{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T00:18:11Z","timestamp":1768349891975,"version":"3.49.0"},"reference-count":58,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2024,5,4]],"date-time":"2024-05-04T00:00:00Z","timestamp":1714780800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,5,4]],"date-time":"2024-05-04T00:00:00Z","timestamp":1714780800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Advance Manufacturing Institute at University of Houston"},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1728321"],"award-info":[{"award-number":["1728321"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Intell Manuf"],"published-print":{"date-parts":[[2025,6]]},"DOI":"10.1007\/s10845-024-02358-7","type":"journal-article","created":{"date-parts":[[2024,5,4]],"date-time":"2024-05-04T11:01:33Z","timestamp":1714820493000},"page":"3009-3030","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Quantile regression-enriched event modeling framework for dropout analysis in high-temperature superconductor manufacturing"],"prefix":"10.1007","volume":"36","author":[{"given":"Mai","family":"Li","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2020-8066","authenticated-orcid":false,"given":"Ying","family":"Lin","sequence":"additional","affiliation":[]},{"given":"Qianmei","family":"Feng","sequence":"additional","affiliation":[]},{"given":"Wenjiang","family":"Fu","sequence":"additional","affiliation":[]},{"given":"Shenglin","family":"Peng","sequence":"additional","affiliation":[]},{"given":"Siwei","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Mahesh","family":"Paidpilli","sequence":"additional","affiliation":[]},{"given":"Chirag","family":"Goel","sequence":"additional","affiliation":[]},{"given":"Eduard","family":"Galstyan","sequence":"additional","affiliation":[]},{"given":"Venkat","family":"Selvamanickam","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,5,4]]},"reference":[{"key":"2358_CR1","doi-asserted-by":"publisher","DOI":"10.1002\/env.961","author":"G Adelfio","year":"2008","unstructured":"Adelfio, G., & Chiodi, M. (2008). Second-order diagnostics for space-time point processes with application to seismic events. Environmetrics. https:\/\/doi.org\/10.1002\/env.961","journal-title":"Environmetrics"},{"issue":"23\u201324","key":"2358_CR2","doi-asserted-by":"publisher","first-page":"2079","DOI":"10.1016\/j.physc.2010.07.005","volume":"470","author":"S Anders","year":"2010","unstructured":"Anders, S., Blamire, M., Buchholz, F.-I., Cr\u00e9t\u00e9, D.-G., Cristiano, R., Febvre, P., Fritzsch, L., Herr, A., Il\u2019Ichev, E., & Kohlmann, J. (2010). European roadmap on superconductive electronics\u2013status and perspectives. Physica C: Superconductivity, 470(23\u201324), 2079\u20132126.","journal-title":"Physica C: Superconductivity"},{"key":"2358_CR3","doi-asserted-by":"publisher","first-page":"39313","DOI":"10.1109\/access.2022.3165621","volume":"10","author":"N Aslam","year":"2022","unstructured":"Aslam, N., Rustam, F., Lee, E., Washington, P. B., & Ashraf, I. (2022). Sentiment analysis and emotion detection on cryptocurrency related tweets using ensemble LSTM-GRU model. IEEE Access, 10, 39313\u201339324. https:\/\/doi.org\/10.1109\/access.2022.3165621","journal-title":"IEEE Access"},{"key":"2358_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.softx.2020.100456","volume":"11","author":"M Barandas","year":"2020","unstructured":"Barandas, M., Folgado, D., Fernandes, L., Santos, S., Abreu, M., Bota, P., Liu, H., Schultz, T., & Gamboa, H. (2020). TSFEL: Time series feature extraction library. SoftwareX, 11, 100456.","journal-title":"SoftwareX"},{"key":"2358_CR5","first-page":"67","volume-title":"Stochastic point processes: Statistical analysis, theory and applications","author":"M Brown","year":"1972","unstructured":"Brown, M. (1972). Statistical analysis of non-homogeneous Poisson processes. In P. A. W. Lewis (Ed.), Stochastic point processes: Statistical analysis, theory and applications (pp. 67\u201389). Wiley."