{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T00:25:52Z","timestamp":1780619152213,"version":"3.54.1"},"reference-count":62,"publisher":"ASME International","issue":"11","license":[{"start":{"date-parts":[[2024,7,22]],"date-time":"2024-07-22T00:00:00Z","timestamp":1721606400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.asme.org\/publications-submissions\/publishing-information\/legal-policies"}],"content-domain":{"domain":["asmedigitalcollection.asme.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,11,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Materials science requires the collection and analysis of great quantities of data. These data almost invariably require various post-acquisition computation to remove noise, classify observations, fit parametric models, or perform other operations. Recently developed machine-learning (ML) algorithms have demonstrated great capability for performing many of these operations, and often produce higher quality output than traditional methods. However, it has been widely observed that such algorithms often suffer from issues such as limited generalizability and the tendency to \u201cover fit\u201d to the input data. In order to address such issues, this work introduces a metacomputing framework capable of systematically selecting, tuning, and training the best available machine-learning model in order to process an input dataset. In addition, a unique \u201ccross-training\u201d methodology is used to incorporate underlying physics or multiphysics relationships into the structure of the resultant ML model. This metacomputing approach is demonstrated on four example problems: repairing \u201cgaps\u201d in a multiphysics dataset, improving the output of electron back-scatter detection crystallographic measurements, removing spurious artifacts from X-ray microtomography data, and identifying material constitutive relationships from tensile test data. The performance of the metacomputing framework on these disparate problems is discussed, as are future plans for further deploying metacomputing technologies in the context of materials science and mechanical engineering.<\/jats:p>","DOI":"10.1115\/1.4064975","type":"journal-article","created":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T12:51:27Z","timestamp":1709297487000},"update-policy":"https:\/\/doi.org\/10.1115\/crossmarkpolicy-asme","source":"Crossref","is-referenced-by-count":2,"title":["Machine-Learning Metacomputing for Materials Science Data"],"prefix":"10.1115","volume":"24","author":[{"given":"J. C.","family":"Steuben","sequence":"first","affiliation":[{"name":"U.S. Naval Research Laboratory Computational Multiphysics Systems Laboratory, , Washington, DC 20375"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"A. B.","family":"Geltmacher","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/04d23a975","id-type":"ROR","asserted-by":"publisher"}],"name":"United States Naval Research Laboratory Simulations and Imaging Section, Materials Science and Technology Division, , Washington, DC 20375"},{"name":"U.S. Naval Research Laboratory Simulations and Imaging Section, Materials Science and Technology Division, , Washington, DC 20375"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"S. N.","family":"Rodriguez","sequence":"additional","affiliation":[{"name":"U.S. Naval Research Laboratory Computational Multiphysics Systems Laboratory, , Washington, DC 20375"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"A. J.","family":"Birnbaum","sequence":"additional","affiliation":[{"name":"U.S. Naval Research Laboratory Computational Multiphysics Systems Laboratory, , Washington, DC 20375"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"B. D.","family":"Graber","sequence":"additional","affiliation":[{"name":"U.S. Naval Research Laboratory Computational Multiphysics Systems Laboratory, , Washington, DC 20375"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"A. K.","family":"Rawlings","sequence":"additional","affiliation":[{"name":"U.S. Naval Research Laboratory Computational Multiphysics Systems Laboratory, , Washington, DC 20375"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"A. P.","family":"Iliopoulos","sequence":"additional","affiliation":[{"name":"U.S. Naval Research Laboratory Computational Multiphysics Systems Laboratory, , Washington, DC 20375"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"J. G.","family":"Michopoulos","sequence":"additional","affiliation":[{"name":"U.S. Naval Research Laboratory Computational Multiphysics Systems Laboratory, , Washington, DC 20375"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"33","published-online":{"date-parts":[[2024,7,22]]},"reference":[{"issue":"21","key":"2024072213585555600_CIT0001","doi-asserted-by":"publisher","first-page":"1900808","DOI":"10.1002\/advs.201900808","article-title":"Data-Driven Materials Science: Status, Challenges, and Perspectives","volume":"6","author":"Himanen","year":"2019","journal-title":"Adv. Sci."