{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T21:57:48Z","timestamp":1761170268399,"version":"build-2065373602"},"reference-count":54,"publisher":"Springer Science and Business Media LLC","issue":"16","license":[{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"State Key Laboratory of Cognitive Intelligence, Iflytek","award":["COGOS-2024HE01"],"award-info":[{"award-number":["COGOS-2024HE01"]}]},{"DOI":"10.13039\/501100017700","name":"Henan Provincial Science and Technology Research Project","doi-asserted-by":"publisher","award":["222103810042"],"award-info":[{"award-number":["222103810042"]}],"id":[{"id":"10.13039\/501100017700","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Henan Academy of Sciences Graduate Innovation Project","award":["24330775"],"award-info":[{"award-number":["24330775"]}]},{"name":"Henan Provincial Science and Technology Attack Project","award":["No.252102221018"],"award-info":[{"award-number":["No.252102221018"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"DOI":"10.1007\/s11227-025-07982-5","type":"journal-article","created":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T13:32:24Z","timestamp":1761053544000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["PCBNet: positional crossing and broad features network for indoor scene semantic segmentation"],"prefix":"10.1007","volume":"81","author":[{"given":"Huifang","family":"Hou","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhang","family":"Wenwen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zihao","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sun","family":"Wentao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yale","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liu","family":"Gang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Han","family":"Haipeng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liu","family":"Tianyuan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li","family":"Xiaofeng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,10,21]]},"reference":[{"key":"7982_CR1","doi-asserted-by":"crossref","unstructured":"Kim W, Seok J (2018) Indoor semantic segmentation for robot navigating on mobile. In: 2018 10th International Conference on Ubiquitous and Future Networks (ICUFN). IEEE, pp 22\u201325","DOI":"10.1109\/ICUFN.2018.8436956"},{"key":"7982_CR2","doi-asserted-by":"crossref","unstructured":"Seichter D, K\u00f6hler M, Lewandowski B, Wengefeld T, Gross H-M (2021) Efficient rgb-d semantic segmentation for indoor scene analysis. In: 2021 IEEE International Conference on Robotics and Automation (ICRA). IEEE, pp 13525\u201313531","DOI":"10.1109\/ICRA48506.2021.9561675"},{"issue":"2","key":"7982_CR3","doi-asserted-by":"publisher","first-page":"2173","DOI":"10.1007\/s13369-022-07092-x","volume":"48","author":"F Abdullah","year":"2023","unstructured":"Abdullah F, Jalal A (2023) Semantic segmentation based crowd tracking and anomaly detection via neuro-fuzzy classifier in smart surveillance system. Arab J Sci Eng 48(2):2173\u20132190","journal-title":"Arab J Sci Eng"},{"issue":"1","key":"7982_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11227-024-06760-z","volume":"81","author":"M Ge","year":"2025","unstructured":"Ge M, Su W, Gao J, Jia G (2025) Cdmanet: central difference mutual attention network for rgb-d semantic segmentation. J Supercomput 81(1):1\u201323","journal-title":"J Supercomput"},{"key":"7982_CR5","doi-asserted-by":"crossref","unstructured":"Noori AY (2021) A survey of rgb-d image semantic segmentation by deep learning. In: 2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS), vol 1. IEEE, pp 1953\u20131957","DOI":"10.1109\/ICACCS51430.2021.9441924"},{"key":"7982_CR6","doi-asserted-by":"crossref","unstructured":"Fang T, Wei Z, Zhang L, Li X, Zhang W (2023) Depth dependence removal in rgb-d semantic segmentation model. In: 2023 IEEE International Conference