{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T13:05:11Z","timestamp":1760101511599,"version":"3.41.0"},"reference-count":57,"publisher":"Springer Science and Business Media LLC","issue":"5-6","license":[{"start":{"date-parts":[[2024,11,22]],"date-time":"2024-11-22T00:00:00Z","timestamp":1732233600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,22]],"date-time":"2024-11-22T00:00:00Z","timestamp":1732233600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"National Major Scientific Research Instrument Development Project of China","award":["62027819"],"award-info":[{"award-number":["62027819"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2025,6]]},"DOI":"10.1007\/s13042-024-02464-1","type":"journal-article","created":{"date-parts":[[2024,11,22]],"date-time":"2024-11-22T05:38:10Z","timestamp":1732253890000},"page":"3521-3537","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Multi-sensors image fusion method for non-destructive inspection in vertical-cavity surface-emitting lasers"],"prefix":"10.1007","volume":"16","author":[{"given":"Minfu","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jumin","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuai","family":"Guo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dengao","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bao","family":"Tang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Biao","family":"Luo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,11,22]]},"reference":[{"issue":"8S2","key":"2464_CR1","doi-asserted-by":"publisher","first-page":"01","DOI":"10.7567\/JJAP.57.08PA01","volume":"57","author":"K Iga","year":"2018","unstructured":"Iga K (2018) Forty years of vertical-cavity surface-emitting laser. Invention and innovation. Jpn J Appl Phys 57(8S2):01\u201308","journal-title":"Jpn J Appl Phys"},{"key":"2464_CR2","doi-asserted-by":"crossref","unstructured":"Ledentsov N, Shchukin V, Kalosha V, Turkiewicz J, Ledentsov N, et al (2020) Development of VCSELs and VCSEL-based links for data communication beyond 50gb\/s. In: Optical fiber communication conference, pp. 2\u20133. Optica Publishing Group","DOI":"10.1364\/OFC.2020.M2A.3"},{"key":"2464_CR3","doi-asserted-by":"crossref","unstructured":"Warren ME et al (2018) Low-divergence high-power VCSEL arrays for lidar application. In: Proc Vertical-Cavity Surf-Emitting Lasers XXII, vol. 10552, pp. 72\u201381","DOI":"10.1117\/12.2290937"},{"key":"2464_CR4","unstructured":"Okur S et al (2019) High-power vcsel arrays with customized beam divergence for 3d-sensing applications. In: Proceedings of SPIE, 10938, pp. 61\u201367"},{"issue":"6","key":"2464_CR5","doi-asserted-by":"publisher","first-page":"1552","DOI":"10.1109\/JSTQE.2011.2119469","volume":"17","author":"A Larsson","year":"2011","unstructured":"Larsson A (2011) Advances in VCSELs for communication and sensing. IEEE J Sel Top Quantum Electron 17(6):1552\u20131567","journal-title":"IEEE J Sel Top Quantum Electron"},{"key":"2464_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.microrel.2022.114828","volume":"140","author":"R Coppeta","year":"2023","unstructured":"Coppeta R, Fabbro R, Pusterhofer M, Haber T, Fasching G (2023) Thermomechanical model of an oxide-confined GAAS-based VCSEL emitter. Microelectron Reliab 140:114828","journal-title":"Microelectron Reliab"},{"issue":"1","key":"2464_CR7","doi-asserted-by":"publisher","first-page":"014201","DOI":"10.1117\/1.2150232","volume":"45","author":"T Itakura","year":"2006","unstructured":"Itakura T, Seyama Y, Terada T, Ueda K (2006) Transmission-electron-microscopy observation of dislocation networks of oxide vertical-cavity surface-emitting lasers. Opt Eng 45(1):014201\u2013014201","journal-title":"Opt Eng"},{"key":"2464_CR8","doi-asserted-by":"crossref","unstructured":"McHugo SA et al (2003) Characterization of failure mechanisms for oxide VCSELs. In: Proceedings of integrated optoelectronics devices, pp. 55\u201366 (San Jose, CA)","DOI":"10.1117\/12.482637"},{"key":"2464_CR9","doi-asserted-by":"crossref","unstructured":"Herrick RW (2012) Reliability and degradation of vertical-cavity surface-emitting lasers. In: Materials and Reliability Handbook for