},{"issue":"1","key":"2024072213585555600_CIT0002","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1016\/j.cad.2012.06.006","article-title":"Key Computational Modeling Issues in Integrated Computational Materials Engineering","volume":"45","author":"Panchal","year":"2013","journal-title":"Comput.-Aided Design"},{"issue":"1","key":"2024072213585555600_CIT0003","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1088\/0957-0233\/16\/1\/001","article-title":"Role of High-Throughput Characterization Tools in Combinatorial Materials Science","volume":"16","author":"Potyrailo","year":"2004","journal-title":"Meas. Sci. Technol."},{"issue":"32","key":"2024072213585555600_CIT0004","doi-asserted-by":"publisher","first-page":"6016","DOI":"10.1002\/anie.200603675","article-title":"Combinatorial and High-Throughput Materials Science","volume":"46","author":"Maier","year":"2007","journal-title":"Angew. Chem., Int. Ed."},{"issue":"1","key":"2024072213585555600_CIT0005","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.scriptamat.2005.12.061","article-title":"3D Crystallographic and Morphological Analysis of Coarse Martensite: Combining EBSD and Serial Sectioning","volume":"55","author":"Rowenhorst","year":"2006","journal-title":"Scr. Mater."},{"issue":"5","key":"2024072213585555600_CIT0006","doi-asserted-by":"publisher","first-page":"053208","DOI":"10.1063\/1.4946894","article-title":"Perspective: Materials Informatics and Big Data: Realization of the \u201cFourth Paradigm\u201d of Science in Materials Science","volume":"4","author":"Agrawal","year":"2016","journal-title":"APL Mater."},{"issue":"3","key":"2024072213585555600_CIT0007","doi-asserted-by":"publisher","first-page":"032001","DOI":"10.1088\/2515-7639\/ab084b","article-title":"From DFT to Machine Learning: Recent Approaches to Materials Science-A Review","volume":"2","author":"Schleder","year":"2019","journal-title":"J. Phys.: Mater."},{"issue":"3","key":"2024072213585555600_CIT0008","doi-asserted-by":"publisher","first-page":"338","DOI":"10.1002\/inf2.12028","article-title":"Machine Learning in Materials Science","volume":"1","author":"Wei","year":"2019","journal-title":"InfoMat"},{"key":"2024072213585555600_CIT0009","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/978-3-030-22475-2_1","volume-title":"A Systematic Review on Supervised and Unsupervised Machine Learning Algorithms for Data Science","author":"Alloghani","year":"2020"},{"issue":"1","key":"2024072213585555600_CIT0010","doi-asserted-by":"publisher","first-page":"011011","DOI":"10.1115\/1.4055852","article-title":"Monotonic Gaussian Process for Physics-Constrained Machine Learning With Materials Science Applications","volume":"23","author":"Tran","year":"2023","journal-title":"ASME J. Comput. Inf. Sci. Eng."},{"issue":"1","key":"2024072213585555600_CIT0011","doi-asserted-by":"publisher","first-page":"011009","DOI":"10.1115\/1.4055546","article-title":"Acceleration of a Physics-Based Machine Learning Approach for Modeling and Quantifying Model-Form Uncertainties and Performing Model Updating","volume":"23","author":"Azzi","year":"2023","journal-title":"ASME J. Comput. Inf. Sci. Eng."},{"key":"2024072213585555600_CIT0012","doi-asserted-by":"crossref","DOI":"10.1017\/9781009089517","volume-title":"Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control","author":"Brunton","year":"2022"},{"issue":"2","key":"2024072213585555600_CIT0013","doi-asserted-by":"publisher","first-page":"022022","DOI":"10.1088\/1742-6596\/1168\/2\/022022","article-title":"An Overview of Overfitting and Its Solutions","volume":"1168","author":"Ying","year":"2019","journal-title":"J. Phys.: Conf. Ser."},{"key":"2024072213585555600_CIT0014","first-page":"153","volume-title":"Generalization Error in Deep Learning","author":"Jakubovitz","year":"2019"},{"key":"2024072213585555600_CIT0015","author":"Nadeau","year":"1999"},{"key":"2024072213585555600_CIT0016","doi-asserted-by":"crossref","DOI":"10.1093\/acref\/9780191884276.001.0001","volume-title":"A Dictionary of the Internet","author":"Ince","year":"2019"},{"issue":"6","key":"2024072213585555600_CIT0017","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1145\/129888.129890","article-title":"Metacomputing","volume":"35","author":"Smarr","year":"1992","journal-title":"Commun. ACM"},{"issue":"2","key":"2024072213585555600_CIT0018","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1177\/109434209701100205","article-title":"Globus: A Metacomputing Infrastructure Toolkit","volume":"11","author":"Foster","year":"1997","journal-title":"Int. J. Supercomput. Appl. High Perform. Comput."