{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T19:23:21Z","timestamp":1792783401463,"version":"4.5.0"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T00:00:00Z","timestamp":1782950400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T00:00:00Z","timestamp":1782950400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100000104","name":"National Aeronautics and Space Administration","doi-asserted-by":"publisher","award":["80NSSC23K1342"],"award-info":[{"award-number":["80NSSC23K1342"]}],"id":[{"id":"10.13039\/100000104","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000104","name":"National Aeronautics and Space Administration","doi-asserted-by":"publisher","award":["80NSSC23K1342"],"award-info":[{"award-number":["80NSSC23K1342"]}],"id":[{"id":"10.13039\/100000104","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Carnegie Mellon University","id":[{"id":"https:\/\/ror.org\/05x2bcf33","id-type":"ROR","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Integr Mater Manuf Innov"],"published-print":{"date-parts":[[2026,9]]},"abstract":"Abstract<\/jats:title>\n \n X-ray Micro Computed Tomography (X-\u00b5CT) is increasingly regarded as the gold standard for inspecting additively manufactured components used in fatigue-critical applications. However, segmentation of X-\u00b5CT data remains inconsistent across users and applications. Additionally, it is unclear if voxel-wise metrics of segmentation quality, such as the Dice coefficient or Intersection over Union (IoU), are relevant to fatigue performance. In this work, we evaluated global binary thresholding, adaptive thresholding, hysteresis thresholding, and a 2.5D U-Net on X-\u00b5CT scans of Powder Bed Fusion \u2013 Laser Beam manufactured Ti-6Al-4V rotating bending fatigue specimens from the NIST AMBench 2025 challenge (AMB2025-03-FL) to quantify segmentation-induced measurement bias and assess its impact on predicting the fatigue-initiating pore. To identify the fatigue-initiating pore, the Murakami\n \n \n $$\\sqrt{A}$$<\/jats:tex-math>\n \n \n A<\/mml:mi>\n <\/mml:msqrt>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n parameter was modified using beam theory to account for the stress gradient due to bending. This modification enables localization of the most critical pore, independent of the applied stress. Using the modified\n \n \n $$\\sqrt{A}$$<\/jats:tex-math>\n \n \n A<\/mml:mi>\n <\/mml:msqrt>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n metric, the global binary thresholding, adaptive thresholding, and hysteresis thresholding predicted the fatigue-initiating pore correctly for three out of the four test cases. The 2.5D U-Net was able to predict the fatigue-initiating pore in all four cases despite having a lower Dice coefficient and IoU values when compared to the other segmentation models. In fatigue-critical applications, these limitations are most consequential for small, near-surface pores, where X-ray reflection artifacts cause threshold-based methods to underestimate pore size. Such pores can occur even in high-density PBF-LB parts. Consequently, voxel-wise metrics such as Dice coefficient or IoU do not indicate whether a segmentation approach can identify the true fatigue-initiating pore, as they weight all pixels equally, highlighting the need for fatigue-aware, feature-based methods for segmentation of X-\u00b5CT data.\n <\/jats:p>","DOI":"10.1007\/s40192-026-00467-0","type":"journal-article","created":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T15:53:26Z","timestamp":1783007606000},"page":"489-505","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Impact of Segmentation Methods on Predicting Fatigue-Initiating Pores from X-ray Computed Tomography Data"],"prefix":"10.1007","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-9747-3326","authenticated-orcid":false,"given":"Justin P.","family":"Miner","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-4575-8505","authenticated-orcid":false,"given":"Michelle","family":"Hobdari","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-4878-8753","authenticated-orcid":false,"given":"Henry","family":"Stanton","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5947-4900","authenticated-orcid":false,"given":"Sneha