},{"issue":"1\u20132","key":"2358_CR6","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1515\/znb-2019-0103","volume":"75","author":"A Bussmann-Holder","year":"2020","unstructured":"Bussmann-Holder, A., & Keller, H. (2020). High-temperature superconductors: Underlying physics and applications. Zeitschrift f\u00fcr Naturforschung B, 75(1\u20132), 3\u201314. https:\/\/doi.org\/10.1515\/znb-2019-0103","journal-title":"Zeitschrift f\u00fcr Naturforschung B"},{"key":"2358_CR7","unstructured":"Chang, C.-H., Rampasek, L., & Goldenberg, A. (2017). Dropout feature ranking for deep learning models. https:\/\/arxiv.org\/abs\/1712.08645."},{"issue":"5","key":"2358_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/tasc.2021.3058868","volume":"31","author":"S Chen","year":"2021","unstructured":"Chen, S., Majkic, G., Jain, R., Pratap, R., Mohan, V., Goel, C., & Selvamanickam, V. (2021). Scale up of high-performance REBCO tapes in a pilot-scale advanced MOCVD tool with in-line 2D-XRD system. IEEE Transactions on Applied Superconductivity, 31(5), 1\u20135. https:\/\/doi.org\/10.1109\/tasc.2021.3058868","journal-title":"IEEE Transactions on Applied Superconductivity"},{"issue":"4","key":"2358_CR9","doi-asserted-by":"publisher","first-page":"337","DOI":"10.6000\/1929-6029.2015.04.04.4","volume":"4","author":"MV Cifuentes-Amado","year":"2015","unstructured":"Cifuentes-Amado, M. V., & Cepeda-Cuervo, E. (2015). Non-homogeneous Poisson process to model seasonal events: Application to the health diseases. International Journal of Statistics in Medical Research, 4(4), 337\u2013346.","journal-title":"International Journal of Statistics in Medical Research"},{"key":"2358_CR10","volume-title":"An introduction to the theory of point processes: Volume I: Elementary theory and methods","author":"DJ Daley","year":"2003","unstructured":"Daley, D. J., & Vere-Jones, D. (2003). An introduction to the theory of point processes: Volume I: Elementary theory and methods. Springer."},{"key":"2358_CR11","doi-asserted-by":"crossref","unstructured":"Du, N., Dai, H., Trivedi, R., Upadhyay, U., Gomez-Rodriguez, M., & Song, L. (2016). Recurrent marked temporal point processes. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.","DOI":"10.1145\/2939672.2939875"},{"issue":"2","key":"2358_CR12","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1214\/009053604000000067","volume":"32","author":"B Efron","year":"2004","unstructured":"Efron, B., Hastie, T., Johnstone, I., & Tibshirani, R. (2004). Least angle regression. The Annals of Statistics, 32(2), 407\u2013499. https:\/\/doi.org\/10.1214\/009053604000000067","journal-title":"The Annals of Statistics"},{"issue":"1","key":"2358_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v033.i01","volume":"33","author":"J Friedman","year":"2010","unstructured":"Friedman, J., Hastie, T., & Tibshirani, R. (2010). Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software, 33(1), 1.","journal-title":"Journal of Statistical Software"},{"key":"2358_CR14","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6668\/ab4638","author":"F G\u00f6m\u00f6ry","year":"2019","unstructured":"G\u00f6m\u00f6ry, F., \u0160ouc, J., Ad\u00e1mek, M., Ghabeli, A., Solovyov, M., & Vojen\u010diak, M. (2019). Impact of critical current fluctuations on the performance of a coated conductor tape. Superconductor Science and Technology. https:\/\/doi.org\/10.1088\/1361-6668\/ab4638","journal-title":"Superconductor Science and Technology"},{"issue":"3","key":"2358_CR15","doi-asserted-by":"publisher","first-page":"1671","DOI":"10.1109\/TASC.2008.2003983","volume":"18","author":"SA