{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T07:47:36Z","timestamp":1782892056425,"version":"3.54.5"},"reference-count":47,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2025,1,27]],"date-time":"2025-01-27T00:00:00Z","timestamp":1737936000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,27]],"date-time":"2025-01-27T00:00:00Z","timestamp":1737936000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Computing"],"published-print":{"date-parts":[[2025,2]]},"DOI":"10.1007\/s00607-024-01397-5","type":"journal-article","created":{"date-parts":[[2025,1,27]],"date-time":"2025-01-27T05:00:19Z","timestamp":1737954019000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["A novel cheetah optimizer hybrid approach based on opposition-based learning (OBL) and diversity metrics"],"prefix":"10.1007","volume":"107","author":[{"given":"Erik","family":"Cuevas","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Oscar","family":"Barba","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"H\u00e9ctor","family":"Escobar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,1,27]]},"reference":[{"key":"1397_CR1","doi-asserted-by":"publisher","DOI":"10.1002\/9780470640425","volume-title":"Engineering optimization","author":"X-S Yang","year":"2010","unstructured":"Yang X-S (2010) Engineering optimization. Wiley. https:\/\/doi.org\/10.1002\/9780470640425"},{"issue":"1\u20132","key":"1397_CR2","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1016\/s0377-0427(00)00425-8","volume":"124","author":"PM Pardalos","year":"2000","unstructured":"Pardalos PM, Romeijn HE, Tuy H (2000) Recent developments and trends in global optimization. J Comput Appl Math 124(1\u20132):209\u2013228. https:\/\/doi.org\/10.1016\/s0377-0427(00)00425-8","journal-title":"J Comput Appl Math"},{"key":"1397_CR3","doi-asserted-by":"publisher","unstructured":"Cavazzuti M (2012) Deterministic optimization. In: Optimization methods. Springer, Berlin Heidelberg, pp 77\u2013102. https:\/\/doi.org\/10.1007\/978-3-642-31187-1_4","DOI":"10.1007\/978-3-642-31187-1_4"},{"issue":"4","key":"1397_CR4","doi-asserted-by":"publisher","first-page":"2431","DOI":"10.1007\/s11831-022-09872-y","volume":"30","author":"MS Daoud","year":"2022","unstructured":"Daoud MS, Shehab M, Al-Mimi HM, Abualigah L, Zitar RA, Shambour MKY (2022) Gradient-based optimizer (GBO): a review, theory, variants, and applications. Arch Comput Methods Eng 30(4):2431\u20132449. https:\/\/doi.org\/10.1007\/s11831-022-09872-y","journal-title":"Arch Comput Methods Eng"},{"issue":"3","key":"1397_CR5","doi-asserted-by":"publisher","first-page":"315","DOI":"10.1111\/j.1751-5823.2002.tb00174.x","volume":"70","author":"D Fouskakis","year":"2002","unstructured":"Fouskakis D, Draper D (2002) Stochastic optimization: a review. Int Stat Rev 70(3):315\u2013349. https:\/\/doi.org\/10.1111\/j.1751-5823.2002.tb00174.x","journal-title":"Int Stat Rev"},{"key":"1397_CR6","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1201\/9781003006312","volume-title":"Metaheuristic computation with MATLAB\u00ae","author":"E Cuevas","year":"2020","unstructured":"Cuevas E, Rodr\u00edguez A (2020) Metaheuristic computation with MATLAB\u00ae. Chapman and Hall\/CRC, pp 3\u20137. https:\/\/doi.org\/10.1201\/9781003006312"},{"key":"1397_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/B978-0-12-813314-9.00010-4","author":"M Abdel-Basset","year":"2018","unstructured":"Abdel-Basset M, Abdel-Fatah