{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T19:58:24Z","timestamp":1780084704883,"version":"3.54.0"},"reference-count":69,"publisher":"Institute of Electronics, Information and Communications Engineers (IEICE)","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Fundamentals"],"published-print":{"date-parts":[[2023,2,1]]},"DOI":"10.1587\/transfun.2022eap1031","type":"journal-article","created":{"date-parts":[[2022,7,25]],"date-time":"2022-07-25T22:09:50Z","timestamp":1658786990000},"page":"106-123","source":"Crossref","is-referenced-by-count":3,"title":["Modal Interval Regression Based on Spline Quantile Regression"],"prefix":"10.1587","volume":"E106.A","author":[{"given":"Sai","family":"YAO","sequence":"first","affiliation":[{"name":"Dept. of Information Science and Engineering, Ritsumeikan University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daichi","family":"KITAHARA","sequence":"additional","affiliation":[{"name":"Division of Electrical, Electronic and Infocommunications Engineering, Osaka University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hiroki","family":"KURODA","sequence":"additional","affiliation":[{"name":"Dept. of Information Science and Engineering, Ritsumeikan University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Akira","family":"HIRABAYASHI","sequence":"additional","affiliation":[{"name":"Dept. of Information Science and Engineering, Ritsumeikan University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"532","reference":[{"key":"1","doi-asserted-by":"crossref","unstructured":"[1] T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd ed., Springer, New York, NY, 2009.","DOI":"10.1007\/978-0-387-84858-7"},{"key":"2","doi-asserted-by":"crossref","unstructured":"[2] G.A.F. Seber and A.J. Lee, Linear Regression Analysis, 2nd ed., Wiley, New York, NY, 2003. 10.1002\/9780471722199","DOI":"10.1002\/9780471722199"},{"key":"3","doi-asserted-by":"crossref","unstructured":"[3] J. Shao, Mathematical Statistics, 2nd ed., Springer, New York, NY, 2003. 10.1007\/b97553","DOI":"10.1007\/b97553"},{"key":"4","unstructured":"[4] B. Abraham and J. Ledolter, Introduction to Regression Modeling, Duxbury, Belmont, CA, 2005."},{"key":"5","unstructured":"[5] J.A. Rice, Mathematical Statistics and Data Analysis, 3rd ed., Dux-bury, Belmont, CA, 2006."},{"key":"6","unstructured":"[6] R.J. Freund, W.J. Wilson, and P. Sa, Regression Analysis, 2nd ed., Academic Press, London, UK, 2006."},{"key":"7","unstructured":"[7] D.C. Montgomery, E.A. Peck, and G.G. Vining, Introduction to Linear Regression Analysis, 5th ed., Wiley, New York, NY, 2013."},{"key":"8","doi-asserted-by":"publisher","unstructured":"[8] G. Bassett, Jr. and R. Koenker, \u201cAsymptotic theory of least absolute error regression,\u201d J. Am. Stat. Assoc., vol.73, no.363, pp.618-622, Sept. 1978. 10.1080\/01621459.1978.10480065","DOI":"10.1080\/01621459.1978.10480065"},{"key":"9","doi-asserted-by":"crossref","unstructured":"[9] P. Bloomfield and W.L. Steiger, Least Absolute Deviations: Theory, Applications, and Algorithms, Birkh\u00e4user, Boston, MA, 1983. 10.1007\/978-1-4684-8574-5","DOI":"10.1007\/978-1-4684-8574-5_7"},{"key":"10","doi-asserted-by":"crossref","unstructured":"[10] P.J. Huber and E.M. Ronchetti, Robust Statistics, 2nd ed., Wiley, New York, NY, 2009. 