Gourlay","year":"2008","unstructured":"Gourlay, S. A. (2008). Challenges and prospects for the large-scale application of superconductivity. IEEE Transactions on Applied Superconductivity, 18(3), 1671\u20131680.","journal-title":"IEEE Transactions on Applied Superconductivity"},{"issue":"7","key":"2358_CR16","doi-asserted-by":"publisher","first-page":"4323","DOI":"10.3390\/app13074323","volume":"13","author":"S Hajdasz","year":"2023","unstructured":"Hajdasz, S., Kempski, A., Solak, K., Marc, M., Rusinski, J., & Szczesniak, P. (2023). Critical current degradation in HTS tapes for superconducting fault current limiter under repeated overcurrent. Applied Sciences, 13(7), 4323. https:\/\/doi.org\/10.3390\/app13074323","journal-title":"Applied Sciences"},{"issue":"1","key":"2358_CR17","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1093\/biomet\/58.1.83","volume":"58","author":"AG Hawkes","year":"1971","unstructured":"Hawkes, A. G. (1971). Spectra of some self-exciting and mutually exciting point processes. Biometrika, 58(1), 83\u201390.","journal-title":"Biometrika"},{"key":"2358_CR18","unstructured":"Huang, Z., Xu, W., & Yu, K. (2015). Bidirectional LSTM-CRF models for sequence tagging. https:\/\/arxiv.org\/abs\/1508.01991."},{"key":"2358_CR19","volume-title":"Point process theory and applications: Marked point and piecewise deterministic processes","author":"M Jacobsen","year":"2006","unstructured":"Jacobsen, M., & Gani, J. (2006). Point process theory and applications: Marked point and piecewise deterministic processes. Springer."},{"issue":"1","key":"2358_CR20","doi-asserted-by":"publisher","first-page":"33","DOI":"10.2307\/1913643","volume":"46","author":"R Koenker","year":"1978","unstructured":"Koenker, R., & Bassett, G. (1978). Regression quantiles. Econometrica, 46(1), 33. https:\/\/doi.org\/10.2307\/1913643","journal-title":"Econometrica"},{"issue":"4","key":"2358_CR21","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1257\/jep.15.4.143","volume":"15","author":"R Koenker","year":"2001","unstructured":"Koenker, R., & Hallock, K. F. (2001). Quantile regression. Journal of Economic Perspectives, 15(4), 143\u2013156. https:\/\/doi.org\/10.1257\/jep.15.4.143","journal-title":"Journal of Economic Perspectives"},{"issue":"5","key":"2358_CR22","doi-asserted-by":"publisher","first-page":"560","DOI":"10.1016\/j.ress.2006.05.002","volume":"92","author":"VV Krivtsov","year":"2007","unstructured":"Krivtsov, V. V. (2007). Practical extensions to NHPP application in repairable system reliability analysis. Reliability Engineering & System Safety, 92(5), 560\u2013562.","journal-title":"Reliability Engineering & System Safety"},{"issue":"4","key":"2358_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/tasc.2020.2971456","volume":"30","author":"TD Le","year":"2020","unstructured":"Le, T. D., Noumeir, R., Quach, H. L., Kim, J. H., Kim, J. H., & Kim, H. M. (2020). Critical temperature prediction for a superconductor: A variational Bayesian neural network approach. IEEE Transactions on Applied Superconductivity, 30(4), 1\u20135. https:\/\/doi.org\/10.1109\/tasc.2020.2971456","journal-title":"IEEE Transactions on Applied Superconductivity"},{"key":"2358_CR24","unstructured":"Li, M., Peng, S., Lin, Y., Feng, Q., Fu, W., Galstyan, E., Chen, S., & Jain, R. (2022). A spatial point process-based approach for dropout events modeling in high-temperature superconductor manufacturing. In: Proceedings of the 2022 IISE Annual Conference."},{"issue":"4","key":"2358_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/tasc.2016.2640942","volume":"27","author":"X-F Li","year":"2017","unstructured":"Li, X.