{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T05:01:38Z","timestamp":1775278898273,"version":"3.50.1"},"reference-count":136,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"5","license":[{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62506355"],"award-info":[{"award-number":["62506355"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Pattern Anal. Mach. Intell."],"published-print":{"date-parts":[[2026,5]]},"DOI":"10.1109\/tpami.2025.3649294","type":"journal-article","created":{"date-parts":[[2025,12,30]],"date-time":"2025-12-30T18:37:35Z","timestamp":1767119855000},"page":"4983-5003","source":"Crossref","is-referenced-by-count":0,"title":["On the Transferability and Discriminability of Representation Learning in Unsupervised Domain Adaptation"],"prefix":"10.1109","volume":"48","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7985-5743","authenticated-orcid":false,"given":"Wenwen","family":"Qiang","sequence":"first","affiliation":[{"name":"National Key Laboratory of Space Integrated Information System, Institute of Software Chinese Academy of Sciences, Beijing, China"}]},{"given":"Ziyin","family":"Gu","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Space Integrated Information System, Institute of Software Chinese Academy of Sciences, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7735-6676","authenticated-orcid":false,"given":"Lingyu","family":"Si","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Space Integrated Information System, Institute of Software Chinese Academy of Sciences, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3376-1522","authenticated-orcid":false,"given":"Jiangmeng","family":"Li","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Space Integrated Information System, Institute of Software Chinese Academy of Sciences, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2311-6757","authenticated-orcid":false,"given":"Changwen","family":"Zheng","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Space Integrated Information System, Institute of Software Chinese Academy of Sciences, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3546-6305","authenticated-orcid":false,"given":"Fuchun","family":"Sun","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Space Integrated Information System, Department of Computer Science and Technology, Tsinghua University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6016-6465","authenticated-orcid":false,"given":"Hui","family":"Xiong","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Thrust, Information Hub, Department of Computer Science &amp; Engineering, School of Engineering, Hong Kong University of Science and Technology, Guangzhou, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.5555\/2946645.2946704"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.3036956"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.52202\/068431-2140"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2024.3352392"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2024.3459626"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11784"},{"key":"ref7","first-page":"214","article-title":"Wasserstein generative adversarial networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Arjovsky","year":"2017"},{"key":"ref8","first-page":"7404","article-title":"Bridging theory and algorithm for domain adaptation","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Zhang","year":"2019"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i14.29493"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i2.27830"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3158637"},{"key":"ref12","first-page":"19304","article-title":"Pseudo-calibration: Improving predictive uncertainty estimation in unsupervised domain adaptation","volume-title":"Proc. 41st Int. Conf. Mach. Learn.","author":"Hu","year":"2024"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2024.3357258"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2024.3486617"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3128560"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00503"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3112815"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.107066"},{"key":"ref19","article-title":"Representation learning with contrastive predictive coding","author":"Oord","year":"2018"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.5555\/3524938.3525087"},{"key":"ref21","first-page":"11834","article-title":"Intriguing properties of contrastive losses","author":"Chen","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref22","first-page":"5468","article-title":"Understanding self-training for gradual domain adaptation","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Kumar","year":"2020"},{"key":"ref23","first-page":"2514","article-title":"Margin-aware adversarial domain adaptation with optimal transport","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Dhouib","year":"2020"},{"key":"ref24","article-title":"Robust optimal transport with applications in generative modeling and domain adaptation","author":"Balaji","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01247"},{"key":"ref26","article-title":"Domain adaptation with conditional distribution matching and generalized label shift","author":"Tachet des Combes","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref27","article-title":"Heuristic domain adaptation","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Cui","year":"2020"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00410"},{"key":"ref29","article-title":"Pixel-level cycle association: A new perspective for domain adaptive semantic segmentation","author":"Kang","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00875"},{"issue":"1","key":"ref31","first-page":"723","article-title":"A kernel two-sample test","volume":"13","author":"Gretton","year":"2012","journal-title":"J. Mach. Learn. Res."