10.1002\/9780470434697","DOI":"10.1002\/9780470434697"},{"key":"11","doi-asserted-by":"crossref","unstructured":"[11] R.R. Wilcox, Introduction to Robust Estimation and Hypothesis Testing, 4th ed., Academic Press, London, UK, 2016.","DOI":"10.1016\/B978-0-12-804733-0.00001-9"},{"key":"12","doi-asserted-by":"crossref","unstructured":"[12] R.A. Maronna, R.D. Martin, V.J. Yohai, and M. Salibi\u00e1n-Barrera, Robust Statistics: Theory and Methods (with R), 2nd ed., Wiley, New York, NY, 2019. 10.1002\/9781119214656","DOI":"10.1002\/9781119214656"},{"key":"13","doi-asserted-by":"crossref","unstructured":"[13] R. Koenker and G. Bassett Jr., \u201cRegression quantiles,\u201d Econometrica, vol.46, no.1, pp.33-50, Jan. 1978. 10.2307\/1913643","DOI":"10.2307\/1913643"},{"key":"14","doi-asserted-by":"publisher","unstructured":"[14] R. Koenker and K.F. Hallock, \u201cQuantile regression,\u201d J. Econ. Perspect., vol.15, no.4, pp.143-156, 2001. 10.1257\/jep.15.4.143","DOI":"10.1257\/jep.15.4.143"},{"key":"15","doi-asserted-by":"crossref","unstructured":"[15] R. Koenker, Quantile Regression, Cambridge University Press, New York, NY, 2005. 10.1017\/cbo9780511754098","DOI":"10.1017\/CBO9780511754098"},{"key":"16","doi-asserted-by":"crossref","unstructured":"[16] R. Koenker, V. Chernozhukov, X. He, and L. Peng, Handbook of Quantile Regression, Chapman &amp; Hall, New York, NY, 2017. 10.1201\/9781315120256","DOI":"10.1201\/9781315120256"},{"key":"17","doi-asserted-by":"publisher","unstructured":"[17] M.J. Lee, \u201cMode regression,\u201d J. Econom., vol.42, no.3, pp.337-349, Nov. 1989. 10.1016\/0304-4076(89)90057-2","DOI":"10.1016\/0304-4076(89)90057-2"},{"key":"18","doi-asserted-by":"publisher","unstructured":"[18] W. Yao, B.G. Lindsay, and R. Li, \u201cLocal modal regression,\u201d J. Nonparametr. Stat., vol.24, no.3, pp.647-663, Sept. 2012. 10.1080\/10485252.2012.678848","DOI":"10.1080\/10485252.2012.678848"},{"key":"19","unstructured":"[19] Y. Feng, J. Fan, and J.A.K. Suykens, \u201cA statistical learning approach to modal regression,\u201d J. Mach. Learn. Res., vol.21, no.2, pp.1-35, 2020."},{"key":"20","unstructured":"[20] H. Sasaki, T. Sakai, and T. Kanamori, \u201cRobust modal regression with direct gradient approximation of modal regression risk,\u201d Proc. Conf. Uncert. Artif. Intell. (UAI), Online, pp.380-389, Aug. 2020."},{"key":"21","doi-asserted-by":"publisher","unstructured":"[21] H. Chen, Y. Wang, F. Zheng, C. Deng, and H. Huang, \u201cSparse modal additive model,\u201d IEEE Trans. Neural Netw. Learn. Syst., vol.32, no.6, pp.2373-2387, June 2021. 10.1109\/tnnls.2020.3005144","DOI":"10.1109\/TNNLS.2020.3005144"},{"key":"22","unstructured":"[22] A. Eldeeb, S. Desoky, and M. Ahmed, \u201cA new robust algorithm for penalized regression splines based on mode-estimation,\u201d Int. J. Nonlinear Anal. Appl., vol.12, no.1, pp.1037-1055, 2021."},{"key":"23","unstructured":"[23] C. de Boor, \u201cBest approximation properties of spline functions of odd degree,\u201d J. Math. Mech., vol.12, no.5, pp.747-749, 1963."},{"key":"24","doi-asserted-by":"publisher","unstructured":"[24] S. Wold, \u201cSpline functions in data analysis,\u201d Technomet., vol.16, no.1, pp.87-111, Feb. 1974. 