Semiconductor Optical and Electron Devices, pp. 147\u2013205","DOI":"10.1007\/978-1-4614-4337-7_6"},{"key":"2464_CR10","doi-asserted-by":"crossref","unstructured":"Mathes D et al (2005) An atlas of esd failure signatures in vertical cavity surface emitting lasers. In: Proc. ISTFA, vol. 31, pp. 330\u2013336","DOI":"10.31399\/asm.cp.istfa2005p0336"},{"key":"2464_CR11","doi-asserted-by":"crossref","unstructured":"Lei C et al (2010) Emcore vcsel failure mechanism and resolution. In: Proc. SPIE, vol. 7615, pp. 29\u201338","DOI":"10.1117\/12.845968"},{"issue":"9","key":"2464_CR12","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6501\/abf730","volume":"32","author":"R Fabbro","year":"2021","unstructured":"Fabbro R, Haber T, Fasching G, Coppeta R, Pusterhofer M, Grogger W (2021) Defect localization in high-power vertical cavity surface emitting laser arrays by means of reverse biased emission microscopy. Meas Sci Technol 32(9):095406","journal-title":"Meas Sci Technol"},{"key":"2464_CR13","doi-asserted-by":"crossref","unstructured":"Zhang Y et al (2022) Analysis of common failure causes in oxide VCSELs. In: Proceedings of SPIE\u2014The International Society of Optical Engineering, vol. 12164, pp. 335\u2013342","DOI":"10.1117\/12.2628609"},{"issue":"6","key":"2464_CR14","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6641\/abfa2f","volume":"36","author":"X Sun","year":"2021","unstructured":"Sun X, Rickard W, Ironside C, Kostakis I, Missous M, Powell D, Anjomshoaa A, Meredith W (2021) Targeted defect analysis in VCSEL oxide windows using 3d slice and view. Semicond Sci Technol 36(6):065015","journal-title":"Semicond Sci Technol"},{"key":"2464_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.micron.2022.103264","volume":"158","author":"R Fabbro","year":"2022","unstructured":"Fabbro R, Coppeta R, Pusterhofer M, Fasching G, Haber T, Grogger W (2022) In-situ observation of lateral alas oxidation and dislocation formation in VCSELs. Micron 158:103264","journal-title":"Micron"},{"issue":"9\u201311","key":"2464_CR16","doi-asserted-by":"publisher","first-page":"1699","DOI":"10.1016\/S0026-2714(03)00337-8","volume":"43","author":"N Lucarelli","year":"2003","unstructured":"Lucarelli N, Cavone M, Muschitiello M, Centrone D, Corsi F (2003) Thermally induced voltage alteration (TIVA) applied to ESD induced failures. Microelectron Reliab 43(9\u201311):1699\u20131704","journal-title":"Microelectron Reliab"},{"issue":"11S","key":"2464_CR17","doi-asserted-by":"publisher","first-page":"01","DOI":"10.1143\/JJAP.51.11PC01","volume":"51","author":"RW Herrick","year":"2012","unstructured":"Herrick RW (2012) Reliability of vertical-cavity surface-emitting lasers. Jpn J Appl Phys 51(11S):01\u201311","journal-title":"Jpn J Appl Phys"},{"key":"2464_CR18","doi-asserted-by":"crossref","unstructured":"Ram\u00a0Prabhakar K et al (2017) Deepfuse: a deep unsupervised approach for exposure fusion with extreme exposure image pairs. In: Proceedings of IEEE International Conference on Computer Vision, pp. 4714\u20134722","DOI":"10.1109\/ICCV.2017.505"},{"key":"2464_CR19","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.inffus.2018.09.004","volume":"48","author":"J Ma","year":"2019","unstructured":"Ma J, Yu W, Liang P, Li C, Jiang J (2019) Fusiongan: a generative adversarial network for infrared and visible image fusion. Inf Fusion 48:11\u201326","journal-title":"Inf Fusion"},{"key":"2464_CR20","doi-asserted-by":"crossref","unstructured":"Zhang H, Xu H, Xiao Y, Guo X, Ma J (2020) Rethinking the image fusion: A fast unified image fusion network based on proportional maintenance of gradient and intensity. In: Proceedings of the AAAI conference on artificial intelligence, vol. 34, pp. 12797\u201312804","DOI":"10.1609\/aaai.v34i07.6975"},{"key":"2464_CR21","doi-asserted-by":"publisher","first-page":"1383","DOI":"10.1109\/TMM.2020.2997127","volume":"23","author":"J Li","year":"2020","unstructured":"Li J, Huo H, Li C, Wang R, Feng Q (2020) Attentionfgan: infrared and visible image fusion using attention-based generative adversarial networks. IEEE Trans Multimedia 23:1383\u20131396","journal-title":"IEEE Trans