},{"key":"2024072213585555600_CIT0019","first-page":"106","article-title":"Metacomputing. New Direction in High Performance Computing","volume-title":"In Information Technology Applications in Biomedicine. ITAB \u201997, Proceedings of the IEEE Engineering in Medicine and Biology Society Region 8 International Conference","author":"Matyska","year":"1997"},{"issue":"12","key":"2024072213585555600_CIT0020","doi-asserted-by":"publisher","first-page":"1751","DOI":"10.1016\/S0167-8191(98)00076-3","article-title":"Specifying Resources and Services in Metacomputing Environments","volume":"24","author":"Brune","year":"1998","journal-title":"Parallel Comput."},{"issue":"5\u20136","key":"2024072213585555600_CIT0021","first-page":"537","article-title":"Metacomputing: From Workstation Clusters to Internet Computing","volume":"15","author":"Gentzsch","year":"1999","journal-title":"Future Gener. Comput. Syst."},{"key":"2024072213585555600_CIT0022","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1007\/3-540-45627-9_6","article-title":"From Metacomputing to Grid Computing, Evolution or Revolution?","volume-title":"SOFSEM 2001: Theory and Practice of Informatics","author":"Laforenza","year":"2001"},{"key":"2024072213585555600_CIT0023","first-page":"49","article-title":"Meta \u03c8: A Web-Based Metacomputing Environment to Build a Computational Chemistry Problem Solving Environment","volume-title":"Proceedings of the 10th Euromicro Conference on Parallel, Distributed and Network-Based Processing, EUROMICRO-PDP\u201902, IEEE Computer Society","author":"Baraglia","year":"2002"},{"issue":"6","key":"2024072213585555600_CIT0024","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3354584","article-title":"A Survey of Metaprogramming Languages","volume":"52","author":"Lilis","year":"2019","journal-title":"ACM Comput. Surv."},{"key":"2024072213585555600_CIT0025","doi-asserted-by":"publisher","first-page":"102142","DOI":"10.1016\/j.jocs.2023.102142","article-title":"Top-Down Metacomputing With Algebraic Dimensionality Raising for Automating Theory-Building to Enable Directly Computable Multiphysics Models","volume":"73","author":"Michopoulos","year":"2023","journal-title":"J. Comput. Sci."},{"issue":"6","key":"2024072213585555600_CIT0026","doi-asserted-by":"publisher","first-page":"060820","DOI":"10.1115\/1.4063103","article-title":"Metacomputing for Directly Computable Multiphysics Models","volume":"23","author":"Michopoulos","year":"2023","journal-title":"ASME J. Comput. Inf. Sci. Eng."},{"key":"2024072213585555600_CIT0027","first-page":"759","article-title":"Selecting an Appropriate Metamodel: The Case for NURBs Metamodels","volume-title":"Volume 2: 31st Design Automation Conference, Parts A and B of International Design Engineering Technical Conferences and Computers and Information in Engineering Conference","author":"Turner","year":"2005"},{"key":"2024072213585555600_CIT0028","first-page":"25","volume-title":"The Supervised Learning No-Free-Lunch Theorems","author":"Wolpert","year":"2002"},{"issue":"1","key":"2024072213585555600_CIT0029","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1038\/s41592-018-0261-2","article-title":"U-Net: Deep Learning for Cell Counting, Detection, and Morphometry","volume":"16","author":"Falk","year":"2019","journal-title":"Nat. Methods"},{"key":"2024072213585555600_CIT0030","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41592-023-01879-y","article-title":"The Cell Tracking Challenge: 10 Years of Objective Benchmarking","author":"Ma\u0161ka","year":"2023","journal-title":"Nat. Methods"},{"key":"2024072213585555600_CIT0031","first-page":"539","article-title":"On Sequential Sampling for Global Metamodeling in Engineering Design","volume-title":"International Design Engineering Technical Conferences and Computers and Information in Engineering Conference","author":"Jin","year":"2002"},{"key":"2024072213585555600_CIT0032","doi-asserted-by":"publisher","first-page":"1413","DOI":"10.1007\/s11222-016-9696-4","article-title":"Practical Bayesian Model Evaluation Using Leave-One-Out Cross-Validation and WAIC","volume":"27","author":"Vehtari","year":"2017","journal-title":"Statist. Comput."