Prabha","family":"Narra","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,2]]},"reference":[{"key":"467_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.addma.2020.101552","volume":"36","author":"JV Gordon","year":"2020","unstructured":"Gordon JV, Narra SP, Cunningham RW, Liu H, Chen H, Suter RM, Beuth JL, Rollett AD (2020) Defect structure process maps for laser powder bed fusion additive manufacturing. Addit Manuf 36:101552. https:\/\/doi.org\/10.1016\/j.addma.2020.101552","journal-title":"Addit Manuf"},{"issue":"1","key":"467_CR2","doi-asserted-by":"publisher","first-page":"3088","DOI":"10.1038\/s41467-019-10973-9","volume":"10","author":"SMH Hojjatzadeh","year":"2019","unstructured":"Hojjatzadeh SMH, Parab ND, Yan W, Guo Q, Xiong L, Zhao C, Qu M, Escano LI, Xiao X, Fezzaa K, Everhart W, Sun T, Chen L (2019) Pore elimination mechanisms during 3d printing of metals. Nat Commun 10(1):3088. https:\/\/doi.org\/10.1038\/s41467-019-10973-9","journal-title":"Nat Commun"},{"key":"467_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.addma.2022.103298","volume":"61","author":"Z Snow","year":"2023","unstructured":"Snow Z, Scime L, Ziabari A, Fisher B, Paquit V (2023) Observation of spatter-induced stochastic lack-of-fusion in laser powder bed fusion using in situ process monitoring. Addit Manuf 61:103298. https:\/\/doi.org\/10.1016\/j.addma.2022.103298","journal-title":"Addit Manuf"},{"key":"467_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijfatigue.2024.108428","volume":"187","author":"T Reddy","year":"2024","unstructured":"Reddy T, Ngo A, Miner JP, Gobert C, Beuth JL, Rollett AD, Lewandowski JJ, Narra SP (2024) Fatigue-based process window for laser beam powder bed fusion additive manufacturing. Int J Fatigue 187:108428. https:\/\/doi.org\/10.1016\/j.ijfatigue.2024.108428","journal-title":"Int J Fatigue"},{"key":"467_CR5","volume-title":"Metal Fatigue: Effects of Small Defects and Nonmetallic Inclusions","author":"Y Murakami","year":"2019","unstructured":"Murakami Y (2019) Metal Fatigue: Effects of Small Defects and Nonmetallic Inclusions. Academic Press, Cambridge"},{"issue":"3","key":"467_CR6","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1089\/3dp.2018.0060","volume":"5","author":"A Plessis","year":"2018","unstructured":"Plessis A, Yadroitsev I, Yadroitsava I, Le Roux SG (2018) X-ray microcomputed tomography in additive manufacturing: a review of the current technology and applications. 3D Printing and Additive Manufacturing 5(3):227\u2013247. https:\/\/doi.org\/10.1089\/3dp.2018.0060","journal-title":"3D Printing and Additive Manufacturing"},{"key":"467_CR7","doi-asserted-by":"publisher","unstructured":"Toda H (2021) 7. In: X-ray CT: Hardware and software techniques, pp. 381\u2013439. Springer, Singapore https:\/\/doi.org\/10.1007\/978-981-16-0590-1_7","DOI":"10.1007\/978-981-16-0590-1_7"},{"key":"467_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.addma.2021.102372","volume":"48","author":"FH Kim","year":"2021","unstructured":"Kim FH, Yeung H, Garboczi EJ (2021) Characterizing the effects of laser control in laser powder bed fusion on near-surface pore formation via combined analysis of in-situ melt pool monitoring and x-ray computed tomography. Addit Manuf 48:102372. https:\/\/doi.org\/10.1016\/j.addma.2021.102372","journal-title":"Addit Manuf"},{"key":"467_CR9","doi-asserted-by":"publisher","first-page":"1102","DOI":"10.1016\/j.mex.2018.09.005","volume":"5","author":"A du Plessis","year":"2018","unstructured":"du Plessis A, Sperling P, Beerlink A, Tshabalala L, Hoosain S, Mathe N, le Roux SG (2018) Standard method for microct-based additive manufacturing quality control 1: Porosity analysis. MethodsX 5:1102\u20131110. https:\/\/doi.org\/10.1016\/j.mex.2018.09.005","journal-title":"MethodsX"},{"key":"467_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.addma.2021.101899","volume":"39","author":"J Lifton","year":"2021","unstructured":"Lifton J, Liu T (2021) An adaptive thresholding algorithm for porosity measurement of additively manufactured metal test samples via x-ray computed tomography. Addit Manuf 39:101899. https:\/\/doi.org\/10.1016\/j.addma.2021.101899","journal-title":"Addit Manuf"},{"key":"467_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/OJIM.2024.3477569","volume":"3","author":"B