on Mechatronics and Automation (ICMA). IEEE, pp 699\u2013704","DOI":"10.1109\/ICMA57826.2023.10215548"},{"issue":"1","key":"7982_CR7","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1109\/LGRS.2020.2968550","volume":"18","author":"R Cao","year":"2020","unstructured":"Cao R, Fang L, Lu T, He N (2020) Self-attention-based deep feature fusion for remote sensing scene classification. IEEE Geosci Remote Sens Lett 18(1):43\u201347","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"7982_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.108881","volume":"131","author":"W Wu","year":"2022","unstructured":"Wu W, Chu T, Liu Q (2022) Complementarity-aware cross-modal feature fusion network for rgb-t semantic segmentation. Pattern Recogn 131:108881","journal-title":"Pattern Recogn"},{"issue":"11","key":"7982_CR9","doi-asserted-by":"publisher","first-page":"8416","DOI":"10.1007\/s10489-021-02282-w","volume":"51","author":"P-H Dinh","year":"2021","unstructured":"Dinh P-H (2021) Multi-modal medical image fusion based on equilibrium optimizer algorithm and local energy functions. Appl Intell 51(11):8416\u20138431","journal-title":"Appl Intell"},{"key":"7982_CR10","doi-asserted-by":"crossref","unstructured":"Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 7132\u20137141","DOI":"10.1109\/CVPR.2018.00745"},{"key":"7982_CR11","doi-asserted-by":"crossref","unstructured":"Cao Y, Xu J, Lin S, Wei F, Hu H (2019) Gcnet: Non-local networks meet squeeze-excitation networks and beyond. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision Workshops","DOI":"10.1109\/ICCVW.2019.00246"},{"key":"7982_CR12","doi-asserted-by":"crossref","unstructured":"Liu J-J, Hou Q, Cheng M-M, Wang C, Feng J (2020) Improving convolutional networks with self-calibrated convolutions. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 10096\u201310105","DOI":"10.1109\/CVPR42600.2020.01011"},{"issue":"15","key":"7982_CR13","doi-asserted-by":"publisher","first-page":"1374","DOI":"10.1007\/s11227-025-07859-7","volume":"81","author":"Q Liu","year":"2025","unstructured":"Liu Q, Fang Y, Liu X (2025) Enhancing visual and semantic alignment of multimodal large models in medical images. J Supercomput 81(15):1374","journal-title":"J Supercomput"},{"key":"7982_CR14","doi-asserted-by":"crossref","unstructured":"Wang Y, Zheng W, Xia Y, Wang S (2025) Asymmetric dual-stream networks for lightweight rgb-d salient object detection. J Shanghai Jiaotong Univ Sci 1\u201311","DOI":"10.1007\/s12204-024-2794-0"},{"key":"7982_CR15","doi-asserted-by":"crossref","unstructured":"Xing Y, Wang J, Chen X, Zeng G (2019) Coupling two-stream rgb-d semantic segmentation network by idempotent mappings. In: 2019 IEEE International Conference on Image Processing (ICIP). IEEE, pp 1850\u20131854","DOI":"10.1109\/ICIP.2019.8803146"},{"key":"7982_CR16","doi-asserted-by":"crossref","unstructured":"Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 3431\u20133440","DOI":"10.1109\/CVPR.2015.7298965"},{"issue":"6","key":"7982_CR17","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2017","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2017) Imagenet classification with deep convolutional neural networks. Commun ACM 60(6):84\u201390","journal-title":"Commun ACM"},{"key":"7982_CR18","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556"},{"key":"7982_CR19","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 1\u20139","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"7982_CR20","unstructured":"Donahue J, Jia Y, Vinyals O, Hoffman J, Zhang N, Tzeng E, Darrell T (2014) Decaf: a deep convolutional activation feature for generic visual recognition. In: International Conference on Machine Learning. PMLR, pp 647\u2013655"},{"issue":"1","key":"7982_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11227-024-06770-x","volume":"81","author":"X Wang","year":"2025","unstructured":"Wang X, An