CR3","unstructured":"Dillon, J.V., Langmore, I., Tran, D., Brevdo, E., Vasudevan, S., Moore, D., Patton, B., Alemi, A., Hoffman, M., Saurous, R.A.: Tensorflow distributions. arXiv preprint (2017)"},{"key":"4836_CR4","doi-asserted-by":"crossref","unstructured":"Dong, X., Huang, J., Yang, Y., Yan, S.: More is less: A more complicated network with less inference complexity. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5840\u20135848 (2017)","DOI":"10.1109\/CVPR.2017.205"},{"key":"4836_CR5","unstructured":"Han, S., Pool, J., Tran, J., Dally, W.: Learning both weights and connections for efficient neural network 28 (2015)"},{"key":"4836_CR6","doi-asserted-by":"crossref","unstructured":"Lin, M., Ji, R., Wang, Y., Zhang, Y., Zhang, B., Tian, Y., Shao, L.: Hrank: Filter pruning using high-rank feature map. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1529\u20131538 (2020)","DOI":"10.1109\/CVPR42600.2020.00160"},{"key":"4836_CR7","doi-asserted-by":"crossref","unstructured":"Ruan, X., Liu, Y., Li, B., Yuan, C., Hu, W.: Dpfps: Dynamic and progressive filter pruning for compressing convolutional neural networks from scratch. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 2495\u20132503 (2022)","DOI":"10.1609\/aaai.v35i3.16351"},{"key":"4836_CR8","unstructured":"Liu, S., Wang, K., Yang, X., Ye, J., Wang, X.: Dataset distillation via factorization. Adv. Neural Inf. Process. Syst. 35 (2022)"},{"key":"4836_CR9","doi-asserted-by":"crossref","unstructured":"Yang, X., Ye, J., Wang, X.: Factorizing knowledge in neural networks. In: European Conference on Computer Vision, pp. 73\u201391 (2022)","DOI":"10.1007\/978-3-031-19830-4_5"},{"key":"4836_CR10","doi-asserted-by":"crossref","unstructured":"Ye, J., Liu, S., Wang, X.: Partial network cloning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 20137\u201320146 (2023)","DOI":"10.1109\/CVPR52729.2023.01928"},{"key":"4836_CR11","doi-asserted-by":"crossref","unstructured":"Fang, G., Ma, X., Song, M., Mi, M.B., Wang, X.: Depgraph: Towards any structural pruning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 16091\u201316101 (2023)","DOI":"10.1109\/CVPR52729.2023.01544"},{"key":"4836_CR12","doi-asserted-by":"crossref","unstructured":"Ding, X., Hao, T., Tan, J., Liu, J., Han, J., Guo, Y., Ding, G.: Resrep: Lossless cnn pruning via decoupling remembering and forgetting. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 4510\u20134520 (2021)","DOI":"10.1109\/ICCV48922.2021.00447"},{"key":"4836_CR13","doi-asserted-by":"crossref","unstructured":"Gao, S., Huang, F., Cai, W., Huang, H.: Network pruning via performance maximization. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9270\u20139280 (2021)","DOI":"10.1109\/CVPR46437.2021.00915"},{"key":"4836_CR14","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1016\/j.neucom.2021.07.045","volume":"461","author":"T Liang","year":"2021","unstructured":"Liang, T., Glossner, J., Wang, L., Shi, S., Zhang, X.: Pruning and quantization for deep neural network acceleration: A survey. Neurocomputing 461, 370\u2013403 (2021)","journal-title":"Neurocomputing"},{"key":"4836_CR15","doi-asserted-by":"crossref","unstructured":"Jing, Y., Yang, Y., Wang, X., Song, M., Tao, D.: Meta-aggregator: Learning to aggregate for 1-bit graph neural networks. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 5301\u20135310 (2021)","DOI":"10.1109\/ICCV48922.2021.00525"},{"key":"4836_CR16","doi-asserted-by":"crossref","unstructured":"Liu, S., Ye, J., Yu, R., Wang, X.: Slimmable dataset condensation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3759\u20133768 (2023)","DOI":"10.1109\/CVPR52729.2023.00366"},{"key":"4836_CR17","unstructured":"Sehwag, V., Wang, S., Mittal, P., Jana, S.: Hydra: Pruning adversarially robust neural networks. Adv. Neural Inf. Process. Syst. 33 (2020)"},{"key":"4836_CR18","unstructured":"Dong, X., Chen, S., Pan, S.: Learning to prune deep neural networks via layer-wise optimal brain surgeon. Adv. Neural Inf. Process. Syst. 30 (2017)"},{"key":"4836_CR19","unstructured":"Guo, Y., Yao, A., Chen, Y.: Dynamic network surgery for efficient dnns. Adv. Neural Inf. Process. Syst. 29 (2016)"},{"key":"4836_CR20","unstructured":"Park, S., Lee, J., Mo, S., Shin, J.: Lookahead: a far-sighted alternative of magnitude-based pruning. arXiv preprint (2020)"},{"key":"4836_CR21","unstructured":"Li, H., Kadav, A., Durdanovic, I., Samet, H., Graf, H.P.: Pruning filters for efficient convnets. arXiv preprint (2016)"},{"key":"4836_CR22","unstructured":"You, Z., Yan, K., Ye, J., Ma, M., Wang, P.: Gate decorator: Global filter pruning method for accelerating deep convolutional neural networks. Adv. Neural Inf. Process. Syst. 32 (2019)"},{"key":"4836_CR23","doi-asserted-by":"crossref","unstructured":"Ding, X., Ding, G., Guo, Y., Han, J.: Centripetal sgd for pruning very deep convolutional networks with complicated structure. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4943\u20134953 (2019)","DOI":"10.1109\/CVPR.2019.00508"},{"key":"4836_CR24","unstructured":"Lee, J., Park, S., Mo, S., Ahn, S., Shin, J.: Layer-adaptive sparsity for the magnitude-based pruning. arXiv preprint (2020)"},{"key":"4836_CR25","doi-asserted-by":"crossref","unstructured":"Wei, S., Ye, T., Zhang, S., Tang, Y., Liang, J.: Joint token pruning and squeezing towards more aggressive compression of vision transformers. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2092\u20132101 (2023)","DOI":"10.1109\/CVPR52729.2023.00208"},{"key":"4836_CR26","doi-asserted-by":"crossref","unstructured":"He, Y., Zhang, X., Sun, J.: Channel pruning for accelerating very deep neural networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1389\u20131397 (2017)","DOI":"10.1109\/ICCV.2017.155"},{"key":"4836_CR27","doi-asserted-by":"crossref","unstructured":"Luo, J.-H., Wu, J., Lin, W.: Thinet: A filter level pruning method for deep neural network compression. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 5058\u20135066 (2017)","DOI":"10.1109\/ICCV.2017.541"},{"key":"4836_CR28","doi-asserted-by":"crossref","unstructured":"He, Y., Liu, P., Wang, Z., Hu, Z., Yang, Y.: Filter pruning via geometric median for deep convolutional neural networks acceleration. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4340\u20134349 (2019)","DOI":"10.1109\/CVPR.2019.00447"},{"key":"4836_CR29","doi-asserted-by":"crossref","unstructured":"He, Y., Ding, Y., Liu, P., Zhu, L., Zhang, H., Yang, Y.: Learning filter pruning criteria for deep convolutional neural networks acceleration. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2009\u20132018 (2020)","DOI":"10.1109\/CVPR42600.2020.00208"},{"key":"4836_CR30","doi-asserted-by":"crossref","unstructured":"He, Y., Kang, G., Dong, X., Fu, Y., Yang, Y.: Soft filter pruning for accelerating deep convolutional neural networks. In: Proceedings of the International Joint Conference on Artificial Intelligence, pp. 2234\u20132240 (2018)","DOI":"10.24963\/ijcai.2018\/309"},{"issue":"12","key":"4836_CR31","doi-asserted-by":"crossref","first-page":"13293","DOI":"10.1109\/TCYB.2021.3130047","volume":"52","author":"X Wang","year":"2022","unstructured":"Wang, X., Zheng, Z., He, Y., Yan, F., Zeng, Z., Yang, Y.: Soft person reidentification network pruning via blockwise adjacent filter decaying. IEEE Trans. Cybern. 52(12), 13293\u201313307 (2022)","journal-title":"IEEE Trans. Cybern."},{"key":"4836_CR32","unstructured":"Han, S., Mao, H., Dally, W.J.: Deep compression: compressing deep neural networks with pruning, trained quantization and huffman coding. arXiv preprint (2015)"},{"key":"4836_CR33","unstructured":"Molchanov, D., Ashukha, A., Vetrov, D.: Variational dropout sparsifies deep neural networks. In: International Conference on Machine Learning, pp. 2498\u20132507 (2017)"},{"key":"4836_CR34","doi-asserted-by":"crossref","unstructured":"Liu, Z., Li, J., Shen, Z., Huang, G., Yan, S., Zhang, C.: Learning efficient convolutional networks through network slimming. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2736\u20132744 (2017)","DOI":"10.1109\/ICCV.2017.298"},{"key":"4836_CR35","unstructured":"Ding, X., Ding, G., Guo, Y., Han, J., Yan, C.: Approximated oracle filter pruning for destructive cnn width optimization. In: International Conference on Machine Learning, pp. 1607\u20131616 (2019)"},{"key":"4836_CR36","doi-asserted-by":"crossref","unstructured":"Lin, S., Ji, R., Li, Y., Wu, Y., Huang, F., Zhang, B.: Accelerating convolutional networks via global & dynamic filter pruning. In: Proceedings of the International Joint Conference on Artificial Intelligence, pp. 2425\u20132432 (2018)","DOI":"10.24963\/ijcai.2018\/336"},{"key":"4836_CR37","doi-asserted-by":"crossref","unstructured":"Li, T., Wu, B., Yang, Y., Fan, Y., Zhang, Y., Liu, W.: Compressing convolutional neural networks via factorized convolutional filters. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3977\u20133986 (2019)","DOI":"10.1109\/CVPR.2019.00410"},{"key":"4836_CR38","doi-asserted-by":"crossref","unstructured":"Lin, M., Ji, R., Zhang, Y., Zhang, B., Wu, Y., Tian, Y.: Channel pruning via automatic structure search. In: Proceedings of the International Conference on International Joint Conferences on Artificial Intelligence, pp. 673\u2013679 (2021)","DOI":"10.24963\/ijcai.2020\/94"},{"key":"4836_CR39","unstructured":"Yang, H., Wen, W., Li, H.: Deephoyer: Learning sparser neural network with differentiable scale-invariant sparsity measures. arXiv preprint (2019)"},{"key":"4836_CR40","unstructured":"Zhuang, T., Zhang, Z., Huang, Y., Zeng, X., Shuang, K., Li, X.: Neuron-level structured pruning using polarization regularizer. Adv. Neural Inf. Process. Syst. 33 (2020)"},{"key":"4836_CR41","unstructured":"Guo, F.-M., Liu, S., Mungall, F.S., Lin, X., Wang, Y.: Reweighted proximal pruning for large-scale language representation. arXiv preprint (2019)"},{"key":"4836_CR42","doi-asserted-by":"crossref","unstructured":"Luo, J.-H., Wu, J.: Neural network pruning with residual-connections and limited-data. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1458\u20131467 (2020)","DOI":"10.1109\/CVPR42600.2020.00153"},{"key":"4836_CR43","unstructured":"Nayman, N., Noy, A., Ridnik, T., Friedman, I., Jin, R., Zelnik, L.: Xnas: Neural architecture search with expert advice. Adv. Neural Inf. Process. Syst. 32 (2019)"},{"key":"4836_CR44","unstructured":"Baykal, C., Liebenwein, L., Gilitschenski, I., Feldman, D., Rus, D.: Sipping neural networks: Sensitivity-informed provable pruning of neural networks. arXiv preprint (2019)"},{"key":"4836_CR45","unstructured":"Adamczewski, K., Park, M.: Dirichlet pruning for neural network compression. In: Proceedings of the International Conference on Artificial Intelligence and Statistics, pp. 3637\u20133645 (2021)"},{"key":"4836_CR46","doi-asserted-by":"crossref","unstructured":"Zhou, S., Wang, Y., Chen, D., Chen, J., Wang, X., Wang, C., Bu, J.: Distilling holistic knowledge with graph neural networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 10387\u201310396 (2021)","DOI":"10.1109\/ICCV48922.2021.01022"},{"key":"4836_CR47","doi-asserted-by":"crossref","unstructured":"Li, Y., Gu, S., Mayer, C., Gool, L.V., Timofte, R.: Group sparsity: The hinge between filter pruning and decomposition for network compression. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8018\u20138027 (2020)","DOI":"10.1109\/CVPR42600.2020.00804"},{"key":"4836_CR48","doi-asserted-by":"crossref","unstructured":"Li, Y., Li, W., Danelljan, M., Zhang, K., Gu, S., Van\u00a0Gool, L., Timofte, R.: The heterogeneity hypothesis: Finding layer-wise differentiated network architectures. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2144\u20132153 (2021)","DOI":"10.1109\/CVPR46437.2021.00218"},{"key":"4836_CR49","doi-asserted-by":"crossref","unstructured":"Wang, K., Liu, Z., Lin, Y., Lin, J., Han, S.: Haq: Hardware-aware automated quantization with mixed precision. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8612\u20138620 (2019)","DOI":"10.1109\/CVPR.2019.00881"},{"key":"4836_CR50","doi-asserted-by":"crossref","unstructured":"Cai, L., An, Z., Yang, C., Yan, Y., Xu, Y.: Prior gradient mask guided pruning-aware fine-tuning. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 140\u2013148 (2022)","DOI":"10.1609\/aaai.v36i1.19888"},{"key":"4836_CR51","first-page":"127","volume":"572","author":"P Zhang","year":"2024","unstructured":"Zhang, P., Tian, C., Zhao, L., Duan, Z.: A multi-granularity CNN pruning framework via deformable soft mask with joint training. Neurocomputing 572, 127\u2013189 (2024)","journal-title":"Neurocomputing"},{"key":"4836_CR52","doi-asserted-by":"crossref","unstructured":"Elkerdawy, S., Elhoushi, M., Zhang, H., Ray, N.: Fire together wire together: A dynamic pruning approach with self-supervised mask prediction. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12454\u201312463 (2022)","DOI":"10.1109\/CVPR52688.2022.01213"},{"issue":"1","key":"4836_CR53","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1007\/s10489-023-05207-x","volume":"54","author":"Y Xue","year":"2024","unstructured":"Xue, Y., Yao, W., Peng, S., Yao, S.: Automatic filter pruning algorithm for image classification. Appl. Intel. 54(1), 216\u2013230 (2024)","journal-title":"Appl. Intel."},{"issue":"11","key":"4836_CR54","doi-asserted-by":"crossref","first-page":"9139","DOI":"10.1109\/TNNLS.2022.3156047","volume":"34","author":"M Lin","year":"2023","unstructured":"Lin, M., Cao, L., Zhang, Y., Shao, L., Lin, C., Ji, R.: Pruning networks with cross-layer ranking & k-reciprocal nearest filters. IEEE Trans. Neural Netw. Learn. Syst. 34(11), 9139\u20139148 (2023)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"1","key":"4836_CR55","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1007\/s10489-023-05164-5","volume":"54","author":"Z Dong","year":"2024","unstructured":"Dong, Z., Duan, Y., Zhou, Y., Duan, S., Hu, X.: Weight-adaptive channel pruning for CNNs based on closeness-centrality modeling. Appl. Intel. 54(1), 201\u2013215 (2024)","journal-title":"Appl. Intel."},{"key":"4836_CR56","unstructured":"Ye, J., Lu, X., Lin, Z., Wang, J.Z.: Rethinking the smaller-norm-less-informative assumption in channel pruning of convolution layers. arXiv preprint (2018)"},{"key":"4836_CR57","volume-title":"Learning multiple layers of features from tiny images","author":"A Krizhevsky","year":"2009","unstructured":"Krizhevsky, A.: Learning multiple layers of features from tiny images. University of Tront, Tront (2009)"},{"issue":"3","key":"4836_CR58","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, L.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vision 115(3), 211\u2013252 (2015)","journal-title":"Int. J. Comput. Vision"},{"key":"4836_CR59","unstructured":"Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., Lerer, A.: Automatic differentiation in pytorch. Neural Inf. Process. Syst. 30 (2017)"},{"key":"4836_CR60","unstructured":"Sui, Y., Yin, M., Xie, Y., Phan, H., Aliari\u00a0Zonouz, S., Yuan, B.: Chip: Channel independence-based pruning for compact neural networks. Adv. Neural Inf. Process. Syst. 34 (2021)"},{"issue":"3","key":"4836_CR61","doi-asserted-by":"crossref","first-page":"4125","DOI":"10.1109\/TNNLS.2022.3201846","volume":"35","author":"P Lei","year":"2024","unstructured":"Lei, P., Liang, J., Zheng, T., Wang, J.: Compression of convolutional neural networks with divergent representation of filters. IEEE Trans. Neural Netw. Learn. Syst. 35(3), 4125\u20134137 (2024)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"4836_CR62","doi-asserted-by":"crossref","unstructured":"Liu, Z., Mu, H., Zhang, X., Guo, Z., Yang, X., Cheng, K.-T., Sun, J.: Metapruning: Meta learning for automatic neural network channel pruning. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3296\u20133305 (2019)","DOI":"10.1109\/ICCV.2019.00339"}],"container-title":["Cluster Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-024-04836-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10586-024-04836-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-024-04836-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,30]],"date-time":"2025-03-30T16:34:59Z","timestamp":1743352499000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10586-024-04836-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,26]]},"references-count":62,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,4]]}},"alternative-id":["4836"],"URL":"https:\/\/doi.org\/10.1007\/s10586-024-04836-2","relation":{},"ISSN":["1386-7857","1573-7543"],"issn-type":[{"type":"print","value":"1386-7857"},{"type":"electronic","value":"1573-7543"}],"subject":[],"published":{"date-parts":[[2024,11,26]]},"assertion":[{"value":"9 March 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 October 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 October 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 November 2024","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 there are no potential Conflict of interest or conflicting relationships in this study","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Relevant ethical guidelines and regulations were followed in conducting this study.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}],"article-number":"124"}}