Jolley","year":"2024","unstructured":"Jolley B, Knott C, Sparkman D, Uchic M (2024) Comparative analysis of internal porosity in am ti64 using x-ray computed tomography and mechanical polishing serial sectioning. IEEE Open J Instrument Measurement 3:1\u201311. https:\/\/doi.org\/10.1109\/OJIM.2024.3477569","journal-title":"IEEE Open J Instrument Measurement"},{"key":"467_CR12","doi-asserted-by":"publisher","DOI":"10.18434\/mds2-3734","author":"N Hrabe","year":"2025","unstructured":"Hrabe N, Benzing J, Moser N, Derimow N, Kafka O, Saville A, Glaessgen E, Newman JA, Yeratapally S, Pribe J, Weber G, Chu S-H, Rogers R, Allen C, Glaubitz E (2025) AMB2025-03 high-cycle rotating bending fatigue tests of PBF-L Ti-6Al-4V. Nat Inst Standards Technol. https:\/\/doi.org\/10.18434\/mds2-3734","journal-title":"Nat Inst Standards Technol"},{"issue":"11","key":"467_CR13","doi-asserted-by":"publisher","first-page":"3230","DOI":"10.1007\/s11837-021-04863-z","volume":"73","author":"BR Jolley","year":"2021","unstructured":"Jolley BR, Uchic MD, Sparkman D, Chapman M, Schwalbach EJ (2021) Application of serial sectioning to evaluate the performance of x-ray computed tomography for quantitative porosity measurements in additively manufactured metals. JOM 73(11):3230\u20133239. https:\/\/doi.org\/10.1007\/s11837-021-04863-z","journal-title":"JOM"},{"key":"467_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.addma.2024.104506","volume":"95","author":"JP Miner","year":"2024","unstructured":"Miner JP, Ngo A, Gobert C, Reddy T, Lewandowski JJ, Rollett AD, Beuth J, Narra SP (2024) Impact of melt pool geometry variability on lack-of-fusion porosity and fatigue life in powder bed fusion-laser beam Ti\u20136Al\u20134V. Addit Manuf 95:104506. https:\/\/doi.org\/10.1016\/j.addma.2024.104506","journal-title":"Addit Manuf"},{"key":"467_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.addma.2020.101460","volume":"36","author":"C Gobert","year":"2020","unstructured":"Gobert C, Kudzal A, Sietins J, Mock C, Sun J, McWilliams B (2020) Porosity segmentation in x-ray computed tomography scans of metal additively manufactured specimens with machine learning. Addit Manuf 36:101460. https:\/\/doi.org\/10.1016\/j.addma.2020.101460","journal-title":"Addit Manuf"},{"key":"467_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.addma.2024.104323","volume":"90","author":"Y Wang","year":"2024","unstructured":"Wang Y, Ng C-H, Bermingham M, Dargusch M (2024) Machine learning driven instance segmentation providing new porosity insights into wire arc directed energy deposited ti-22v-4al. Addit Manuf 90:104323. https:\/\/doi.org\/10.1016\/j.addma.2024.104323","journal-title":"Addit Manuf"},{"issue":"1","key":"467_CR17","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1038\/s44334-025-00021-4","volume":"2","author":"IZ Era","year":"2025","unstructured":"Era IZ, Ahmed I, Liu Z, Das S (2025) An unsupervised approach towards promptable porosity segmentation in laser powder bed fusion by segment anything. npj Advanced Manufacturing 2(1):10. https:\/\/doi.org\/10.1038\/s44334-025-00021-4","journal-title":"npj Advanced Manufacturing"},{"key":"467_CR18","unstructured":"Hexagon: VGStudio. https:\/\/volumegraphics.hexagon.com\/en\/products\/vgstudio.html. Accessed 09 Oct 2025"},{"key":"467_CR19","unstructured":"Thermo Fisher Scientific (2025) Avizo software for materials science image analysis. https:\/\/www.thermofisher.com\/us\/en\/home\/electron-microscopy\/products\/software-em-3d-vis\/avizo-software.html. Accessed 09 Oct 2025"},{"key":"467_CR20","unstructured":"Comet Technologies Canada Inc. (2025) Dragonfly 3D world. https:\/\/dragonfly.comet.tech\/. Accessed 09 Oct 2025"},{"issue":"3","key":"467_CR21","doi-asserted-by":"publisher","DOI":"10.1115\/1.4053078","volume":"22","author":"VWH Wong","year":"2021","unstructured":"Wong VWH, Ferguson M, Law KH, Lee Y-TT, Witherell P (2021) Segmentation of additive manufacturing defects using U-Net. J Comput Inf Sci Eng 22(3):031005. https:\/\/doi.org\/10.1115\/1.4053078","journal-title":"J Comput Inf Sci Eng"},{"key":"467_CR22","doi-asserted-by":"publisher","DOI":"10.1007\/s10845-023-02296-w","author":"C Desrosiers","year":"2024","unstructured":"Desrosiers