H, Zhang J, Huang D, Wen J (2025) Dpsmunet: a new network based on a dual-pooling self-attention module for carotid artery plaque segmentation in ultrasound images. J Supercomput 81(1):1\u201323","journal-title":"J Supercomput"},{"key":"7982_CR22","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":"7982_CR23","first-page":"17864","volume":"34","author":"B Cheng","year":"2021","unstructured":"Cheng B, Schwing A, Kirillov A (2021) Per-pixel classification is not all you need for semantic segmentation. Adv Neural Inf Process Syst 34:17864\u201317875","journal-title":"Adv Neural Inf Process Syst"},{"key":"7982_CR24","unstructured":"Chen Z, Duan Y, Wang W, He J, Lu T, Dai J, Qiao Y. Vision transformer adapter for dense predictions. arXiv preprint arXiv:2205.08534"},{"key":"7982_CR25","doi-asserted-by":"crossref","unstructured":"Hazirbas C, Ma L, Domokos C, Cremers D (2017) Fusenet: incorporating depth into semantic segmentation via fusion-based cnn architecture. In: Computer Vision\u2014ACCV 2016: 13th Asian Conference on Computer Vision, Taipei, Taiwan, November 20\u201324, 2016, Revised Selected Papers, Part I 13. Springer, pp 213\u2013228","DOI":"10.1007\/978-3-319-54181-5_14"},{"issue":"12","key":"7982_CR26","doi-asserted-by":"publisher","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","volume":"39","author":"V Badrinarayanan","year":"2017","unstructured":"Badrinarayanan V, Kendall A, Cipolla R (2017) Segnet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell 39(12):2481\u20132495","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"4","key":"7982_CR27","doi-asserted-by":"publisher","first-page":"3279","DOI":"10.1109\/TKDE.2021.3126456","volume":"35","author":"G Brauwers","year":"2021","unstructured":"Brauwers G, Frasincar F (2021) A general survey on attention mechanisms in deep learning. IEEE Trans Knowl Data Eng 35(4):3279\u20133298","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"4","key":"7982_CR28","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","volume":"40","author":"L-C Chen","year":"2017","unstructured":"Chen L-C, Papandreou G, Kokkinos I, Murphy K, Yuille AL (2017) Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Trans Pattern Anal Mach Intell 40(4):834\u2013848","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"7982_CR29","doi-asserted-by":"crossref","unstructured":"Fu J, Liu J, Tian H, Li Y, Bao Y, Fang Z, Lu H (2019) Dual attention network for scene segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 3146\u20133154","DOI":"10.1109\/CVPR.2019.00326"},{"key":"7982_CR30","doi-asserted-by":"crossref","unstructured":"Xu D, Ouyang W, Wang X, Sebe N (2018) Pad-net: multi-tasks guided prediction-and-distillation network for simultaneous depth estimation and scene parsing. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 675\u2013684","DOI":"10.1109\/CVPR.2018.00077"},{"key":"7982_CR31","doi-asserted-by":"crossref","unstructured":"Li H, Sun Y (2023) Igfnet: illumination-guided fusion network for semantic scene understanding using rgb-thermal images. In: 2023 IEEE International Conference on Robotics and Biomimetics (ROBIO). IEEE, pp 1\u20136","DOI":"10.1109\/ROBIO58561.2023.10354613"},{"key":"7982_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.dsp.2025.105043","volume":"160","author":"B Ge","year":"2025","unstructured":"Ge B, Lu Y, Xia C, Zhu X, Zhang M, Gao M, Chen N (2025) Cfeinet: cross-fusion and feature enhancement interaction network for rgb-d semantic segmentation. Digit Signal Process 160:105043","journal-title":"Digit Signal Process"},{"issue":"2","key":"7982_CR33","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1587\/transfun.2024EAP1023","volume":"108","author":"K Zhou","year":"2025","unstructured":"Zhou K, Zhang Z, Tang X, Xu W, Xie J, Tang C (2025) Shape-aware convolution with convolutional kernel attention for rgb-d image semantic segmentation. IEICE Trans Fundam