Multimedia"},{"issue":"1","key":"2464_CR22","doi-asserted-by":"publisher","first-page":"502","DOI":"10.1109\/TPAMI.2020.3012548","volume":"44","author":"H Xu","year":"2020","unstructured":"Xu H, Ma J, Jiang J, Guo X, Ling H (2020) U2fusion: a unified unsupervised image fusion network. IEEE Trans Pattern Anal Mach Intell 44(1):502\u2013518","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2464_CR23","first-page":"1","volume":"72","author":"J Wang","year":"2023","unstructured":"Wang J, Xi X, Li D, Li F (2023) Fusiongram: an infrared and visible image fusion framework based on gradient residual and attention mechanism. IEEE Trans Instrum Meas 72:1\u201312","journal-title":"IEEE Trans Instrum Meas"},{"key":"2464_CR24","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1016\/j.inffus.2021.12.004","volume":"82","author":"L Tang","year":"2022","unstructured":"Tang L, Yuan J, Ma J (2022) Image fusion in the loop of high-level vision tasks: a semantic-aware real-time infrared and visible image fusion network. Inf Fusion 82:28\u201342","journal-title":"Inf Fusion"},{"issue":"1","key":"2464_CR25","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1049\/ipr2.12627","volume":"17","author":"Q Zheng","year":"2023","unstructured":"Zheng Q, Zhao Y, Zhang X, Zhu P, Ma W (2023) A multi-view image fusion algorithm for industrial weld. IET Image Proc 17(1):193\u2013203","journal-title":"IET Image Proc"},{"key":"2464_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.optlaseng.2023.107488","volume":"163","author":"J Guan","year":"2023","unstructured":"Guan J, Fei J, Li W, Jiang X, Wu L, Liu Y, Xi J (2023) Defect classification for specular surfaces based on deflectometry and multi-modal fusion network. Opt Lasers Eng 163:107488","journal-title":"Opt Lasers Eng"},{"key":"2464_CR27","doi-asserted-by":"publisher","first-page":"406","DOI":"10.1016\/j.solener.2022.03.018","volume":"236","author":"F Hong","year":"2022","unstructured":"Hong F, Song J, Meng H, Wang R, Fang F, Zhang G (2022) A novel framework on intelligent detection for module defects of PV plant combining the visible and infrared images. Solar Energy 236:406\u2013416","journal-title":"Solar Energy"},{"issue":"20","key":"2464_CR28","doi-asserted-by":"publisher","first-page":"23390","DOI":"10.1109\/JSEN.2021.3106057","volume":"21","author":"M Li","year":"2021","unstructured":"Li M, Yao N, Liu S, Li S, Zhao Y, Kong SG (2021) Multisensor image fusion for automated detection of defects in printed circuit boards. IEEE Sens J 21(20):23390\u201323399","journal-title":"IEEE Sens J"},{"key":"2464_CR29","doi-asserted-by":"publisher","first-page":"584","DOI":"10.1109\/TCI.2021.3083965","volume":"7","author":"W Tang","year":"2021","unstructured":"Tang W, Liu Y, Cheng J, Li C, Chen X (2021) Green fluorescent protein and phase contrast image fusion via detail preserving cross network. IEEE Trans Comput Imaging 7:584\u2013597","journal-title":"IEEE Trans Comput Imaging"},{"key":"2464_CR30","first-page":"1","volume":"72","author":"W Zhou","year":"2023","unstructured":"Zhou W, Wang Z, Zhang M, Wang L (2023) Wind turbine actual defects detection based on visible and infrared image fusion. IEEE Trans Instrum Meas 72:1\u20138","journal-title":"IEEE Trans Instrum Meas"},{"issue":"1","key":"2464_CR31","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1186\/s43074-022-00051-7","volume":"3","author":"H Sun","year":"2022","unstructured":"Sun H, Wang S, Hu X, Liu H, Zhou X, Huang J, Cheng X, Sun F, Liu Y, Liu D (2022) Detection of surface defects and subsurface defects of polished optics with multisensor image fusion. PhotoniX 3(1):6","journal-title":"PhotoniX"},{"key":"2464_CR32","doi-asserted-by":"publisher","first-page":"374","DOI":"10.1016\/j.measurement.2017.08.002","volume":"111","author":"SS Mart\u00ednez","year":"2017","unstructured":"Mart\u00ednez SS, V\u00e1zquez CO, Garc\u00eda JG, Ortega JG (2017) Quality inspection of machined metal parts using an image fusion technique. Measurement 111:374\u2013383","journal-title":"Measurement"},{"key":"2464_CR33","doi-asserted-by":"crossref","unstructured":"Eisler K, Homma C, Goldammer M, Rothenfusser M, Arnold W (2013) Fusion of visual and infrared thermography images for