},{"key":"ref32","article-title":"Deep domain confusion: Maximizing for domain invariance","author":"Tzeng","year":"2014"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v30i1.10306"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33014122"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-43424-2_19"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00848"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/WACV56688.2023.00059"},{"key":"ref38","first-page":"1081","article-title":"Transferability vs. discriminability: Batch spectral penalization for adversarial domain adaptation","volume-title":"Proc. 36th Int. Conf. Mach. Learn., ICML 9-15 Jun. 2019, Long Beach, California, USA, Ser. Proc. Mach. Learn.","volume":"97","author":"Chen","year":"2019"},{"key":"ref39","first-page":"1647","article-title":"Conditional adversarial domain adaptation","author":"Long","year":"2018","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref40","first-page":"1994","article-title":"CYCADA: Cycle-consistent adversarial domain adaptation","volume-title":"Proc. 35th Int. Conf. Mach. Learn., ICML Stockholmsm\u00e4ssan, Stockholm, Sweden, July 10-15, 2018, Ser. Proc. Mach. Learn. Res.","volume":"80","author":"Hoffman","year":"2018"},{"key":"ref41","first-page":"700","article-title":"Unsupervised image-to-image translation networks","volume-title":"Proc. Adv. in Neural Inf. Process. Syst. 30: Annu. Conf. Neural Inf. Process. Syst. 2017 December 4-9, 2017, Long Beach, CA, USA","author":"Liu","year":"2017"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00262"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58598-3_44"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01299"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2024.3437212"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00408"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2024.3438154"},{"key":"ref48","first-page":"22968","article-title":"Cycle self-training for domain adaptation","volume":"34","author":"Liu","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref49","article-title":"Domain adaptation: Learning bounds and algorithms","volume-title":"Proc. Conf. Learn. Theory","author":"Mansour","year":"2009"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-009-5152-4"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-34106-9_13"},{"key":"ref52","first-page":"738","article-title":"A Pac-Bayesian approach for domain adaptation with specialization to linear classifiers","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Germain","year":"2013"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783368"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-71246-8_45"},{"key":"ref55","first-page":"6872","article-title":"Domain adaptation with asymmetrically-relaxed distribution alignment","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Wu","year":"2019"},{"key":"ref56","first-page":"7523","article-title":"On learning invariant representations for domain adaptation","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Zhao","year":"2019"},{"key":"ref57","first-page":"11710","article-title":"Balancing discriminability and transferability for source-free domain adaptation","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Kundu","year":"2022"},{"key":"ref58","first-page":"1081","article-title":"Transferability vs. discriminability: Batch spectral penalization for adversarial domain adaptation","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Chen","year":"2019"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00889"},{"key":"ref60","article-title":"The information bottleneck method","author":"Tishby","year":"2000"},{"issue":"114","key":"ref61","first-page":"403","article-title":"An information theoretic framework for multi-view learning","volume-title":"Proc. COLT","author":"Sridharan","year":"2008"},{"key":"ref62","article-title":"A survey on multi-view learning","author":"Xu","year":"2013"},{"key":"ref63","article-title":"Self-supervised Learning from a Multi-view Perspective","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Xu","year":"2021"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1002\/047174882x"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2013.50"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.319"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2858826"},{"key":"ref68","article-title":"Efficient estimation of word representations in vector space","volume-title":"Proc. Int. Conf. Learn. Represent., Workshop","author":"Mikolov","year":"2013"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1145\/2601097.2601175"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1023\/a:1026543900054"},{"issue":"4","key":"ref71","first-page":"13","article-title":"Geometry of probability simplex via optimal transport","volume":"2","author":"Li","year":"2018"},{"key":"ref72","first-page":"1716","article-title":"Wasserstein of Wasserstein loss for learning generative models","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Dukler","year":"2019"},{"key":"ref73","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-540-71050-9","volume-title":"Optimal Transport: Old and New","volume":"338","author":"Villani","year":"2009"},{"key":"ref74","first-page":"5769","article-title":"Improved training of Wasserstein GANs","volume":"30","author":"Gulrajani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref75","article-title":"On the regularization of Wasserstein GANs","volume-title":"Proc. Int. Conf. Learn. Representations, Int. Conf. Learn. Representations","author":"Petzka","year":"2017"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1109\/18.272494"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.572"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-15561-1_16"},{"key":"ref79","article-title":"VISDA: The visual domain adaptation challenge","author":"Peng","year":"2017"},{"key":"ref80","doi-asserted-by":"publisher","DOI":"10.1109\/34.291440"},{"key":"ref81","article-title":"Reading digits in natural images with unsupervised feature learning","volume-title":"Proc. NIPS","author":"Netzer","year":"2011"},{"key":"ref82","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref83","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00149"},{"key":"ref84","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref85","first-page":"97","article-title":"Learning transferable features with deep adaptation networks","volume-title":"Proc. 32nd Int. Conf. Mach. Learn., ICML 2015, Lille, France, 6-11 Jul. Ser. JMLR Workshop