-F., Yahia, A. B., Majkic, G., Kochat, M., Kar, S., & Selvamanickam, V. (2017). Reel-to-reel critical current measurement of REBCO coated conductors. IEEE Transactions on Applied Superconductivity, 27(4), 1\u20135. https:\/\/doi.org\/10.1109\/tasc.2016.2640942","journal-title":"IEEE Transactions on Applied Superconductivity"},{"issue":"4","key":"2358_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/tasc.2022.3140688","volume":"32","author":"Y Li","year":"2022","unstructured":"Li, Y., Chen, S., Paidpilli, M., Jain, R., Goel, C., & Selvamanickam, V. (2022). A reel-to-reel scanning hall probe microscope for characterizing long REBCO conductor in magnetic fields up to 5 Tesla. IEEE Transactions on Applied Superconductivity, 32(4), 1\u20136. https:\/\/doi.org\/10.1109\/tasc.2022.3140688","journal-title":"IEEE Transactions on Applied Superconductivity"},{"key":"2358_CR27","unstructured":"Linderman, S., & Adams, R. (2014). Discovering latent network structure in point process data. Proceedings of the 31st International Conference on Machine Learning, Proceedings of Machine Learning Research. https:\/\/proceedings.mlr.press\/v32\/linderman14.html"},{"key":"2358_CR28","doi-asserted-by":"publisher","DOI":"10.1088\/0953-2048\/26\/9\/093001","author":"Y Lvovsky","year":"2013","unstructured":"Lvovsky, Y., Stautner, E. W., & Zhang, T. (2013). Novel technologies and configurations of superconducting magnets for MRI. Superconductor Science and Technology. https:\/\/doi.org\/10.1088\/0953-2048\/26\/9\/093001","journal-title":"Superconductor Science and Technology"},{"issue":"3","key":"2358_CR29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/tasc.2014.2372902","volume":"25","author":"G Majkic","year":"2015","unstructured":"Majkic, G., Galstyan, E., & Selvamanickam, V. (2015). High performance 2G-HTS wire using a novel MOCVD system. IEEE Transactions on Applied Superconductivity, 25(3), 1\u20134. https:\/\/doi.org\/10.1109\/tasc.2014.2372902","journal-title":"IEEE Transactions on Applied Superconductivity"},{"key":"2358_CR30","volume-title":"Software reliability: measurement, prediction, application","author":"JD Musa","year":"1987","unstructured":"Musa, J. D., Iannino, A., & Okumoto, K. (1987). Software reliability: measurement, prediction, application. McGraw-Hill Inc."},{"issue":"7","key":"2358_CR31","doi-asserted-by":"publisher","first-page":"3670","DOI":"10.1016\/j.csda.2007.12.003","volume":"52","author":"J Nielsen","year":"2008","unstructured":"Nielsen, J., & Dean, C. B. (2008). Adaptive functional mixed NHPP models for the analysis of recurrent event panel data. Computational Statistics & Data Analysis, 52(7), 3670\u20133685.","journal-title":"Computational Statistics & Data Analysis"},{"key":"2358_CR32","doi-asserted-by":"publisher","DOI":"10.1088\/0953-2048\/27\/4\/044003","author":"X Obradors","year":"2014","unstructured":"Obradors, X., & Puig, T. (2014). Coated conductors for power applications: Materials challenges. Superconductor Science and Technology. https:\/\/doi.org\/10.1088\/0953-2048\/27\/4\/044003","journal-title":"Superconductor Science and Technology"},{"issue":"1","key":"2358_CR33","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1007\/s10948-014-2891-7","volume":"28","author":"TO Owolabi","year":"2015","unstructured":"Owolabi, T. O., Akande, K. O., & Olatunji, S. O. (2015). Estimation of superconducting transition temperature T C for superconductors of the doped MgB2 system from the crystal lattice parameters using support vector regression. Journal of Superconductivity and Novel Magnetism, 28(1), 75\u201381. https:\/\/doi.org\/10.1007\/s10948-014-2891-7","journal-title":"Journal of Superconductivity and Novel Magnetism"},{"key":"2358_CR34","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1098\/rsta.1896.0007","volume":"187","author":"K