C, Letenneur M, Bernier F, Pich\u00e9 N, Provencher B, Cheriet F, Guibault F, Brailovski V (2024) Automated porosity segmentation in laser powder bed fusion part using computed tomography: a validity study. J Intell Manuf. https:\/\/doi.org\/10.1007\/s10845-023-02296-w","journal-title":"J Intell Manuf"},{"key":"467_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.tmater.2025.100065","volume":"8","author":"JS Rathore","year":"2025","unstructured":"Rathore JS, King A, Le Bourdais F, Garandet J-P (2025) In-depth analysis of ct resolution impact on porosity evaluation in laser powder bed fusion additive manufacturing. Tomography of Materials and Structures 8:100065. https:\/\/doi.org\/10.1016\/j.tmater.2025.100065","journal-title":"Tomography of Materials and Structures"},{"key":"467_CR24","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1016\/j.compositesa.2019.04.029","volume":"123","author":"I Tretiak","year":"2019","unstructured":"Tretiak I, Smith RA (2019) A parametric study of segmentation thresholds for x-ray ct porosity characterisation in composite materials. Compos A Appl Sci Manuf 123:10\u201324. https:\/\/doi.org\/10.1016\/j.compositesa.2019.04.029","journal-title":"Compos A Appl Sci Manuf"},{"issue":"3\u20134","key":"467_CR25","doi-asserted-by":"publisher","first-page":"202200017","DOI":"10.1002\/gamm.202200017","volume":"45","author":"U Gebhardt","year":"2022","unstructured":"Gebhardt U, Schulz P, Ra\u00dfloff A, Koch I, Gude M, K\u00e4stner M (2022) Influence of ct image processing on the predicted impact of pores on fatigue of additively manufactured ti6al4v and alsi10mg. GAMM-Mitteilungen 45(3\u20134):202200017. https:\/\/doi.org\/10.1002\/gamm.202200017","journal-title":"GAMM-Mitteilungen"},{"issue":"1","key":"467_CR26","doi-asserted-by":"publisher","first-page":"7308","DOI":"10.1038\/s41598-017-06504-5","volume":"7","author":"S Tammas-Williams","year":"2017","unstructured":"Tammas-Williams S, Withers PJ, Todd I, Prangnell PB (2017) The influence of porosity on fatigue crack initiation in additively manufactured titanium components. Sci Rep 7(1):7308. https:\/\/doi.org\/10.1038\/s41598-017-06504-5","journal-title":"Sci Rep"},{"issue":"1","key":"467_CR27","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1007\/s10704-020-00463-1","volume":"225","author":"JM Erickson","year":"2020","unstructured":"Erickson JM, Rahman A, Spear AD (2020) A void descriptor function to uniquely characterize pore networks and predict ductile-metal failure properties. Int J Fract 225(1):47\u201367. https:\/\/doi.org\/10.1007\/s10704-020-00463-1","journal-title":"Int J Fract"},{"key":"467_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.actamat.2021.117464","volume":"223","author":"DS Watring","year":"2022","unstructured":"Watring DS, Benzing JT, Kafka OL, Liew L-A, Moser NH, Erickson J, Hrabe N, Spear AD (2022) Evaluation of a modified void descriptor function to uniquely characterize pore networks and predict fracture-related properties in additively manufactured metals. Acta Mater 223:117464. https:\/\/doi.org\/10.1016\/j.actamat.2021.117464","journal-title":"Acta Mater"},{"key":"467_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijfatigue.2025.109213","volume":"202","author":"S Sun","year":"2026","unstructured":"Sun S, Choi YR, Bayoumy D, Miller JD, Liu Q (2026) Empirical fatigue life model based on micro-ct defect severity indicator for laser powder-bed additively manufactured ti-6al-4v. Int J Fatigue 202:109213. https:\/\/doi.org\/10.1016\/j.ijfatigue.2025.109213","journal-title":"Int J Fatigue"},{"key":"467_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.addma.2025.104935","volume":"110","author":"N Huang","year":"2025","unstructured":"Huang N, Furton ET, Mundada YA, Beese AM (2025) Identifying fracture location in additively manufactured samples based on defect characteristics: Demonstration using alsi10mg. Addit Manuf 110:104935. https:\/\/doi.org\/10.1016\/j.addma.2025.104935","journal-title":"Addit Manuf"},{"key":"467_CR31","doi-asserted-by":"publisher","unstructured":"Suresh S (1998) 1. In: Fatigue of Materials, pp. 1\u201336. Cambridge University Press, Cambridge