10.1080\/00401706.1974.10489142","DOI":"10.1080\/00401706.1974.10489142"},{"key":"25","doi-asserted-by":"publisher","unstructured":"[25] B.W. Silverman, \u201cSome aspects of the spline smoothing approach to non-parametric regression curve fitting,\u201d J. Royal Stat. Soc. B, vol.47, no.1, pp.1-52, 1985. 10.1111\/j.2517-6161.1985.tb01327.x","DOI":"10.1111\/j.2517-6161.1985.tb01327.x"},{"key":"26","doi-asserted-by":"crossref","unstructured":"[27] M. Unser, \u201cSplines: A perfect fit for signal and image processing,\u201d IEEE Signal Process. Mag., vol.16, no.6, pp.22-38, Nov. 1999. 10.1109\/79.799930","DOI":"10.1109\/79.799930"},{"key":"27","doi-asserted-by":"crossref","unstructured":"[28] J.O. Ramsay and B.W. Silverman, Functional Data Analysis, 2nd ed, Springer, New York, NY, 2005. 10.1007\/b98888","DOI":"10.1007\/b98888"},{"key":"28","unstructured":"[30] J. Qui\u00f1onero-Candela and C.E. Rasmussen, \u201cA unifying view of sparse approximate Gaussian process regression,\u201d J. Mach. Learn. Res., vol.6, no.65, pp.1939-1959, Dec. 2005."},{"key":"29","doi-asserted-by":"crossref","unstructured":"[31] J.Q. Shi and T. Choi, Gaussian Process Regression Analysis for Functional Data, Chapman &amp; Hall, New York, NY, 2011. 10.1201\/b11038","DOI":"10.1201\/b11038"},{"key":"30","doi-asserted-by":"crossref","unstructured":"[32] R.B. Gramacy, Surrogates: Gaussian Process Modeling, Design and Optimization for the Applied Sciences, Chapman &amp; Hall, New York, NY, 2021. 10.1201\/9780367815493","DOI":"10.1201\/9780367815493"},{"key":"31","doi-asserted-by":"publisher","unstructured":"[33] R.J. Hyndman, \u201cComputing and graphing highest density regions,\u201d Am. Stat., vol.50, no.2, pp.120-126, May 1996. 10.1080\/00031305.1996.10474359","DOI":"10.1080\/00031305.1996.10474359"},{"key":"32","doi-asserted-by":"crossref","unstructured":"[34] J. Fan and Q. Yao, Nonlinear Time Series: Nonparametric and Parametric Methods Annals of the Institute of Statistical Mathematics, Springer, NY, New York, 2003.","DOI":"10.1007\/b97702"},{"key":"33","doi-asserted-by":"publisher","unstructured":"[35] J.H.J. Einmahl, M. Gantner, and G. Sawitzki, \u201cThe shorth plot,\u201d J. Comput. Graph. Stat., vol.19, no.1, pp.62-73, 2010. 10.1198\/jcgs.2009.08020","DOI":"10.1198\/jcgs.2009.08020"},{"key":"34","doi-asserted-by":"publisher","unstructured":"[36] J.G. de Gooijer and A. Gannoun, \u201cNonparametric conditional predictive regions for time series,\u201d Comput. Stat. Data Anal., vol.33, no.3, pp.259-275, May 2000. 10.1016\/s0167-9473(99)00056-0","DOI":"10.1016\/S0167-9473(99)00056-0"},{"key":"35","doi-asserted-by":"publisher","unstructured":"[37] W. Polonik and Q. Yao, \u201cConditional minimum volume predictive regions for stochastic processes,\u201d J. Am. Stat. Assoc., vol.95, no.450, pp.509-519, June 2000. 10.1080\/01621459.2000.10474228","DOI":"10.1080\/01621459.2000.10474228"},{"key":"36","doi-asserted-by":"publisher","unstructured":"[38] J. Demongeot, A. Laksaci, M. Rachdi, and S. Rahmani, \u201cOn the local linear modelization of the conditional distribution for functional data,\u201d Sankhy\u0101: Indian J. Stat., vol.76, no.2, pp.328-355, March 2014. 