L, Sangaiah AK (2018) Metaheuristic algorithms: a comprehensive review. Comput Intell Multimed Data Cloud Eng Appl. https:\/\/doi.org\/10.1016\/B978-0-12-813314-9.00010-4","journal-title":"Comput Intell Multimed Data Cloud Eng Appl"},{"issue":"3","key":"1397_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2480741.2480752","volume":"45","author":"M \u010crepin\u0161ek","year":"2013","unstructured":"\u010crepin\u0161ek M, Liu S-H, Mernik M (2013) Exploration and exploitation in evolutionary algorithms. ACM Comput Surv 45(3):1\u201333. https:\/\/doi.org\/10.1145\/2480741.2480752","journal-title":"ACM Comput Surv"},{"issue":"2","key":"1397_CR9","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1007\/s00607-021-00955-5","volume":"104","author":"SS Vinod Chandra","year":"2021","unstructured":"Vinod Chandra SS, Anand HS (2021) Nature inspired meta heuristic algorithms for optimization problems. Computing 104(2):251\u2013269. https:\/\/doi.org\/10.1007\/s00607-021-00955-5","journal-title":"Computing"},{"key":"1397_CR10","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-33-6773-9","volume-title":"Nature-inspired metaheuristic algorithms for engineering optimization applications","author":"S Carbas","year":"2021","unstructured":"Carbas S, Toktas A, Ustun D (2021) Nature-inspired metaheuristic algorithms for engineering optimization applications. Springer Singapore. https:\/\/doi.org\/10.1007\/978-981-33-6773-9"},{"key":"1397_CR11","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1007\/s00607-021-00955-5","volume-title":"Nature inspired metaheuristic algorithms","author":"X-S Yang","year":"2010","unstructured":"Yang X-S (2010) Nature inspired metaheuristic algorithms, 2nd edn. Lunvier Press, pp 63\u201369. https:\/\/doi.org\/10.1007\/s00607-021-00955-5","edition":"2"},{"key":"1397_CR12","doi-asserted-by":"publisher","unstructured":"Holland JH (1984) Genetic algorithms and adaptation. In: Adaptive control of Ill-defined systems. Springer US, pp 317\u2013333. https:\/\/doi.org\/10.1007\/978-1-4684-8941-5_21","DOI":"10.1007\/978-1-4684-8941-5_21"},{"issue":"4","key":"1397_CR13","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1023\/A:1008202821328","volume":"11","author":"R Storn","year":"1997","unstructured":"Storn R, Price K (1997) Differential evolution\u2014a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341\u2013359. https:\/\/doi.org\/10.1023\/A:1008202821328","journal-title":"J Glob Optim"},{"issue":"1","key":"1397_CR14","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1023\/A:1015059928466","volume":"1","author":"H-G Beyer","year":"2002","unstructured":"Beyer H-G, Schwefel H-P (2002) Evolution strategies\u2014a comprehensive introduction. Nat Comput 1(1):3\u201352. https:\/\/doi.org\/10.1023\/A:1015059928466","journal-title":"Nat Comput"},{"key":"1397_CR15","doi-asserted-by":"publisher","unstructured":"Kennedy J, Eberhart R (2002) Particle swarm optimization. In: Proceedings of ICNN\u201995\u2014international conference on neural networks. IEEE, vol 4, pp 1942\u20131948. https:\/\/doi.org\/10.1109\/ICNN.1995.488968","DOI":"10.1109\/ICNN.1995.488968"},{"key":"1397_CR16","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1016\/j.advengsoft.2016.01.008","volume":"95","author":"S Mirjalili","year":"2016","unstructured":"Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51\u201367. https:\/\/doi.org\/10.1016\/j.advengsoft.2016.01.008","journal-title":"Adv Eng