},{"issue":"2","key":"2024072213585555600_CIT0033","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1093\/bib\/bbp059","article-title":"Dealing With Missing Values in Large-Scale Studies: Microarray Data Imputation and Beyond","volume":"11","author":"Aittokallio","year":"2009","journal-title":"Brief. Bioinform."},{"issue":"5","key":"2024072213585555600_CIT0034","doi-asserted-by":"publisher","first-page":"498","DOI":"10.1093\/bib\/bbq080","article-title":"Missing Value Imputation for Gene Expression Data: Computational Techniques to Recover Missing Data From Available Information","volume":"12","author":"Liew","year":"2010","journal-title":"Brief. Bioinform."},{"issue":"1","key":"2024072213585555600_CIT0035","doi-asserted-by":"publisher","first-page":"bbab489","DOI":"10.1093\/bib\/bbab489","article-title":"Evaluating the State of the Art in Missing Data Imputation for Clinical Data","volume":"23","author":"Luo","year":"2021","journal-title":"Brief. Bioinform."},{"key":"2024072213585555600_CIT0036","article-title":"Mxnet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems","volume-title":"preprint","author":"Chen","year":"2015"},{"key":"2024072213585555600_CIT0037","first-page":"012187","article-title":"Missing Data Filling Method Based on Linear Interpolation and lightgbm","volume-title":"Journal of Physics: Conference Series","author":"Huang","year":"2021"},{"issue":"3","key":"2024072213585555600_CIT0038","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1109\/TPAMI.2011.142","article-title":"Prototype Selection for Nearest Neighbor Classification: Taxonomy and Empirical Study","volume":"34","author":"Garcia","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2024072213585555600_CIT0039","volume-title":"Anderson Cook, CM: Response Surface Methodology: Process and Product Optimization Using Designed Experiments","author":"Myers","year":"2009"},{"issue":"10","key":"2024072213585555600_CIT0040","doi-asserted-by":"publisher","first-page":"1358","DOI":"10.1109\/tpami.2002.1039207","article-title":"Reconstructing Surfaces by Volumetric Regularization Using Radial Basis Functions","volume":"24","author":"Dinh","year":"2002","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"3","key":"2024072213585555600_CIT0041","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1007\/BF00889887","article-title":"The Origins of Kriging","volume":"22","author":"Cressie","year":"1990","journal-title":"Math. Geology"},{"key":"2024072213585555600_CIT0042","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jmp.2018.03.001","article-title":"A Tutorial on Gaussian Process Regression: Modelling, Exploring, and Exploiting Functions","volume":"85","author":"Schulz","year":"2018","journal-title":"J. Math. Psychol."},{"issue":"6","key":"2024072213585555600_CIT0043","doi-asserted-by":"publisher","first-page":"568","DOI":"10.1109\/72.97934","article-title":"A General Regression Neural Network","volume":"2","author":"Specht","year":"1991","journal-title":"IEEE Trans. Neural Netw."},{"key":"2024072213585555600_CIT0044","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1016\/j.compchemeng.2014.05.021","article-title":"Adaptive Sequential Sampling for Surrogate Model Generation With Artificial Neural Networks","volume":"68","author":"Eason","year":"2014","journal-title":"Comput. Chem. Eng."},{"issue":"4","key":"2024072213585555600_CIT0045","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1109\/5254.708428","article-title":"Support Vector Machines","volume":"13","author":"Hearst","year":"1998","journal-title":"IEEE Intel. Syst. Their Appl."},{"key":"2024072213585555600_CIT0046","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1007\/978-1-4302-5990-9_4","article-title":"Support Vector Regression","volume-title":"Efficient Learning Machines: Theories, Concepts, and Applications for Engineers and System Designers","author":"Awad","year":"2015"},{"issue":"1","key":"2024072213585555600_CIT0047","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1145\/234313.234346","article-title":"Learning Decision Tree Classifiers","volume":"28","author":"Quinlan","year":"1996","journal-title":"ACM Comput. Surveys (CSUR)"},{"key":"2024072213585555600_CIT0048","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1016\/j.advengsoft.2016.09.001","article-title":"Gtapprox: Surrogate Modeling for Industrial Design","volume":"102","author":"Belyaev","year":"2016","journal-title":"Adv. Eng. Soft."