Pearson","year":"1896","unstructured":"Pearson, K. (1896). Mathematical contributions to the theory of evolution. III. Regression, heredity, and panmixia. Philosophical Transactions of the Royal Society of London Series A, Containing Papers of a Mathematical or Physical Character, 187, 253\u2013318. https:\/\/doi.org\/10.1098\/rsta.1896.0007","journal-title":"Philosophical Transactions of the Royal Society of London Series A, Containing Papers of a Mathematical or Physical Character"},{"issue":"11","key":"2358_CR35","doi-asserted-by":"publisher","first-page":"559","DOI":"10.1080\/14786440109462720","volume":"2","author":"K Pearson","year":"1901","unstructured":"Pearson, K. (1901). LIII. On lines and planes of closest fit to systems of points in space. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, 2(11), 559\u2013572. https:\/\/doi.org\/10.1080\/14786440109462720","journal-title":"The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science"},{"key":"2358_CR36","unstructured":"Peng, S., Li, M., Lin, Y., Feng, Q., Fu, W., Galstyan, E., Chen, S., & Jain, R. (2022). Dynamic uniformity modeling in superconductor manufacturing via vector autoregression analysis. In: Proceedings of the 2022 IISE Annual Conference."},{"issue":"1","key":"2358_CR37","doi-asserted-by":"publisher","first-page":"413","DOI":"10.1146\/annurev-statistics-042720-020233","volume":"8","author":"L Peng","year":"2021","unstructured":"Peng, L. (2021). Quantile regression for survival data. Annual Review of Statistics and Its Application, 8(1), 413\u2013437. https:\/\/doi.org\/10.1146\/annurev-statistics-042720-020233","journal-title":"Annual Review of Statistics and Its Application"},{"key":"2358_CR38","doi-asserted-by":"publisher","first-page":"119692","DOI":"10.1016\/j.energy.2020.119692","volume":"220","author":"X Peng","year":"2021","unstructured":"Peng, X., Wang, H., Lang, J., Li, W., Xu, Q., Zhang, Z., Cai, T., Duan, S., Liu, F., & Li, C. (2021). EALSTM-QR: Interval wind-power prediction model based on numerical weather prediction and deep learning. Energy, 220, 119692. https:\/\/doi.org\/10.1016\/j.energy.2020.119692","journal-title":"Energy"},{"issue":"5","key":"2358_CR39","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/tasc.2019.2899244","volume":"29","author":"R Pratap","year":"2019","unstructured":"Pratap, R., Majkic, G., Galstyan, E., Mohanasundaram, G., Chakradhar, S., & Selvamanickam, V. (2019). Growth of high-performance thick film REBCO tapes using advanced MOCVD. IEEE Transactions on Applied Superconductivity, 29(5), 1\u20135. https:\/\/doi.org\/10.1109\/tasc.2019.2899244","journal-title":"IEEE Transactions on Applied Superconductivity"},{"key":"2358_CR40","doi-asserted-by":"publisher","first-page":"53988","DOI":"10.1109\/ACCESS.2019.2902640","volume":"7","author":"A Rahangdale","year":"2019","unstructured":"Rahangdale, A., & Raut, S. (2019). Deep neural network regularization for feature selection in learning-to-rank. IEEE Access, 7, 53988\u201354006.","journal-title":"IEEE Access"},{"issue":"3","key":"2358_CR41","first-page":"299","volume":"33","author":"A Reinhart","year":"2018","unstructured":"Reinhart, A. (2018). A review of self-exciting spatio-temporal point processes and their applications. Statistical Science, 33(3), 299\u2013318.","journal-title":"Statistical Science"},{"issue":"20","key":"2358_CR42","doi-asserted-by":"publisher","first-page":"17193","DOI":"10.1007\/s00521-022-07687-3","volume":"34","author":"A Rostamian","year":"2022","unstructured":"Rostamian, A., & O\u2019Hara, J. G. (2022). Event prediction within directional change framework using a CNN-LSTM model. Neural Computing and Applications, 34(20), 17193\u201317205. https:\/\/doi.org\/10.1007\/s00521-022-07687-3","journal-title":"Neural Computing and