Softw"},{"key":"1397_CR17","unstructured":"Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. In: Technical report-TR06. Erciyes University, Engineering Faculty"},{"key":"1397_CR18","doi-asserted-by":"publisher","unstructured":"Yang X-S (2010) A new metaheuristic bat-inspired algorithm. In: Studies in computational intelligence. Springer, pp 65\u201374. https:\/\/doi.org\/10.1007\/978-3-642-12538-6_6","DOI":"10.1007\/978-3-642-12538-6_6"},{"key":"1397_CR19","doi-asserted-by":"publisher","unstructured":"Yang X-S, Deb S (2009) Cuckoo search via L\u00e9vy flights. In: 2009 world congress on nature & biologically inspired computing (NaBIC). IEEE, pp 210\u2013214. https:\/\/doi.org\/10.1109\/NABIC.2009.5393690","DOI":"10.1109\/NABIC.2009.5393690"},{"key":"1397_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.compstruc.2016.03.001","volume":"169","author":"A Askarzadeh","year":"2016","unstructured":"Askarzadeh A (2016) A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Comput Struct 169:1\u201312. https:\/\/doi.org\/10.1016\/j.compstruc.2016.03.001","journal-title":"Comput Struct"},{"key":"1397_CR21","doi-asserted-by":"publisher","first-page":"114570","DOI":"10.1016\/j.cma.2022.114570","volume":"391","author":"JO Agushaka","year":"2022","unstructured":"Agushaka JO, Ezugwu AE, Abualigah L (2022) Dwarf mongoose optimization algorithm. Comput Methods Appl Mech Eng 391:114570. https:\/\/doi.org\/10.1016\/j.cma.2022.114570","journal-title":"Comput Methods Appl Mech Eng"},{"issue":"4598","key":"1397_CR22","doi-asserted-by":"publisher","first-page":"671","DOI":"10.1126\/science.220.4598.671","volume":"220","author":"S Kirkpatrick","year":"1983","unstructured":"Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Science (1979) 220(4598):671\u2013680. https:\/\/doi.org\/10.1126\/science.220.4598.671","journal-title":"Science (1979)"},{"issue":"13","key":"1397_CR23","doi-asserted-by":"publisher","first-page":"2232","DOI":"10.1016\/j.ins.2009.03.004","volume":"179","author":"E Rashedi","year":"2009","unstructured":"Rashedi E, Nezamabadi-pour H, Saryazdi S (2009) GSA: A gravitational search algorithm. Inf Sci 179(13):2232\u20132248. https:\/\/doi.org\/10.1016\/j.ins.2009.03.004","journal-title":"Inf Sci"},{"issue":"2","key":"1397_CR24","doi-asserted-by":"publisher","first-page":"256","DOI":"10.1007\/s10489-013-0458-0","volume":"40","author":"E Cuevas","year":"2014","unstructured":"Cuevas E, Echavarr\u00eda A, Ram\u00edrez-Orteg\u00f3n MA (2014) An optimization algorithm inspired by the states of matter that improves the balance between exploration and exploitation. Appl Intell 40(2):256\u2013272. https:\/\/doi.org\/10.1007\/s10489-013-0458-0","journal-title":"Appl Intell"},{"issue":"2","key":"1397_CR25","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1177\/003754970107600201","volume":"76","author":"ZW Geem","year":"2001","unstructured":"Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. SIMULATION 76(2):60\u201368. https:\/\/doi.org\/10.1177\/003754970107600201","journal-title":"SIMULATION"},{"key":"1397_CR26","doi-asserted-by":"publisher","first-page":"110454","DOI":"10.1016\/j.knosys.2023.110454","volume":"268","author":"M Abdel-Basset","year":"2023","unstructured":"Abdel-Basset M, Mohamed R, Azeem SAA, Jameel M, Abouhawwash M (2023) Kepler optimization algorithm: a new metaheuristic algorithm inspired by Kepler\u2019s laws of planetary motion. Knowl-Based Syst 