{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T10:13:20Z","timestamp":1766139200564,"version":"3.41.0"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2025,6,8]],"date-time":"2025-06-08T00:00:00Z","timestamp":1749340800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,6,8]],"date-time":"2025-06-08T00:00:00Z","timestamp":1749340800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100005046","name":"Natural Science Foundation of Heilongjiang Province","doi-asserted-by":"publisher","award":["LH2022E114"],"award-info":[{"award-number":["LH2022E114"]}],"id":[{"id":"10.13039\/501100005046","id-type":"DOI","asserted-by":"publisher"}]},{"name":"2024 Basic Scientific Research funds for colleges and universities in Heilongjiang Province","award":["2024-KYYWF-0553"],"award-info":[{"award-number":["2024-KYYWF-0553"]}]},{"name":"Innovation Incentive Project of Jiamusi Municipal Science and Technology Plan","award":["GY2023JL0002"],"award-info":[{"award-number":["GY2023JL0002"]}]},{"name":"Heilongjiang Provincial Department of Education innovation team project","award":["2024-KYYWF-0625"],"award-info":[{"award-number":["2024-KYYWF-0625"]}]},{"name":"Jiamusi university Education and teaching reform research project","award":["2021JY2-02"],"award-info":[{"award-number":["2021JY2-02"]}]},{"name":"\"East Pole\" Academic Team Project of Jiamusi University","award":["DJXSTD202417"],"award-info":[{"award-number":["DJXSTD202417"]}]},{"name":"Horizontal Project of Jiamusi University","award":["JMSUHXXM2024082101"],"award-info":[{"award-number":["JMSUHXXM2024082101"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"DOI":"10.1007\/s11227-025-07453-x","type":"journal-article","created":{"date-parts":[[2025,6,8]],"date-time":"2025-06-08T16:06:11Z","timestamp":1749398771000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A multi-branch semantic segmentation method for autonomous driving"],"prefix":"10.1007","volume":"81","author":[{"given":"Huaqi","family":"Zhao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhengguang","family":"Lu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Songnan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiang","family":"Peng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guojing","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,6,8]]},"reference":[{"key":"7453_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.123489","volume":"248","author":"G Wang","year":"2024","unstructured":"Wang G, Li H, Li P, Lang X, Feng Y, Ding Z, Xie S (2024) M4sfwd: a multi-faceted synthetic dataset for remote sensing forest wildfires detection. Expert Syst Appl 248:123489","journal-title":"Expert Syst Appl"},{"key":"7453_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.122256","volume":"238","author":"Y Zhou","year":"2024","unstructured":"Zhou Y (2024) A yolo-nl object detector for real-time detection. Expert Syst Appl 238:122256","journal-title":"Expert Syst Appl"},{"key":"7453_CR3","doi-asserted-by":"crossref","unstructured":"Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431\u20133440","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"7453_CR4","doi-asserted-by":"crossref","unstructured":"Mehta S, Rastegari M, Shapiro L, Hajishirzi H (2019) Espnetv2: A light-weight, power efficient, and general purpose convolutional neural network. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9190\u20139200","DOI":"10.1109\/CVPR.2019.00941"},{"key":"7453_CR5","doi-asserted-by":"crossref","unstructured":"Chen L-C, Zhu Y, Papandreou G, Schroff F, Adam H (2018) Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 801\u2013818","DOI":"10.1007\/978-3-030-01234-2_49"},{"issue":"3","key":"7453_CR6","doi-asserted-by":"publisher","first-page":"3448","DOI":"10.1109\/TITS.2022.3228042","volume":"24","author":"H Pan","year":"2022","unstructured":"Pan H, Hong Y, Sun W, Jia Y (2022) Deep dual-resolution networks for real-time and accurate semantic segmentation of traffic scenes. IEEE Trans Intell Trans Syst 24(3):3448\u20133460. https:\/\/doi.org\/10.1109\/TITS.2022.3228042","journal-title":"IEEE Trans Intell Trans Syst"},{"key":"7453_CR7","doi-asserted-by":"publisher","unstructured":"Peng J, Liu Y, Tang S, Hao Y, Chu L, Chen G, Wu Z, Chen Z, Yu Z, Du Y, et al Pp-liteseg: A superior real-time semantic segmentation model. (2022) arXiv preprint arXiv:2204.02681https:\/\/doi.org\/10.48550\/arXiv.2204.02681","DOI":"10.48550\/arXiv.2204.02681"},{"key":"7453_CR8","doi-asserted-by":"crossref","unstructured":"Fan M, Lai S, Huang J, Wei X, Chai Z, Luo J, Wei X (2021) Rethinking bisenet for real-time semantic segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9716\u20139725","DOI":"10.1109\/CVPR46437.2021.00959"},{"key":"7453_CR9","doi-asserted-by":"publisher","unstructured":"Wei H, Liu X, Xu S, Dai Z, Dai Y, Xu X (2022) Dwrseg: Rethinking efficient acquisition of multi-scale contextual information for real-time semantic segmentation. arXiv preprint arXiv:2212.01173https:\/\/doi.org\/10.48550\/arXiv.2212.01173","DOI":"10.48550\/arXiv.2212.01173"},{"key":"7453_CR10","first-page":"1","volume":"19","author":"Z Yang","year":"2022","unstructured":"Yang Z, Zhou D, Yang Y, Zhang J, Chen Z (2022) Transroadnet: a novel road extraction method for remote sensing images via combining high-level semantic feature and context. IEEE Geosci Remote Sens Lett 19:1\u20135","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"7453_CR11","first-page":"8","volume":"4","author":"Z Yang","year":"2024","unstructured":"Yang Z, Zhang W, Li Q, Ni W, Wu J, Wang Q (2024) C 2 net: road extraction via context perception and cross spatial-scale feature interaction. IEEE Geosci Remote Sens Lett 4:8","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"7453_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/LGRS.2023.3330867","volume":"20","author":"Z Yang","year":"2023","unstructured":"Yang Z, Zhou D, Yang Y, Zhang J, Chen Z (2023) Road extraction from satellite imagery by road context and full-stage feature. IEEE Geosci Remote Sens Lett 20:1\u20135","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"7453_CR13","unstructured":"Kenton JDM-WC, Toutanova LK (2019) Bert: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of naacL-HLT, vol. 1, p. 2. Minneapolis, Minnesota"},{"key":"7453_CR14","doi-asserted-by":"publisher","unstructured":"Dosovitskiy A (2020) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929https:\/\/doi.org\/10.48550\/arXiv.2010.11929","DOI":"10.48550\/arXiv.2010.11929"},{"key":"7453_CR15","doi-asserted-by":"crossref","unstructured":"Zheng S, Lu J, Zhao H, Zhu X, Luo Z, Wang Y, Fu Y, Feng J, Xiang T, Torr PH, et al (2021) Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6881\u20136890","DOI":"10.1109\/CVPR46437.2021.00681"},{"key":"7453_CR16","unstructured":"Parmar N, Vaswani A, Uszkoreit J, Kaiser L, Shazeer N, Ku A, Tran D (2018) Image transformer. In: International Conference on Machine Learning, pp. 4055\u20134064 . PMLR"},{"key":"7453_CR17","doi-asserted-by":"crossref","unstructured":"Wu K, Zhang J, Peng H, Liu M, Xiao B, Fu J, Yuan L (2022) Tinyvit: Fast pretraining distillation for small vision transformers. In: European Conference on Computer Vision, pp. 68\u201385 . Springer","DOI":"10.1007\/978-3-031-19803-8_5"},{"key":"7453_CR18","first-page":"12077","volume":"34","author":"E Xie","year":"2021","unstructured":"Xie E, Wang W, Yu Z, Anandkumar A, Alvarez JM, Luo P (2021) Segformer: simple and efficient design for semantic segmentation with transformers. Adv