Electron Commun Comput Sci 108(2):140\u2013148","journal-title":"IEICE Trans Fundam Electron Commun Comput Sci"},{"key":"7982_CR34","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"},{"issue":"3","key":"7982_CR35","doi-asserted-by":"publisher","first-page":"315","DOI":"10.1111\/j.1751-5823.2002.tb00174.x","volume":"70","author":"D Fouskakis","year":"2002","unstructured":"Fouskakis D, Draper D (2002) Stochastic optimization: a review. Int Stat Rev 70(3):315\u2013349","journal-title":"Int Stat Rev"},{"key":"7982_CR36","doi-asserted-by":"publisher","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 et al (2015) Imagenet large scale visual recognition challenge. Int J Comput Vis 115:211\u2013252","journal-title":"Int J Comput Vis"},{"key":"7982_CR37","doi-asserted-by":"publisher","first-page":"3520","DOI":"10.1109\/TIP.2019.2962685","volume":"29","author":"H Ding","year":"2020","unstructured":"Ding H, Jiang X, Shuai B, Liu AQ, Wang G (2020) Semantic segmentation with context encoding and multi-path decoding. IEEE Trans Image Process 29:3520\u20133533","journal-title":"IEEE Trans Image Process"},{"key":"7982_CR38","doi-asserted-by":"crossref","unstructured":"Hu X, Yang K, Fei L, Wang K (2019) Acnet: Attention based network to exploit complementary features for rgbd semantic segmentation. In: 2019 IEEE International Conference on Image Processing (ICIP). IEEE, pp 1440\u20131444","DOI":"10.1109\/ICIP.2019.8803025"},{"key":"7982_CR39","unstructured":"Park S-J, Hong K-S, Lee S (2017) Rdfnet: Rgb-d multi-level residual feature fusion for indoor semantic segmentation. In: Proceedings of the IEEE International Conference on Computer Vision, pp 4980\u20134989"},{"key":"7982_CR40","doi-asserted-by":"crossref","unstructured":"Zhou H, Qi L, Wan Z, Huang H, Yang X (2020) Rgb-d co-attention network for semantic segmentation. In: Proceedings of the Asian Conference on Computer Vision","DOI":"10.1007\/978-3-030-69525-5_31"},{"key":"7982_CR41","doi-asserted-by":"crossref","unstructured":"Zhang Z, Cui Z, Xu C, Yan Y, Sebe N, Yang J (2019) Pattern-affinitive propagation across depth, surface normal and semantic segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 4106\u20134115","DOI":"10.1109\/CVPR.2019.00423"},{"key":"7982_CR42","doi-asserted-by":"publisher","first-page":"2313","DOI":"10.1109\/TIP.2021.3049332","volume":"30","author":"L-Z Chen","year":"2021","unstructured":"Chen L-Z, Lin Z, Wang Z, Yang Y-L, Cheng M-M (2021) Spatial information guided convolution for real-time rgbd semantic segmentation. IEEE Trans Image Process 30:2313\u20132324","journal-title":"IEEE Trans Image Process"},{"key":"7982_CR43","doi-asserted-by":"crossref","unstructured":"Cao J, Leng H, Lischinski D, Cohen-Or D, Tu C, Li Y (2021) Shapeconv: shape-aware convolutional layer for indoor rgb-d semantic segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp 7088\u20137097","DOI":"10.1109\/ICCV48922.2021.00700"},{"key":"7982_CR44","doi-asserted-by":"crossref","unstructured":"Chen X, Lin K-Y, Wang J, Wu W, Qian C, Li H, Zeng G (2020) Bi-directional cross-modality feature propagation with separation-and-aggregation gate for rgb-d semantic segmentation. In: European Conference on Computer Vision. Springer, pp 561\u2013577","DOI":"10.1007\/978-3-030-58621-8_33"},{"key":"7982_CR45","doi-asserted-by":"publisher","first-page":"6348","DOI":"10.1109\/TMM.2023.3349072","volume":"26","author":"Y Lv","year":"2024","unstructured":"Lv Y, Liu Z, Li G (2024) Context-aware interaction network for rgb-t semantic segmentation. IEEE Trans Multimedia 26:6348\u20136360","journal-title":"IEEE Trans Multimedia"},{"issue":"3","key":"7982_CR46","doi-asserted-by":"publisher","first-page":"1481","DOI":"10.1109\/TCSVT.2023.3296162","volume":"34","author":"J Yang","year":"2023","unstructured":"Yang J, Bai L, Sun Y, Tian C, Mao M, Wang G (2023) Pixel difference convolutional network for RGB-D semantic