{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T14:51:19Z","timestamp":1770821479786,"version":"3.50.1"},"reference-count":55,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2022,6,10]],"date-time":"2022-06-10T00:00:00Z","timestamp":1654819200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["2021YFF0603904"],"award-info":[{"award-number":["2021YFF0603904"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["U2033213"],"award-info":[{"award-number":["U2033213"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["2021YFS0319"],"award-info":[{"award-number":["2021YFS0319"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2021YFF0603904"],"award-info":[{"award-number":["2021YFF0603904"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U2033213"],"award-info":[{"award-number":["U2033213"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2021YFS0319"],"award-info":[{"award-number":["2021YFS0319"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Sichuan Science and Technology Program","award":["2021YFF0603904"],"award-info":[{"award-number":["2021YFF0603904"]}]},{"name":"the Sichuan Science and Technology Program","award":["U2033213"],"award-info":[{"award-number":["U2033213"]}]},{"name":"the Sichuan Science and Technology Program","award":["2021YFS0319"],"award-info":[{"award-number":["2021YFS0319"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The Enhanced Flight Vision System (EFVS) plays a significant role in the Next-Generation low visibility aircraft landing technology, where the involvement of optical sensing systems increases the visual dimension for pilots. This paper focuses on deploying infrared and visible image fusion systems in civil flight, particularly generating integrated results to contend with registration deviation and adverse weather conditions. The existing enhancement methods push ahead with metrics-driven integration, while the dynamic distortion and the continuous visual scene are overlooked in the landing stage. Hence, the proposed visual enhancement scheme is divided into homography estimation and image fusion based on deep learning. A lightweight framework integrating hardware calibration and homography estimation is designed for image calibration before fusion and reduces the offset between image pairs. The transformer structure adopting the self-attention mechanism in distinguishing composite properties is incorporated into a concise autoencoder to construct the fusion strategy, and the improved weight allocation strategy enhances the feature combination. These things considered, a flight verification platform accessing the performances of different algorithms is built to capture image pairs in the landing stage. Experimental results confirm the equilibrium of the proposed scheme in perception-inspired and feature-based metrics compared to other approaches.<\/jats:p>","DOI":"10.3390\/rs14122789","type":"journal-article","created":{"date-parts":[[2022,6,12]],"date-time":"2022-06-12T23:55:24Z","timestamp":1655078124000},"page":"2789","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Infrared and Visible Image Fusion with Deep Neural Network in Enhanced Flight Vision System"],"prefix":"10.3390","volume":"14","author":[{"given":"Xuyang","family":"Gao","sequence":"first","affiliation":[{"name":"School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yibing","family":"Shi","sequence":"additional","affiliation":[{"name":"School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qi","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Mechatronic Engineering, Southwest Petroleum University, Chengdu 610500, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiang","family":"Fu","sequence":"additional","affiliation":[{"name":"College of Computer Science, Civil Aviation Flight University of China, Guanghan 618307, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuezhou","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Computer Science, Civil Aviation Flight University of China, Guanghan 618307, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,10]]},"reference":[{"key":"ref_1","first-page":"533","article-title":"Assessing Dual-Sensor Enhanced Flight Vision Systems to Enable Equivalent Visual Operations","volume":"14","author":"Kramer","year":"2017","journal-title":"J. Aerosp. Inf. Syst."