10.1007\/s13171-013-0050-z","DOI":"10.1007\/s13171-013-0050-z"},{"key":"37","doi-asserted-by":"publisher","unstructured":"[39] M. Rachdi, A. Laksaci, I.M. Almanjahie, and Z. Chikr-Elmezouar, \u201cFDA: Theoretical and practical efficiency of the local linear estimation based on the <i>k<\/i>NN smoothing of the conditional distribution when there are missing data,\u201d J. Stat. Comput. Sim., vol.90, no.8, pp.1479-1495, 2020. 10.1080\/00949655.2020.1732378","DOI":"10.1080\/00949655.2020.1732378"},{"key":"38","doi-asserted-by":"crossref","unstructured":"[40] L. Zhu, J. Lu, and Y. Chen, \u201cHDI-forest: Highest density interval regression forest,\u201d Proc. Int. Joint Conf. Artif. Intell. (IJCAI), Macao, China, pp.4468-4474, Aug. 2019. 10.24963\/ijcai.2019\/621","DOI":"10.24963\/ijcai.2019\/621"},{"key":"39","doi-asserted-by":"publisher","unstructured":"[41] M.H. Roy and D. Larocque, \u201cPrediction intervals with random forests,\u201d Stat. Methods Med. Res., vol.29, no.1, pp.205-229, 2020. 10.1177\/0962280219829885","DOI":"10.1177\/0962280219829885"},{"key":"40","doi-asserted-by":"crossref","unstructured":"[42] D. Kitahara, K. Leng, Y. Tezuka, and A. Hirabayashi, \u201cSimultaneous spline quantile regression under shape constraints,\u201d Proc. Eur. Signal Process. Conf. (EUSIPCO), Amsterdam, The Netherlands, pp.2423-2427, Oct. 2020. 10.23919\/eusipco47968.2020.9287462","DOI":"10.23919\/Eusipco47968.2020.9287462"},{"key":"41","doi-asserted-by":"publisher","unstructured":"[43] R.J. Vanderbei and T.J. Carpenter, \u201cSymmetric indefinite systems for interior point methods,\u201d Math. Program., vol.58, no.1-3, pp.1-32, Jan. 1993. 10.1007\/bf01581257","DOI":"10.1007\/BF01581257"},{"key":"42","doi-asserted-by":"publisher","unstructured":"[44] A. Altman and J. Gondzio, \u201cRegularized symmetric indefinite systems in interior point methods for linear and quadratic optimization,\u201d Optim. Methods Softw., vol.11, no.1-4, pp.275-302, 1999. 10.1080\/10556789908805754","DOI":"10.1080\/10556789908805754"},{"key":"43","doi-asserted-by":"publisher","unstructured":"[45] D. Gabay and B. Mercier, \u201cA dual algorithm for the solution of nonlinear variational problems via finite elements approximations,\u201d Comput. Math. Appl., vol.2, no.1, pp.17-40, 1976. 10.1016\/0898-1221(76)90003-1","DOI":"10.1016\/0898-1221(76)90003-1"},{"key":"44","unstructured":"[46] S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein, \u201cDistributed optimization and statistical learning via the alternating direction method of multipliers,\u201d Found. Trends Mach. Learn., vol.3, no.1, pp.1-122, Jan. 2011. 10.1561\/2200000016"},{"key":"45","doi-asserted-by":"crossref","unstructured":"[47] R. Glowinski, \u201cOn alternating direction methods of multipliers: A historical perspective,\u201d Modeling, Simulation and Optimization for Science and Technology, W. Fitzgibbon, Y.A. Kuznetsov, P. Neittaanm\u00e4ki, and O. Pironneau, eds., pp.59-82, Springer Netherlands, Dordrecht, The Netherlands, 2014. 10.1007\/978-94-017-9054-3_4","DOI":"10.1007\/978-94-017-9054-3_4"},{"key":"46","unstructured":"[48] J. Eckstein and W. Yao, \u201cUnderstanding the convergence of the alternating direction method of multipliers: Theoretical and computational perspectives,\u201d Pacific J. Optim., vol.11, no.4, pp.619-644, Oct. 2015."