advanced assessment in non-destructive testing. Rev Sci Instrum 84(6)","DOI":"10.1063\/1.4808280"},{"key":"2464_CR34","doi-asserted-by":"crossref","unstructured":"Herrick RW (2012) Reliability and degradation of vertical-cavity surface-emitting lasers. In: Materials and Reliability Handbook for Semiconductor Optical and Electron Devices, pp. 147\u2013205. Springer","DOI":"10.1007\/978-1-4614-4337-7_6"},{"key":"2464_CR35","first-page":"12","volume":"1432","author":"RW Herrick","year":"2012","unstructured":"Herrick RW (2012) Design for reliability and common failure mechanisms in vertical cavity surface emitting lasers. MRS Online Proceed Library (OPL) 1432:12\u20131432","journal-title":"MRS Online Proceed Library (OPL)"},{"key":"2464_CR36","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"2464_CR37","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Van Der\u00a0Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision Pattern Recognition, pp. 4700\u20134708","DOI":"10.1109\/CVPR.2017.243"},{"issue":"1","key":"2464_CR38","first-page":"15","volume":"10","author":"JM Prewitt","year":"1970","unstructured":"Prewitt JM et al (1970) Object enhancement and extraction. Pict Process Psychopictorics 10(1):15\u201319","journal-title":"Pict Process Psychopictorics"},{"key":"2464_CR39","unstructured":"Sobel I (1990) An isotropic 3$$\\times$$ 3 image gradient operater. Machine vision for three-dimensional scenes, 376\u2013379"},{"issue":"12","key":"2464_CR40","doi-asserted-by":"publisher","first-page":"9645","DOI":"10.1109\/TIM.2020.3005230","volume":"69","author":"H Li","year":"2020","unstructured":"Li H, Wu X-J, Durrani T (2020) Nestfuse: an infrared and visible image fusion architecture based on nest connection and spatial\/channel attention models. IEEE Trans Instrum Meas 69(12):9645\u20139656","journal-title":"IEEE Trans Instrum Meas"},{"key":"2464_CR41","doi-asserted-by":"crossref","unstructured":"Lin T-Y, et al (2014) Microsoft coco: Common objects in context. In: Proceedings of European conference on computer vision, pp. 740\u2013755","DOI":"10.1007\/978-3-319-10602-1_48"},{"issue":"1","key":"2464_CR42","doi-asserted-by":"publisher","DOI":"10.1117\/1.2945910","volume":"2","author":"JW Roberts","year":"2008","unstructured":"Roberts JW, Van Aardt JA, Ahmed FB (2008) Assessment of image fusion procedures using entropy, image quality, and multispectral classification. J Appl Remote Sens 2(1):023522","journal-title":"J Appl Remote Sens"},{"issue":"4","key":"2464_CR43","doi-asserted-by":"publisher","first-page":"355","DOI":"10.1088\/0957-0233\/8\/4\/002","volume":"8","author":"Y-J Rao","year":"1997","unstructured":"Rao Y-J (1997) In-fibre bragg grating sensors. Meas Sci Technol 8(4):355","journal-title":"Meas Sci Technol"},{"issue":"4","key":"2464_CR44","doi-asserted-by":"publisher","first-page":"308","DOI":"10.1049\/el:20000267","volume":"36","author":"CS Xydeas","year":"2000","unstructured":"Xydeas CS, Petrovic V et al (2000) Objective image fusion performance measure. Electron Lett 36(4):308\u2013309","journal-title":"Electron Lett"},{"issue":"12","key":"2464_CR45","doi-asserted-by":"publisher","first-page":"2959","DOI":"10.1109\/26.477498","volume":"43","author":"AM Eskicioglu","year":"1995","unstructured":"Eskicioglu AM, Fisher PS (1995) Image quality measures and their performance. IEEE Trans Commun 43(12):2959\u20132965","journal-title":"IEEE Trans Commun"},{"key":"2464_CR46","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1016\/j.optcom.2014.12.032","volume":"341","author":"G Cui","year":"2015","unstructured":"Cui G, Feng H, Xu Z, Li Q, Chen Y (2015) Detail preserved fusion of visible and infrared images using regional saliency extraction and multi-scale image decomposition. Opt Commun 341:199\u2013209","journal-title":"Opt Commun"},{"issue":"7","key":"2464_CR47","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1049\/el:20020212","volume":"38","author":"G Qu","year":"2002","unstructured":"Qu G, Zhang D, Yan P (2002) Information measure for performance of image fusion. Electron Lett 38(7):1","journal-title":"Electron Lett"},{"issue":"1","key":"2464_CR48","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1109\/TCI.2016.2644865","volume":"3","author":"H