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Fadhil, A.F., Kanneganti, R., Gupta, L., Eberle, H., and Vaidyanathan, R. (2019). Fusion of Enhanced and Synthetic Vision System Images for Runway and Horizon Detection. Sensors, 19.","DOI":"10.3390\/s19173802"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Cross, J., Schneider, J., and Cariani, P. (2013, January 1). MMW radar enhanced vision systems: The Helicopter Autonomous Landing System (HALS) and Radar-Enhanced Vision System (REVS) are rotary and fixed wing enhanced flight vision systems that enable safe flight operations in degraded visual environments. Proceedings of the Degraded Visual Environments: Enhanced, Synthetic, and External Vision Solutions 2013, Baltimore, MA, USA.","DOI":"10.1117\/12.2016302"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Shelton, K.J., Kramer, L.J., Ellis, K., and Rehfeld, S.A. (2012, January 14\u201318). Synthetic and Enhanced Vision Systems (SEVS) for NextGen simulation and flight test performance evaluation. Proceedings of the 2012 IEEE\/AIAA 31st Digital Avionics Systems Conference (DASC), Williamsburg, VA, USA.","DOI":"10.1109\/DASC.2012.6382967"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Goshi, D.S., Rhoads, C., McKitterick, J., and Case, T. (2019, January 13). Millimeter wave imaging for fixed wing zero visibility landing. Proceedings of the Passive and Active Millimeter-Wave Imaging XXII, Baltimore, MA, USA.","DOI":"10.1117\/12.2519921"},{"key":"ref_6","first-page":"12","article-title":"Cfit Prevention with Combined Enhanced Flight Vision System and Synthetic Vision System","volume":"87","author":"Iradukunda","year":"2021","journal-title":"Adv. Aerosp. Technol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"012048","DOI":"10.1088\/1742-6596\/1518\/1\/012048","article-title":"Infrared Image Enhancement by Multi-Modal Sensor Fusion in Enhanced Synthetic Vision System","volume":"1518","author":"Cheng","year":"2020","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1729881419845528","DOI":"10.1177\/1729881419845528","article-title":"Visual\u2013inertial fusion-based registration between real and synthetic images in airborne combined vision system","volume":"16","author":"Zhang","year":"2019","journal-title":"Int. J. Adv. Robot. Syst."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.inffus.2020.05.002","article-title":"Object fusion tracking based on visible and infrared images: A comprehensive review","volume":"63","author":"Zhang","year":"2020","journal-title":"Inf. Fusion"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.inffus.2018.02.004","article-title":"Infrared and visible image fusion methods and applications: A survey","volume":"45","author":"Ma","year":"2019","journal-title":"Inf. Fusion"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.inffus.2016.02.001","article-title":"Infrared and visible image fusion via gradient transfer and total variation minimization","volume":"31","author":"Ma","year":"2016","journal-title":"Inf. Fusion"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/j.infrared.2017.05.007","article-title":"Infrared and visual image fusion through infrared feature extraction and visual information preservation","volume":"83","author":"Zhang","year":"2017","journal-title":"Infrared Phys. Technol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.inffus.2017.10.007","article-title":"Deep learning for pixel-level image fusion: Recent advances and future prospects","volume":"42","author":"Liu","year":"2018","journal-title":"Inf. Fusion"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1850018","DOI":"10.1142\/S0219691318500182","article-title":"Infrared and visible image fusion with convolutional neural networks","volume":"16","author":"Liu","year":"2018","journal-title":"Int. J. Wavelets Multiresolut. Inf. Process."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Li, W., Cao, D., Peng, Y., and Yang, C. (2021). MSNet: A Multi-Stream Fusion Network for Remote Sensing Spatiotemporal Fusion Based on Transformer and Convolution. Remote Sens., 13.","DOI":"10.3390\/rs13183724"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2614","DOI":"10.1109\/TIP.2018.2887342","article-title":"DenseFuse: A Fusion Approach to Infrared and Visible Images","volume":"28","author":"Li","year":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1109\/TPAMI.2020.3012548","article-title":"U2Fusion: A Unified Unsupervised Image Fusion Network","volume":"44","author":"Xu","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Jia, X., Zhu, C., Li, M., Tang, W., and Zhou, W. (2021, January 11\u201317). LLVIP: A Visible-infrared Paired Dataset for Low-light Vision. Proceedings of the 2021 IEEE\/CVF International Conference on Computer Vision Workshops (ICCVW), Montreal, BC, Canada.","DOI":"10.1109\/ICCVW54120.2021.00389"},{"key":"ref_19","first-page":"171","article-title":"A real-time photogrammetric algorithm for sensor and synthetic image fusion with application to aviation combined vision","volume":"XL-3","author":"Lebedev","year":"2014","journal-title":"ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","article-title":"Distinctive Image Features from Scale-Invariant Keypoints","volume":"60","author":"Lowe","year":"2004","journal-title":"Int. J. Comput. Vis."