https:\/\/doi.org\/10.1017\/CBO9780511806575","DOI":"10.1017\/CBO9780511806575"},{"key":"467_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijfatigue.2020.106063","volume":"144","author":"R Shrestha","year":"2021","unstructured":"Shrestha R, Simsiriwong J, Shamsaei N (2021) Fatigue behavior of additive manufactured 316l stainless steel under axial versus rotating-bending loading: synergistic effects of stress gradient, surface roughness, and volumetric defects. Int J Fatigue 144:106063. https:\/\/doi.org\/10.1016\/j.ijfatigue.2020.106063","journal-title":"Int J Fatigue"},{"key":"467_CR33","volume-title":"Metallic materials\u2014rotating bar bending fatigue testing","author":"International Organization for Standardization","year":"2021","unstructured":"International Organization for Standardization (2021) Metallic materials\u2014rotating bar bending fatigue testing. Standard, International Organization for Standardization, Geneva"},{"key":"467_CR34","doi-asserted-by":"publisher","unstructured":"Miner J, Narra SP, Hobdari M, Stanton H (2026) Dataset of processed X-ray computed tomography scans comparing segmentation methods from AMBench 2025-03 Rotating Bending Fatigue Challenge https:\/\/doi.org\/10.1184\/R1\/31378690","DOI":"10.1184\/R1\/31378690"},{"key":"467_CR35","doi-asserted-by":"publisher","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015, pp. 234\u2013241. Springer, Cham https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"467_CR36","unstructured":"Comet Technologies Canada Inc. (2024) Dragonfly 2024.1. https:\/\/dragonfly.comet.tech\/"},{"key":"467_CR37","doi-asserted-by":"publisher","unstructured":"Zeiler MD (2012) ADADELTA: An adaptive learning rate method. https:\/\/doi.org\/10.48550\/arXiv.1212.5701","DOI":"10.48550\/arXiv.1212.5701"},{"key":"467_CR38","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1016\/j.engfracmech.2017.11.002","volume":"187","author":"S Romano","year":"2018","unstructured":"Romano S, Br\u00fcckner-Foit A, Brand\u00e3o A, Gumpinger J, Ghidini T, Beretta S (2018) Fatigue properties of AlSi10Mg obtained by additive manufacturing: defect-based modelling and prediction of fatigue strength. Eng Fract Mech 187:165\u2013189. https:\/\/doi.org\/10.1016\/j.engfracmech.2017.11.002","journal-title":"Eng Fract Mech"},{"issue":"2","key":"467_CR39","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1016\/0925-8388(95)02057-8","volume":"234","author":"FX Gil Mur","year":"1996","unstructured":"Gil Mur FX, Rodr\u00edguez D, Planell JA (1996) Influence of tempering temperature and time on the $$\\alpha ^{\\prime }$$-Ti-6Al-4V martensite. J Alloy Compd 234(2):287\u2013289. https:\/\/doi.org\/10.1016\/0925-8388(95)02057-8","journal-title":"J Alloy Compd"},{"key":"467_CR40","doi-asserted-by":"publisher","unstructured":"Shaikh A, Kumar S, Dawari A, Kirwai S, Patil A, Singh R (2019) Effect of temperature and cooling rates on the $$\\alpha + \\beta $$ morphology of Ti-6Al-4V alloy. In: SICE 2018 (2nd International Conference on Structural Integrity and Exhibition 2018). Procedia Structural Integrity 14, 782\u2013789 https:\/\/doi.org\/10.1016\/j.prostr.2019.07.056","DOI":"10.1016\/j.prostr.2019.07.056"}],"container-title":["Integrating Materials and Manufacturing Innovation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40192-026-00467-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40192-026-00467-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40192-026-00467-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T15:08:48Z","timestamp":1789657728000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40192-026-00467-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,2]]},"references-count":40,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2026,9]]}},"alternative-id":["467"],"URL":"https:\/\/doi.org\/10.1007\/s40192-026-00467-0","relation":{},"ISSN":["2193-9764","2193-9772"],"issn-type":[{"value":"2193-9764","type":"print"},{"value":"2193-9772","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,2]]},"assertion":[{"value":"6 February 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 May 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 July 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":1,"name":"Ethics","label":"Conflict of interest","group":{"name":"EthicsHeading","label":"Declarations"}}]}}