Applications"},{"issue":"2","key":"2358_CR43","doi-asserted-by":"publisher","first-page":"461","DOI":"10.1214\/aos\/1176344136","volume":"6","author":"G Schwarz","year":"1978","unstructured":"Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6(2), 461\u2013464. https:\/\/doi.org\/10.1214\/aos\/1176344136","journal-title":"The Annals of Statistics"},{"key":"2358_CR44","unstructured":"scikit-learn. (2022). Linear regression tests returning F-statistic and p-values https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.feature_selection.f_regression.html#sklearn.feature_selection.f_regression"},{"key":"2358_CR45","doi-asserted-by":"publisher","unstructured":"Selvamanickam, V. (2012). 2 - High temperature superconductor (HTS) wires and tapes. High temperature superconductors (HTS) for energy applications (pp. 34\u201368). Woodhead Publishing. https:\/\/doi.org\/10.1533\/9780857095299.1.34","DOI":"10.1533\/9780857095299.1.34"},{"issue":"1","key":"2358_CR46","doi-asserted-by":"publisher","first-page":"8","DOI":"10.3390\/molecules26010008","volume":"26","author":"N Sizochenko","year":"2020","unstructured":"Sizochenko, N., & Hofmann, M. (2020). Predictive modeling of critical temperatures in superconducting materials. Molecules, 26(1), 8. https:\/\/doi.org\/10.3390\/molecules26010008","journal-title":"Molecules"},{"key":"2358_CR47","doi-asserted-by":"publisher","DOI":"10.1038\/s41524-018-0085-8","author":"V Stanev","year":"2018","unstructured":"Stanev, V., Oses, C., Kusne, A. G., Rodriguez, E., Paglione, J., Curtarolo, S., & Takeuchi, I. (2018). Machine learning modeling of superconducting critical temperature. npj Computational Materials. https:\/\/doi.org\/10.1038\/s41524-018-0085-8","journal-title":"npj Computational Materials"},{"issue":"1","key":"2358_CR48","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","volume":"58","author":"R Tibshirani","year":"1996","unstructured":"Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Methodological), 58(1), 267\u2013288.","journal-title":"Journal of the Royal Statistical Society: Series B (Methodological)"},{"key":"2358_CR49","first-page":"76","volume":"35","author":"A Veevers","year":"1986","unstructured":"Veevers, A. (1986). Repairable systems reliability: Modeling, inference, misconceptions and their causes. Journal of The Royal Statistical Society Series C-applied Statistics, 35, 76\u201376.","journal-title":"Journal of The Royal Statistical Society Series C-applied Statistics"},{"key":"2358_CR50","first-page":"5105","volume":"33","author":"M Wojtas","year":"2020","unstructured":"Wojtas, M., & Chen, K. (2020). Feature importance ranking for deep learning. Advances in Neural Information Processing Systems, 33, 5105\u20135114.","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"2","key":"2358_CR51","doi-asserted-by":"publisher","first-page":"1981","DOI":"10.1109\/tasc.2007.898186","volume":"17","author":"YY Xie","year":"2007","unstructured":"Xie, Y. Y., Tekletsadik, K., Hazelton, D., & Selvamanickam, V. (2007). Second generation high-temperature superconducting wires for fault current limiter applications. IEEE Transactions on Applied Superconductivity, 17(2), 1981\u20131985. https:\/\/doi.org\/10.1109\/tasc.2007.898186","journal-title":"IEEE Transactions on Applied Superconductivity"},{"issue":"6","key":"2358_CR52","doi-asserted-by":"publisher","DOI":"10.1016\/j.isci.2021.102541","volume":"24","author":"C Yao","year":"2021","unstructured":"Yao, C., & Ma, Y. (2021). Superconducting materials: Challenges and opportunities for large-scale applications. Iscience, 24(6), 102541.","journal-title":"Iscience"},{"issue":"3","key":"2358_CR53","first-page":"331","volume":"52","author":"K Yu","year":"2003","unstructured":"Yu, K., Lu, Z., & Stander, J. (2003). Quantile regression: applications