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Bay, H., Tuytelaars, T., and Van Gool, L. (2006, January 7\u201313). SURF: Speeded Up Robust Features. Proceedings of the European Conference on Computer Vision, Berlin, Heidelberg.","DOI":"10.1007\/11744023_32"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Rublee, E., Rabaud, V., Konolige, K., and Bradski, G. (2011, January 6\u201313). ORB: An efficient alternative to SIFT or SURF. Proceedings of the 2011 International Conference on Computer Vision, Barcelona, Spain.","DOI":"10.1109\/ICCV.2011.6126544"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1145\/358669.358692","article-title":"Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography","volume":"24","author":"Fischler","year":"1981","journal-title":"Commun. ACM"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.mri.2019.05.037","article-title":"Learning image-based spatial transformations via convolutional neural networks: A review","volume":"64","author":"Tustison","year":"2019","journal-title":"Magn. Reson. Imaging"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Chang, C.H., Chou, C.N., and Chang, E.Y. (2017, January 21\u201326). CLKN: Cascaded Lucas-Kanade Networks for Image Alignment. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.402"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2346","DOI":"10.1109\/LRA.2018.2809549","article-title":"Unsupervised Deep Homography: A Fast and Robust Homography Estimation Model","volume":"3","author":"Nguyen","year":"2018","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1016\/j.neucom.2021.03.099","article-title":"Image stitching via deep homography estimation","volume":"450","author":"Zhao","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zhang, X., Ye, P., and Xiao, G. (2020, January 14\u201319). VIFB: A Visible and Infrared Image Fusion Benchmark. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Seattle, WA, USA.","DOI":"10.1109\/CVPRW50498.2020.00060"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1708","DOI":"10.1016\/j.procs.2015.02.114","article-title":"Image Denoising Using Multiresolution Singular Value Decomposition Transform","volume":"46","author":"Malini","year":"2015","journal-title":"Procedia Comput. Sci."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1193","DOI":"10.1007\/s11760-013-0556-9","article-title":"Image fusion based on pixel significance using cross bilateral filter","volume":"9","year":"2015","journal-title":"Signal Image Video Process."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1109\/JSEN.2015.2478655","article-title":"Fusion of Infrared and Visible Sensor Images Based on Anisotropic Diffusion and Karhunen-Loeve Transform","volume":"16","author":"Bavirisetti","year":"2016","journal-title":"IEEE Sens. J."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"6480","DOI":"10.1364\/AO.55.006480","article-title":"Fusion of infrared and visible images for night-vision context enhancement","volume":"55","author":"Zhou","year":"2016","journal-title":"Appl. Opt."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.infrared.2016.01.009","article-title":"Two-scale image fusion of visible and infrared images using saliency detection","volume":"76","author":"Bavirisetti","year":"2016","journal-title":"Infrared Phys. Technol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"5576","DOI":"10.1007\/s00034-019-01131-z","article-title":"Multi-scale Guided Image and Video Fusion: A Fast and Efficient Approach","volume":"38","author":"Bavirisetti","year":"2019","journal-title":"Circuits Syst. Signal Process."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Bavirisetti, D.P., Xiao, G., and Liu, G. (2017, January 10\u201313). Multi-sensor image fusion based on fourth order partial differential equations. Proceedings of the 20th International Conference on Information Fusion (Fusion), Xi\u2019an, China.","DOI":"10.23919\/ICIF.2017.8009719"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.infrared.2017.02.005","article-title":"Infrared and visible image fusion based on visual saliency map and weighted least square optimization","volume":"82","author":"Ma","year":"2017","journal-title":"Infrared Phys. Technol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"9645","DOI":"10.1109\/TIM.2020.3005230","article-title":"NestFuse: An Infrared and Visible Image Fusion Architecture Based on Nest Connection and Spatial\/Channel Attention Models","volume":"69","author":"Li","year":"2020","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_38","first-page":"1","article-title":"STDFusionNet: An Infrared and Visible Image Fusion Network Based on Salient