segmentation. IEEE Trans Circuits Syst Video Technol 34(3):1481\u20131492","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"7982_CR47","doi-asserted-by":"crossref","unstructured":"Hazirbas C, Ma L, Domokos C, Cremers D (2017) Fusenet: incorporating depth into semantic segmentation via fusion-based cnn architecture. In: Computer Vision\u2013ACCV 2016: 13th Asian Conference on Computer Vision, Taipei, Taiwan, November 20\u201324, 2016, Revised Selected Papers, Part I 13. Springer, pp 213\u2013228","DOI":"10.1007\/978-3-319-54181-5_14"},{"key":"7982_CR48","doi-asserted-by":"crossref","unstructured":"Wang W, Neumann U (2018) Depth-aware cnn for rgb-d segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp 135\u2013150","DOI":"10.1007\/978-3-030-01252-6_9"},{"key":"7982_CR49","doi-asserted-by":"crossref","unstructured":"Kong S, Fowlkes CC (2018) Recurrent scene parsing with perspective understanding in the loop. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 956\u2013965","DOI":"10.1109\/CVPR.2018.00106"},{"key":"7982_CR50","doi-asserted-by":"crossref","unstructured":"Qi X, Liao R, Jia J, Fidler S, Urtasun R (2017) 3d graph neural networks for rgbd semantic segmentation. In: Proceedings of the IEEE International Conference on Computer Vision, pp 5199\u20135208","DOI":"10.1109\/ICCV.2017.556"},{"key":"7982_CR51","doi-asserted-by":"publisher","first-page":"658","DOI":"10.1109\/LSP.2021.3066071","volume":"28","author":"G Zhang","year":"2021","unstructured":"Zhang G, Xue J-H, Xie P, Yang S, Wang G (2021) Non-local aggregation for rgb-d semantic segmentation. IEEE Signal Process Lett 28:658\u2013662","journal-title":"IEEE Signal Process Lett"},{"key":"7982_CR52","doi-asserted-by":"crossref","unstructured":"Xiong Z, Yuan Y, Guo N, Wang Q (2020) Variational context-deformable convnets for indoor scene parsing. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 3992\u20134002","DOI":"10.1109\/CVPR42600.2020.00405"},{"key":"7982_CR53","doi-asserted-by":"crossref","unstructured":"Seichter D, K\u00f6hler M, Lewandowski B, Wengefeld T, Gross H-M (2021) Efficient rgb-d semantic segmentation for indoor scene analysis. In: 2021 IEEE International Conference on Robotics and Automation (ICRA). IEEE, pp 13525\u201313531","DOI":"10.1109\/ICRA48506.2021.9561675"},{"key":"7982_CR54","first-page":"4835","volume":"33","author":"Y Wang","year":"2020","unstructured":"Wang Y, Huang W, Sun F, Xu T, Rong Y, Huang J (2020) Deep multimodal fusion by channel exchanging. Adv Neural Inf Process Syst 33:4835\u20134845","journal-title":"Adv Neural Inf Process Syst"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-025-07982-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-025-07982-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-025-07982-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T02:05:20Z","timestamp":1761098720000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-025-07982-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,21]]},"references-count":54,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2025,11]]}},"alternative-id":["7982"],"URL":"https:\/\/doi.org\/10.1007\/s11227-025-07982-5","relation":{},"ISSN":["1573-0484"],"issn-type":[{"type":"electronic","value":"1573-0484"}],"subject":[],"published":{"date-parts":[[2025,10,21]]},"assertion":[{"value":"8 May 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 October 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 October 2025","order":3,"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"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval and consent to participate"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"Code availability not applicable.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Code availability"}},{"value":"Not applicable.","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Materials availability"}}],"article-number":"1483"}}