and current research areas. Journal of the Royal Statistical Society: Series D (The Statistician), 52(3), 331\u2013350.","journal-title":"Journal of the Royal Statistical Society: Series D (The Statistician)"},{"issue":"2","key":"2358_CR54","doi-asserted-by":"publisher","first-page":"288","DOI":"10.1002\/cjs.11244","volume":"43","author":"YR Yue","year":"2015","unstructured":"Yue, Y. R., & Loh, J. M. (2015). Variable selection for inhomogeneous spatial point process models. Canadian Journal of Statistics, 43(2), 288\u2013305. https:\/\/doi.org\/10.1002\/cjs.11244","journal-title":"Canadian Journal of Statistics"},{"issue":"6","key":"2358_CR55","doi-asserted-by":"publisher","first-page":"1149","DOI":"10.1109\/tnsre.2019.2913400","volume":"27","author":"P Zhang","year":"2019","unstructured":"Zhang, P., Wang, X., Chen, J., You, W., & Zhang, W. (2019). Spectral and temporal feature learning with two-stream neural networks for mental workload assessment. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 27(6), 1149\u20131159. https:\/\/doi.org\/10.1109\/tnsre.2019.2913400","journal-title":"IEEE Transactions on Neural Systems and Rehabilitation Engineering"},{"issue":"1\u20132","key":"2358_CR56","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1007\/s10909-020-02545-9","volume":"202","author":"Y Zhang","year":"2021","unstructured":"Zhang, Y., & Xu, X. (2021). Fe-based superconducting transition temperature modeling through Gaussian process regression. Journal of Low Temperature Physics, 202(1\u20132), 205\u2013218. https:\/\/doi.org\/10.1007\/s10909-020-02545-9","journal-title":"Journal of Low Temperature Physics"},{"issue":"1","key":"2358_CR57","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1515\/ijmr-2020-7986","volume":"112","author":"Y Zhang","year":"2021","unstructured":"Zhang, Y., & Xu, X. (2021). Predicting doped Fe-based superconductor critical temperature from structural and topological parameters using machine learning. International Journal of Materials Research, 112(1), 2\u20139. https:\/\/doi.org\/10.1515\/ijmr-2020-7986","journal-title":"International Journal of Materials Research"},{"issue":"2","key":"2358_CR58","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1111\/j.1467-9868.2012.01044.x","volume":"75","author":"Z Zhou","year":"2013","unstructured":"Zhou, Z., & Shao, X. (2013). Inference for linear models with dependent errors. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 75(2), 323\u2013343. https:\/\/doi.org\/10.1111\/j.1467-9868.2012.01044.x","journal-title":"Journal of the Royal Statistical Society: Series B (Statistical Methodology)"}],"container-title":["Journal of Intelligent Manufacturing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-024-02358-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10845-024-02358-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-024-02358-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,19]],"date-time":"2025-05-19T20:50:46Z","timestamp":1747687846000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10845-024-02358-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,4]]},"references-count":58,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2025,6]]}},"alternative-id":["2358"],"URL":"https:\/\/doi.org\/10.1007\/s10845-024-02358-7","relation":{},"ISSN":["0956-5515","1572-8145"],"issn-type":[{"value":"0956-5515","type":"print"},{"value":"1572-8145","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,4]]},"assertion":[{"value":"12 May 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 February 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 May 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"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":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}