Target Detection","volume":"70","author":"Ma","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Fu, Y., and Wu, X.J. (2021, January 10\u201315). A Dual-Branch Network for Infrared and Visible Image Fusion. Proceedings of the 2020 25th International Conference on Pattern Recognition (ICPR), Milan, Italy.","DOI":"10.1109\/ICPR48806.2021.9412293"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.inffus.2018.09.004","article-title":"FusionGAN: A generative adversarial network for infrared and visible image fusion","volume":"48","author":"Ma","year":"2019","journal-title":"Inf. Fusion"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"4980","DOI":"10.1109\/TIP.2020.2977573","article-title":"DDcGAN: A Dual-Discriminator Conditional Generative Adversarial Network for Multi-Resolution Image Fusion","volume":"29","author":"Ma","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Li, H., Wu, X., and Kittler, J. (2018, January 20\u201324). Infrared and Visible Image Fusion using a Deep Learning Framework. Proceedings of the 2018 24th International Conference on Pattern Recognition (ICPR), Beijing, China.","DOI":"10.1109\/ICPR.2018.8546006"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Yang, T., Li, G., Li, J., Zhang, Y., Zhang, X., Zhang, Z., and Li, Z. (2016). A Ground-Based Near Infrared Camera Array System for UAV Auto-Landing in GPS-Denied Environment. Sensors, 16.","DOI":"10.3390\/s16091393"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"103","DOI":"10.14358\/PERS.81.2.103","article-title":"Direct Linear Transformation from Comparator Coordinates into Object Space Coordinates in Close-Range Photogrammetry","volume":"81","author":"Karara","year":"2015","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.-Y., and Kweon, I.S. (2018, January 8\u201314). CBAM: Convolutional Block Attention Module. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_46","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/TIP.2003.819861","article-title":"Image quality assessment: From error visibility to structural similarity","volume":"13","author":"Zhou","year":"2004","journal-title":"IEEE Trans. Image Process."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"626","DOI":"10.1049\/el:20060693","article-title":"Image fusion metric based on mutual information and Tsallis entropy","volume":"42","author":"Cvejic","year":"2006","journal-title":"Electron. Lett."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1421","DOI":"10.1016\/j.imavis.2007.12.002","article-title":"A new automated quality assessment algorithm for image fusion","volume":"27","author":"Chen","year":"2009","journal-title":"Image Vis. Comput."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/j.inffus.2005.10.001","article-title":"A human perception inspired quality metric for image fusion based on regional information","volume":"8","author":"Chen","year":"2007","journal-title":"Inf. Fusion"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1016\/j.physd.2004.11.001","article-title":"A nonlinear correlation measure for multivariable data set","volume":"200","author":"Wang","year":"2005","journal-title":"Phys. D Nonlinear Phenom."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Shanshan, L., Richang, H., and Xiuqing, W. (2008, January 7\u20139). A novel similarity based quality metric for image fusion. Proceedings of the International Conference on Audio, Language and Image Processing, Shanghai, China.","DOI":"10.1109\/ICALIP.2008.4589989"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"430","DOI":"10.1109\/TIP.2005.859378","article-title":"Image information and visual quality","volume":"15","author":"Sheikh","year":"2006","journal-title":"IEEE Trans. Image Process."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Hasler, D., and Suesstrunk, S.E. (2003, January 17). Measuring colorfulness in natural images. Proceedings of the Human Vision and Electronic Imaging VIII, Santa Clara, CA, USA.","DOI":"10.1117\/12.477378"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Tan, M., Chen, B., Pang, R., Vasudevan, V., Sandler, M., Howard, A., and Le, Q.V. (2019, January 15\u201320). MnasNet: Platform-Aware Neural Architecture Search for Mobile. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00293"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/12\/2789\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:27:34Z","timestamp":1760138854000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/12\/2789"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,10]]},"references-count":55,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2022,6]]}},"alternative-id":["rs14122789"],"URL":"https:\/\/doi.org\/10.3390\/rs14122789","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,10]]}}}