},{"key":"47","unstructured":"[49] L. Condat, D. Kitahara, A. Contreras, and A. Hirabayashi, \u201cProximal splitting algorithms for convex optimization: A tour of recent advances, with new twists,\u201d SIAM Review, to appear."},{"key":"48","doi-asserted-by":"crossref","unstructured":"[50] G.R. Shorack, Probability for Statistics, 2nd ed., Springer, New York, NY, 2017. 10.1007\/978-3-319-52207-4","DOI":"10.1007\/978-3-319-52207-4"},{"key":"49","doi-asserted-by":"publisher","unstructured":"[51] R.J. Casady and J.D. Cryer, \u201cMonotone percentile regression,\u201d Ann. Stat., vol.4, no.3, pp.532-541, May 1976. 10.1214\/aos\/1176343459","DOI":"10.1214\/aos\/1176343459"},{"key":"50","doi-asserted-by":"publisher","unstructured":"[52] D. Griffiths and M. Willcox, \u201cPercentile regression: A parametric approach,\u201d J. Am. Stat. Assoc., vol.73, no.363, pp.496-498, Sept. 1978. 10.1080\/01621459.1978.10480042","DOI":"10.1080\/01621459.1978.10480042"},{"key":"51","doi-asserted-by":"publisher","unstructured":"[53] T.J. Cole, \u201cFitting smoothed centile curves to reference data,\u201d J. Royal Stat. Soc. A, vol.151, no.3, pp.385-418, 1988. 10.2307\/2982992","DOI":"10.2307\/2982992"},{"key":"52","doi-asserted-by":"publisher","unstructured":"[54] Y.C. Chen, C.R. Genovese, R.J. Tibshirani, and L. Wasserman, \u201cNonparametric modal regression,\u201d Ann. Stat., vol.44, no.2, pp.489-514, April 2016. 10.1214\/15-aos1373","DOI":"10.1214\/15-AOS1373"},{"key":"53","doi-asserted-by":"publisher","unstructured":"[55] Y.C. Chen, \u201cModal regression using kernel density estimation: A review,\u201d WIREs Comput. Stat., vol.10, no.4, 14 pages, July\/Aug. 2018. 10.1002\/wics.1431","DOI":"10.1002\/wics.1431"},{"key":"54","doi-asserted-by":"publisher","unstructured":"[56] J.E. Chac\u00f3n, \u201cThe modal age of statistics,\u201d Int. Stat. Review, vol.88, no.1, pp.122-141, April 2020. 10.1111\/insr.12340","DOI":"10.1111\/insr.12340"},{"key":"55","doi-asserted-by":"publisher","unstructured":"[57] P. \u010c\u00ed\u017eek and S. Sad\u0131ko\u011flu, \u201cRobust nonparametric regression: A review,\u201d WIREs Comput. Stat., vol.12, no.3, 16 pages, May\/June 2020. 10.1002\/wics.1492","DOI":"10.1002\/wics.1492"},{"key":"56","doi-asserted-by":"publisher","unstructured":"[58] H. Ota, K. Kato, and S. Hara, \u201cQuantile regression approach to conditional mode estimation,\u201d Electron. J. Statist., vol.13, no.2, pp.3120-3160, Sept. 2019. 10.1214\/19-ejs1607","DOI":"10.1214\/19-EJS1607"},{"key":"57","doi-asserted-by":"publisher","unstructured":"[59] W. Yao and L. Li, \u201cA new regression model: Modal linear regression,\u201d Scand. J. Stat. Theory Appl., vol.41, no.3, pp.656-671, Sept. 2014. 10.1111\/sjos.12054","DOI":"10.1111\/sjos.12054"},{"key":"58","doi-asserted-by":"crossref","unstructured":"[60] A. Pensia, V. Jog, and P.L. Loh, \u201cEstimating location parameters in entangled single-sample distributions,\u201d preprint arXiv:1907.03087, 70 pages, 2019. 10.48550\/arXiv.1907.03087","DOI":"10.1109\/ISIT.2019.8849279"},{"key":"59","doi-asserted-by":"publisher","unstructured":"[61] K. Dearborn and R. Frongillo, \u201cOn the indirect elicitability of the mode and modal interval,\u201d Ann. Inst. Stat. Math., vol.72, pp.1095-1108, 2020. 