Neural Inf Process Syst 34:12077\u201312090","journal-title":"Adv Neural Inf Process Syst"},{"key":"7453_CR19","doi-asserted-by":"crossref","unstructured":"Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, Lin S, Guo B (2021) Swin transformer: Hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 10012\u201310022","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"7453_CR20","doi-asserted-by":"crossref","unstructured":"Zhang W, Huang Z, Luo G, Chen T, Wang X, Liu W, Yu G, Shen C (2022) Topformer: Token pyramid transformer for mobile semantic segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12083\u201312093","DOI":"10.1109\/CVPR52688.2022.01177"},{"key":"7453_CR21","first-page":"7281","volume":"34","author":"Y Yuan","year":"2021","unstructured":"Yuan Y, Fu R, Huang L, Lin W, Zhang C, Chen X, Wang J (2021) Hrformer: high-resolution vision transformer for dense predict. Adv Neural Inf Process Syst 34:7281\u20137293","journal-title":"Adv Neural Inf Process Syst"},{"key":"7453_CR22","first-page":"7423","volume":"35","author":"J Wang","year":"2022","unstructured":"Wang J, Gou C, Wu Q, Feng H, Han J, Ding E, Wang J (2022) Rtformer: efficient design for real-time semantic segmentation with transformer. Adv Neural Inf Process Syst 35:7423\u20137436","journal-title":"Adv Neural Inf Process Syst"},{"key":"7453_CR23","unstructured":"Wan Q, Huang Z, Lu J, Gang Y, Zhang L (2023) Seaformer: Squeeze-enhanced axial transformer for mobile semantic segmentation. In: The Eleventh International Conference on Learning Representations"},{"key":"7453_CR24","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2024.3409808","author":"C Lin","year":"2024","unstructured":"Lin C, Mao X, Qiu C, Zou L (2024) Dtcnet: transformer-cnn distillation for super-resolution of remote sensing image. IEEE J Sel Top Appl Earth Obs Remote Sens. https:\/\/doi.org\/10.1109\/JSTARS.2024.3409808","journal-title":"IEEE J Sel Top Appl Earth Obs Remote Sens"},{"key":"7453_CR25","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2025.3529532","author":"C Lin","year":"2025","unstructured":"Lin C, Jiang Z, Cong J, Zou L (2025) Rnn with high precision and noise immunity: a robust and learning-free method for beamforming. IEEE Internet of Things J. https:\/\/doi.org\/10.1109\/JIOT.2025.3529532","journal-title":"IEEE Internet of Things J"},{"key":"7453_CR26","unstructured":"Yuan Y, Fu R, Huang L, Lin W, Zhang C, Chen X, Wang J (2021) Hrformer: High-resolution transformer for dense prediction. arXiv preprint arXiv:2110.09408"},{"issue":"9","key":"7453_CR27","doi-asserted-by":"publisher","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","volume":"37","author":"K He","year":"2015","unstructured":"He K, Zhang X, Ren S, Sun J (2015) Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans Pattern Anal Mach Intell 37(9):1904\u20131916. https:\/\/doi.org\/10.1109\/TPAMI.2015.2389824","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"7453_CR28","doi-asserted-by":"publisher","unstructured":"Chen L-C (2017) Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587https:\/\/doi.org\/10.48550\/arXiv.1706.05587","DOI":"10.48550\/arXiv.1706.05587"},{"key":"7453_CR29","doi-asserted-by":"crossref","unstructured":"Hou Q, Zhang L, Cheng M-M, Feng J (2020) Strip pooling: Rethinking spatial pooling for scene parsing. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4003\u20134012","DOI":"10.1109\/CVPR42600.2020.00406"},{"key":"7453_CR30","doi-asserted-by":"crossref","unstructured":"He J, Deng Z, Zhou L, Wang Y, Qiao Y (2019) Adaptive pyramid context network for semantic segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7519\u20137528","DOI":"10.1109\/CVPR.2019.00770"},{"key":"7453_CR31","first-page":"1","volume":"30","author":"A Vaswani","year":"2017","unstructured":"Vaswani A (2017) Attention is all you need. Adv Neural Inf Process Syst 30:1","journal-title":"Adv Neural Inf Process Syst"},{"issue":"5","key":"7453_CR32","doi-asserted-by":"publisher","first-page":"5436","DOI":"10.1109\/TPAMI.2022.3211006","volume":"45","author":"M-H Guo","year":"2022","unstructured":"Guo M-H, Liu Z-N, Mu T-J, Hu S-M (2022) Beyond self-attention: external attention using two linear layers for visual tasks. IEEE Trans Pattern Anal Mach Intell 45(5):5436\u20135447. https:\/\/doi.org\/10.1109\/TPAMI.2022.3211006","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"7453_CR33","doi-asserted-by":"crossref","unstructured":"Wang W, Xie E, Li X, Fan D-P, Song K, Liang D, Lu T, Luo P, Shao L (2021) Pyramid vision transformer: A versatile backbone for dense prediction without convolutions. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 568\u2013578","DOI":"10.1109\/ICCV48922.2021.00061"},{"key":"7453_CR34","doi-asserted-by":"crossref","unstructured":"Ren S, Zhou D, He S, Feng J, Wang X (2022) Shunted self-attention via multi-scale token aggregation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10853\u201310862","DOI":"10.1109\/CVPR52688.2022.01058"},{"key":"7453_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2024.103280","volume":"97","author":"J Chen","year":"2024","unstructured":"Chen J, Mei J, Li X, Lu Y, Yu Q, Wei Q, Luo X, Xie Y, Adeli E, Wang Y, Lungren MP, Zhang S, Xing L, Lu L, Yuille A, Zhou Y (2024) Transunet: rethinking the u-net architecture design for medical image segmentation through the lens of transformers. Med Image Anal 97:103280. https:\/\/doi.org\/10.1016\/j.media.2024.103280","journal-title":"Med Image Anal"},{"key":"7453_CR36","doi-asserted-by":"crossref","unstructured":"Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: Medical Image Computing and Computer Assisted intervention\u2013MICCAI 2021: 24th International Conference, Strasbourg, France, September 27\u2013October 1, 2021, Proceedings, Part I 24, pp. 14\u201324 . Springer","DOI":"10.1007\/978-3-030-87193-2_2"},{"key":"7453_CR37","doi-asserted-by":"crossref","unstructured":"Yin M, Yao Z, Cao Y, Li X, Zhang Z, Lin S, Hu H (2020) Disentangled non-local neural networks. In: Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part XV 16, pp. 191\u2013207 . Springer","DOI":"10.1007\/978-3-030-58555-6_12"},{"key":"7453_CR38","doi-asserted-by":"publisher","unstructured":"Si H, Zhang Z, Lv F, Yu G, Lu F (2019) Real-time semantic segmentation via multiply spatial fusion network. arXiv preprint arXiv:1911.07217https:\/\/doi.org\/10.48550\/arXiv.1911.07217","DOI":"10.48550\/arXiv.1911.07217"},{"key":"7453_CR39","doi-asserted-by":"crossref","unstructured":"Xu J, Xiong Z, Bhattacharyya SP (2023) Pidnet: A real-time semantic segmentation network inspired by pid controllers. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 19529\u201319539","DOI":"10.1109\/CVPR52729.2023.01871"},{"key":"7453_CR40","doi-asserted-by":"crossref","unstructured":"Zhao H, Shi J, Qi X, Wang X, Jia J (2017) Pyramid scene parsing network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2881\u20132890","DOI":"10.1109\/CVPR.2017.660"},{"key":"7453_CR41","doi-asserted-by":"crossref","unstructured":"Yu C, Wang J, Peng C, Gao C, Yu G, Sang N (2018) Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325\u2013341","DOI":"10.1007\/978-3-030-01261-8_20"},{"issue":"7","key":"7453_CR42","doi-asserted-by":"publisher","first-page":"1733","DOI":"10.1049\/ipr2.13058","volume":"18","author":"R Jiang","year":"2024","unstructured":"Jiang R, Chen R, Zhang L, Wang X, Xu Y (2024) Am-mulfsnet: a fast semantic segmentation network combining attention mechanism and multi-branch. IET Image Process 18(7):1733\u20131744. https:\/\/doi.org\/10.1049\/ipr2.13058","journal-title":"IET Image Process"},{"key":"7453_CR43","doi-asserted-by":"crossref","unstructured":"Sandler M, Howard A, Zhu M, Zhmoginov A, Chen L-C (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510\u20134520","DOI":"10.1109\/CVPR.2018.00474"},{"key":"7453_CR44","doi-asserted-by":"crossref","unstructured":"Cordts M, Omran M, Ramos S, Rehfeld T, Enzweiler M, Benenson R, Franke U, Roth S, Schiele B (2016) The cityscapes dataset for semantic urban scene understanding. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3213\u20133223","DOI":"10.1109\/CVPR.2016.350"},{"key":"7453_CR45","doi-asserted-by":"crossref","unstructured":"Sakaridis C, Dai D, Van Gool L (2021) Acdc: The adverse conditions dataset with correspondences for semantic driving scene understanding. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 10765\u201310775","DOI":"10.1109\/ICCV48922.2021.01059"},{"key":"7453_CR46","doi-asserted-by":"publisher","first-page":"3051","DOI":"10.1007\/s11263-021-01515-2","volume":"129","author":"C Yu","year":"2021","unstructured":"Yu C, Gao C, Wang J, Yu G, Shen C, Sang N (2021) Bisenet v2: bilateral network with guided aggregation for real-time semantic segmentation. Int J Comput Vis 129:3051\u20133068. https:\/\/doi.org\/10.1007\/s11263-021-01515-2","journal-title":"Int J Comput Vis"},{"key":"7453_CR47","doi-asserted-by":"crossref","unstructured":"Sun K, Xiao B, Liu D, Wang J (2019) Deep high-resolution representation learning for human pose estimation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5693\u20135703","DOI":"10.1109\/CVPR.2019.00584"},{"key":"7453_CR48","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-assisted intervention\u2013MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18, pp. 234\u2013241. Springer","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"7453_CR49","doi-asserted-by":"crossref","unstructured":"Xia C, Wang X, Lv F, Hao X, Shi Y (2024) Vit-comer: Vision transformer with convolutional multi-scale feature interaction for dense predictions. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5493\u20135502","DOI":"10.1109\/CVPR52733.2024.00525"},{"key":"7453_CR50","doi-asserted-by":"crossref","unstructured":"Cavagnero N, Rosi G, Cuttano C, Pistilli F, Ciccone M, Averta G, Cermelli F (2024) Pem: Prototype-based efficient maskformer for image segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 15804\u201315813","DOI":"10.1109\/CVPR52733.2024.01496"},{"key":"7453_CR51","doi-asserted-by":"crossref","unstructured":"Xu Z, Wu D, Yu C, Chu X, Sang N, Gao C (2024) Sctnet: Single-branch cnn with transformer semantic information for real-time segmentation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, pp. 6378\u20136386","DOI":"10.1609\/aaai.v38i6.28457"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-025-07453-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-025-07453-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-025-07453-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,8]],"date-time":"2025-06-08T16:06:21Z","timestamp":1749398781000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-025-07453-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,8]]},"references-count":51,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2025,6]]}},"alternative-id":["7453"],"URL":"https:\/\/doi.org\/10.1007\/s11227-025-07453-x","relation":{},"ISSN":["1573-0484"],"issn-type":[{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,8]]},"assertion":[{"value":"13 May 2025","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 June 2025","order":2,"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 no Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"987"}}