Zhao","year":"2016","unstructured":"Zhao H, Gallo O, Frosio I, Kautz J (2016) Loss functions for image restoration with neural networks. IEEE Trans Comput Imaging 3(1):47\u201357","journal-title":"IEEE Trans Comput Imaging"},{"key":"2464_CR49","unstructured":"DO\u00a0CT OR\u00a0OF PE (1961) Machine perception of three-dimensional, so lids. PhD thesis, MASSACHUSETTS INSTITUTE OF TECHNOLOGY"},{"key":"2464_CR50","doi-asserted-by":"publisher","first-page":"679","DOI":"10.1109\/TPAMI.1986.4767851","volume":"6","author":"J Canny","year":"1986","unstructured":"Canny J (1986) A computational approach to edge detection. IEEE Trans Patt Anal Mach Intell 6:679\u2013698","journal-title":"IEEE Trans Patt Anal Mach Intell"},{"issue":"5","key":"2464_CR51","doi-asserted-by":"publisher","first-page":"2614","DOI":"10.1109\/TIP.2018.2887342","volume":"28","author":"H Li","year":"2018","unstructured":"Li H, Wu X-J (2018) Densefuse: a fusion approach to infrared and visible images. IEEE Trans Image Process 28(5):2614\u20132623","journal-title":"IEEE Trans Image Process"},{"key":"2464_CR52","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1016\/j.inffus.2019.07.011","volume":"54","author":"Y Zhang","year":"2020","unstructured":"Zhang Y, Liu Y, Sun P, Yan H, Zhao X, Zhang L (2020) Ifcnn: a general image fusion framework based on convolutional neural network. Inf Fusion 54:99\u2013118","journal-title":"Inf Fusion"},{"key":"2464_CR53","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1016\/j.inffus.2021.02.023","volume":"73","author":"H Li","year":"2021","unstructured":"Li H, Wu X-J, Kittler J (2021) RFN-nest: an end-to-end residual fusion network for infrared and visible images. Inf Fusion 73:72\u201386","journal-title":"Inf Fusion"},{"key":"2464_CR54","doi-asserted-by":"publisher","first-page":"2761","DOI":"10.1007\/s11263-021-01501-8","volume":"129","author":"H Zhang","year":"2021","unstructured":"Zhang H, Ma J (2021) SDNet: a versatile squeeze-and-decomposition network for real-time image fusion. Int J Comput Vision 129:2761\u20132785","journal-title":"Int J Comput Vision"},{"key":"2464_CR55","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1016\/j.inffus.2022.03.007","volume":"83","author":"L Tang","year":"2022","unstructured":"Tang L, Yuan J, Zhang H, Jiang X, Ma J (2022) PIAFusion: a progressive infrared and visible image fusion network based on illumination aware. Inf Fusion 83:79\u201392","journal-title":"Inf Fusion"},{"issue":"7","key":"2464_CR56","doi-asserted-by":"publisher","first-page":"1200","DOI":"10.1109\/JAS.2022.105686","volume":"9","author":"J Ma","year":"2022","unstructured":"Ma J, Tang L, Fan F, Huang J, Mei X, Ma Y (2022) SwinFusion: cross-domain long-range learning for general image fusion via Swin transformer. IEEE\/CAA J Autom Sin 9(7):1200\u20131217","journal-title":"IEEE\/CAA J Autom Sin"},{"key":"2464_CR57","doi-asserted-by":"crossref","unstructured":"Jia X, Zhu C, Li M, Tang W, Zhou W (2021) Llvip: A visible-infrared paired dataset for low-light vision. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 3496\u20133504","DOI":"10.1109\/ICCVW54120.2021.00389"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02464-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-024-02464-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02464-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,7]],"date-time":"2025-06-07T04:34:01Z","timestamp":1749270841000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-024-02464-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,22]]},"references-count":57,"journal-issue":{"issue":"5-6","published-print":{"date-parts":[[2025,6]]}},"alternative-id":["2464"],"URL":"https:\/\/doi.org\/10.1007\/s13042-024-02464-1","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"type":"print","value":"1868-8071"},{"type":"electronic","value":"1868-808X"}],"subject":[],"published":{"date-parts":[[2024,11,22]]},"assertion":[{"value":"21 November 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 November 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 November 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}