10.1007\/s10463-019-00719-1","DOI":"10.1007\/s10463-019-00719-1"},{"key":"60","unstructured":"[62] H. Ota and S. Hara, \u201cOn estimation of conditional modes using multiple quantile regressions,\u201d preprint arXiv:1712.08754, 26 pages, 2017. 10.48550\/arXiv.1712.08754"},{"key":"61","doi-asserted-by":"publisher","unstructured":"[63] W. He\u00df and J.W. Schmidt, \u201cPositive quartic, monotone quintic <i>C<\/i><sup>2<\/sup>-spline interpolation in one and two dimensions,\u201d J. Comput. Appl. Math., vol.55, no.1, pp.51-67, Oct. 1994. 10.1016\/0377-0427(94)90184-8","DOI":"10.1016\/0377-0427(94)90184-8"},{"key":"62","doi-asserted-by":"crossref","unstructured":"[64] D. Kitahara and I. Yamada, \u201cProbability density function estimation by positive quartic <i>C<\/i><sup>2<\/sup>-spline functions,\u201d Proc. IEEE Int. Conf. Acoust. Speech Signal Process. (ICASSP), Brisbane, Australia, pp.3556-3560, April 2015. 10.1109\/icassp.2015.7178633","DOI":"10.1109\/ICASSP.2015.7178633"},{"key":"63","doi-asserted-by":"crossref","unstructured":"[65] D. Kitahara and I. Yamada, \u201cTwo-dimensional positive spline smoothing and its application to probability density estimation,\u201d Proc. IEEE Int. Conf. Acoust. Speech Signal Process. (ICASSP), Shanghai, China, pp.4219-4223, March 2016. 10.1109\/icassp.2016.7472472","DOI":"10.1109\/ICASSP.2016.7472472"},{"key":"64","unstructured":"[66] MATLAB, Quadratic Programming Algorithms, https:\/\/www.mathworks.com\/help\/optim\/ug\/quadratic-programming-algorithms.html, accessed Feb. 15, 2022."},{"key":"65","doi-asserted-by":"publisher","unstructured":"[67] R.J. Hyndman, D.M. Bashtannyk, and G.K. Grunwald, \u201cEstimating and visualizing conditional densities,\u201d J. Comput. Graph. Stat., vol.5, no.4, pp.315-336, Dec. 1996. 10.1080\/10618600.1996.10474715","DOI":"10.1080\/10618600.1996.10474715"},{"key":"66","unstructured":"[68] MATLAB, Kernel Smoothing Function Estimate for Univariate and Bivariate Data: ksdensity, https:\/\/www.mathworks.com\/help\/stats\/ksdensity.html, accessed Feb. 15, 2022."},{"key":"67","unstructured":"[69] MATLAB, Smoothing Splines, https:\/\/www.mathworks.com\/help\/curvefit\/smoothing-splines.html, accessed Feb. 15, 2022."},{"key":"68","unstructured":"[70] China Meteorological Data Service Centre, http:\/\/data.cma.cn\/en, accessed Feb. 15, 2022."},{"key":"69","doi-asserted-by":"crossref","unstructured":"[71] D. Bolton, \u201cThe computation of equivalent potential temperature,\u201d Mon. Weather Rev., vol.108, no.7, pp.1046-1053, July 1980. 10.1175\/1520-0493(1980)108%3C1046:tcoept%3E2.0.co;2","DOI":"10.1175\/1520-0493(1980)108<1046:TCOEPT>2.0.CO;2"}],"container-title":["IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transfun\/E106.A\/2\/E106.A_2022EAP1031\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,29]],"date-time":"2024-09-29T21:29:56Z","timestamp":1727645396000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transfun\/E106.A\/2\/E106.A_2022EAP1031\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,1]]},"references-count":69,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2023]]}},"URL":"https:\/\/doi.org\/10.1587\/transfun.2022eap1031","relation":{},"ISSN":["0916-8508","1745-1337"],"issn-type":[{"value":"0916-8508","type":"print"},{"value":"1745-1337","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,1]]},"article-number":"2022EAP1031"}}