},{"issue":"5","key":"2024072213585555600_CIT0049","doi-asserted-by":"publisher","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy Function Approximation: A Gradient Boosting Machine","volume":"29","author":"Friedman","year":"2001","journal-title":"Ann. Statist."},{"issue":"15","key":"2024072213585555600_CIT0050","doi-asserted-by":"publisher","first-page":"1796","DOI":"10.3390\/rs11151796","article-title":"A Decision Tree Approach for Spatially Interpolating Missing Land Cover Data and Classifying Satellite Images","volume":"11","author":"Holloway","year":"2019","journal-title":"Remote Sens."},{"issue":"6","key":"2024072213585555600_CIT0051","doi-asserted-by":"crossref","first-page":"1135","DOI":"10.1214\/aos\/1176345632","article-title":"Estimation of the Mean of a Multivariate Normal Distribution","volume":"9","author":"Stein","year":"1981","journal-title":"Ann. Stat."},{"issue":"3","key":"2024072213585555600_CIT0052","doi-asserted-by":"publisher","first-page":"907","DOI":"10.1214\/aoms\/1177696968","article-title":"An Iterative Procedure for Estimation in Contingency Tables","volume":"41","author":"Fienberg","year":"1970","journal-title":"Ann. Math. Stat."},{"issue":"3","key":"2024072213585555600_CIT0053","doi-asserted-by":"crossref","first-page":"1236","DOI":"10.1214\/aos\/1176348768","article-title":"Variable Kernel Density Estimation","volume":"20","author":"Terrell","year":"1992","journal-title":"Ann. Statist."},{"key":"2024072213585555600_CIT0054","doi-asserted-by":"publisher","first-page":"659","DOI":"10.1007\/978-0-387-73003-5_196","article-title":"Gaussian Mixture Models.","volume":"741","author":"Reynolds","year":"2009","journal-title":"Encyclopedia Biom."},{"key":"2024072213585555600_CIT0055","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1007\/s10479-005-5724-z","article-title":"A Tutorial on the Cross-Entropy Method","volume":"134","author":"De Boer","year":"2005","journal-title":"Ann. Operat. Res."},{"key":"2024072213585555600_CIT0056","author":"Wolfram Research Inc"},{"key":"2024072213585555600_CIT0057","author":"Steuben","year":"2023"},{"key":"2024072213585555600_CIT0058","doi-asserted-by":"publisher","first-page":"101927","DOI":"10.1016\/j.eml.2022.101927","article-title":"Plasma Formation in Ambient Fluid From Hypervelocity Impacts","volume":"58","author":"Islam","year":"2023","journal-title":"Extreme Mech. Lett."},{"key":"2024072213585555600_CIT0059","doi-asserted-by":"crossref","DOI":"10.1007\/978-0-387-88136-2","volume-title":"Electron Backscatter Diffraction in Materials Science","author":"Schwartz","year":"2009"},{"issue":"1","key":"2024072213585555600_CIT0060","doi-asserted-by":"publisher","first-page":"627","DOI":"10.1146\/annurev.matsci.37.052506.084401","article-title":"Three-Dimensional Characterization of Microstructure by Electron Back-Scatter Diffraction","volume":"37","author":"Rollett","year":"2007","journal-title":"Annu. Rev. Mater. Res."},{"key":"2024072213585555600_CIT0061","first-page":"V002T02A035","article-title":"X-ray Marching for the Computational Modeling of Tomographic Systems Applied to Materials Applications","volume-title":"International Design Engineering Technical Conferences and Computers and Information in Engineering Conference","author":"Steuben","year":"2022"},{"issue":"8","key":"2024072213585555600_CIT0062","doi-asserted-by":"publisher","first-page":"1627","DOI":"10.1021\/ac60214a047","article-title":"Smoothing and Differentiation of Data by Simplified Least Squares Procedures.","volume":"36","author":"Savitzky","year":"1964","journal-title":"Anal. Chem."}],"container-title":["Journal of Computing and Information Science in Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/asmedigitalcollection.asme.org\/computingengineering\/article-pdf\/24\/11\/111005\/7355402\/jcise_24_11_111005.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/asmedigitalcollection.asme.org\/computingengineering\/article-pdf\/24\/11\/111005\/7355402\/jcise_24_11_111005.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,22]],"date-time":"2024-07-22T13:59:24Z","timestamp":1721656764000},"score":1,"resource":{"primary":{"URL":"https:\/\/asmedigitalcollection.asme.org\/computingengineering\/article\/24\/11\/111005\/1198095\/Machine-Learning-Metacomputing-for-Materials"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,22]]},"references-count":62,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2024,11,1]]}},"URL":"https:\/\/doi.org\/10.1115\/1.4064975","relation":{},"ISSN":["1530-9827","1944-7078"],"issn-type":[{"value":"1530-9827","type":"print"},{"value":"1944-7078","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,22]]},"article-number":"111005"}}