Conf. Proc.","volume":"37","author":"Long","year":"2015"},{"key":"ref86","first-page":"2208","article-title":"Deep transfer learning with joint adaptation networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Long","year":"2017"},{"key":"ref87","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00887"},{"key":"ref88","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.316"},{"key":"ref89","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00392"},{"key":"ref90","first-page":"7404","article-title":"Bridging theory and algorithm for domain adaptation","volume-title":"Proc. 36th Int. Conf. Mach. Learn., ICML 9-15 Jun. 2019, Long Beach, California, USA, Ser.","volume":"97","author":"Zhang","year":"2019"},{"key":"ref91","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01053"},{"key":"ref92","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01395"},{"key":"ref93","doi-asserted-by":"publisher","DOI":"10.52202\/068431-1511"},{"key":"ref94","first-page":"19847","article-title":"Connect, not collapse: Explaining contrastive learning for unsupervised domain adaptation","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Shen","year":"2022"},{"key":"ref95","first-page":"7982","article-title":"Identifiability conditions for domain adaptation","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Gulrajani","year":"2022"},{"key":"ref96","first-page":"11455","article-title":"Partial disentanglement for domain adaptation","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Kong","year":"2022"},{"key":"ref97","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01228-1_32"},{"key":"ref98","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01219-9_18"},{"key":"ref99","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.01081"},{"key":"ref100","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00218"},{"key":"ref101","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00686"},{"key":"ref102","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58555-6_42"},{"key":"ref103","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58542-6_18"},{"key":"ref104","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58568-6_38"},{"key":"ref105","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00382"},{"key":"ref106","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00439"},{"key":"ref107","article-title":"CDTRANS: Cross-domain transformer for unsupervised domain adaptation","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Xu","year":"2021"},{"key":"ref108","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i8.28674"},{"key":"ref109","first-page":"6028","article-title":"Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Liang","year":"2020"},{"key":"ref110","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3103390"},{"key":"ref111","first-page":"29393","article-title":"Exploiting the intrinsic neighborhood structure for source-free domain adaptation","volume":"34","author":"Yang","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref112","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/402"},{"key":"ref113","first-page":"3635","article-title":"Model adaptation: Historical contrastive learning for unsupervised domain adaptation without source data","volume":"34","author":"Huang","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref114","doi-asserted-by":"publisher","DOI":"10.3390\/s23094436"},{"key":"ref115","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00107"},{"key":"ref116","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01265"},{"key":"ref117","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00127"},{"key":"ref118","doi-asserted-by":"publisher","DOI":"10.2307\/2279372"},{"key":"ref119","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-009-0275-4"},{"key":"ref120","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00525"},{"key":"ref121","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46475-6_7"},{"key":"ref122","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.350"},{"key":"ref123","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.352"},{"key":"ref124","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3128560"},{"key":"ref125","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00936"},{"key":"ref126","article-title":"KL guided domain adaptation","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Nguyen","year":"2022"},{"key":"ref127","article-title":"Graph-relational domain adaptation","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Xu","year":"2022"},{"key":"ref128","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2699184"},{"key":"ref129","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00283"},{"key":"ref130","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.461"},{"key":"ref131","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2723009"},{"key":"ref132","article-title":"LSUN: Construction of a large-scale image dataset using deep learning with humans in the loop","author":"Yu","year":"2015"},{"key":"ref133","article-title":"Turkergaze: Crowdsourcing saliency with webcam based eye tracking","author":"Xu","year":"2015"},{"key":"ref134","first-page":"21464","article-title":"Energy-based out-of-distribution detection","volume":"33","author":"Liu","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref135","first-page":"5102","article-title":"Domain agnostic learning with disentangled representations","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Peng","year":"2019"},{"key":"ref136","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2022\/237"}],"container-title":["IEEE Transactions on Pattern Analysis and Machine Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/34\/11474534\/11319256.pdf?arnumber=11319256","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T04:17:33Z","timestamp":1775276253000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11319256\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5]]},"references-count":136,"journal-issue":{"issue":"5"},"URL":"https:\/\/doi.org\/10.1109\/tpami.2025.3649294","relation":{},"ISSN":["0162-8828","2160-9292","1939-3539"],"issn-type":[{"value":"0162-8828","type":"print"},{"value":"2160-9292","type":"electronic"},{"value":"1939-3539","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5]]}}}