268:110454. https:\/\/doi.org\/10.1016\/j.knosys.2023.110454","journal-title":"Knowl-Based Syst"},{"key":"1397_CR27","doi-asserted-by":"publisher","first-page":"122413","DOI":"10.1016\/j.eswa.2023.122413","volume":"239","author":"M Han","year":"2024","unstructured":"Han M, Du Z, Yuen KF, Zhu H, Li Y, Yuan Q (2024) Walrus optimizer: a novel nature-inspired metaheuristic algorithm. Expert Syst Appl 239:122413. https:\/\/doi.org\/10.1016\/j.eswa.2023.122413","journal-title":"Expert Syst Appl"},{"key":"1397_CR28","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-023-10680-4","author":"MA Al-Betar","year":"2024","unstructured":"Al-Betar MA, Awadallah MA, Braik MS, Makhadmeh S, Doush IA (2024) Elk herd optimizer: a novel nature-inspired metaheuristic algorithm. Artif Intell Rev. https:\/\/doi.org\/10.1007\/s10462-023-10680-4","journal-title":"Artif Intell Rev"},{"issue":"2","key":"1397_CR29","doi-asserted-by":"publisher","first-page":"65","DOI":"10.3390\/biomimetics9020065","volume":"9","author":"O Al-Baik","year":"2024","unstructured":"Al-Baik O, Alomari S, Alssayed O, Gochhait S, Leonova I, Dutta U, Dehghani M (2024) Pufferfish optimization algorithm: a new bio-inspired metaheuristic algorithm for solving optimization problems. Biomimetics 9(2):65. https:\/\/doi.org\/10.3390\/biomimetics9020065","journal-title":"Biomimetics"},{"issue":"11","key":"1397_CR30","doi-asserted-by":"publisher","first-page":"e31629","DOI":"10.1016\/j.heliyon.2024.e31629","volume":"10","author":"JO Agushaka","year":"2024","unstructured":"Agushaka JO, Ezugwu AE, Saha AK, Pal J, Abualigah L, Mirjalili S (2024) Greater cane rat algorithm (GCRA): a nature-inspired metaheuristic for optimization problems. Heliyon 10(11):e31629. https:\/\/doi.org\/10.1016\/j.heliyon.2024.e31629","journal-title":"Heliyon"},{"key":"1397_CR31","doi-asserted-by":"publisher","first-page":"111257","DOI":"10.1016\/j.knosys.2023.111257","volume":"284","author":"M Abdel-Basset","year":"2024","unstructured":"Abdel-Basset M, Mohamed R, Abouhawwash M (2024) Crested porcupine optimizer: a new nature-inspired metaheuristic. Knowl-Based Syst 284:111257. https:\/\/doi.org\/10.1016\/j.knosys.2023.111257","journal-title":"Knowl-Based Syst"},{"key":"1397_CR32","doi-asserted-by":"publisher","first-page":"327","DOI":"10.1016\/j.apm.2023.10.045","volume":"126","author":"A-Q Tian","year":"2024","unstructured":"Tian A-Q, Liu F-F, Lv H-X (2024) Snow geese algorithm: a novel migration-inspired meta-heuristic algorithm for constrained engineering optimization problems. Appl Math Model 126:327\u2013347. https:\/\/doi.org\/10.1016\/j.apm.2023.10.045","journal-title":"Appl Math Model"},{"issue":"1","key":"1397_CR33","doi-asserted-by":"publisher","first-page":"10953","DOI":"10.1038\/s41598-022-14338-z","volume":"12","author":"MA Akbari","year":"2022","unstructured":"Akbari MA, Zare M, Azizipanah-abarghooee R, Mirjalili S, Deriche M (2022) The cheetah optimizer: a nature-inspired metaheuristic algorithm for large-scale optimization problems. Sci Rep 12(1):10953. https:\/\/doi.org\/10.1038\/s41598-022-14338-z","journal-title":"Sci Rep"},{"issue":"18","key":"1397_CR34","doi-asserted-by":"publisher","first-page":"9997","DOI":"10.3390\/app13189997","volume":"13","author":"ZA Memon","year":"2023","unstructured":"Memon ZA, Akbari MA, Zare M (2023) An improved cheetah optimizer for accurate and reliable estimation of unknown parameters in photovoltaic cell and module models. Appl Sci 13(18):9997. https:\/\/doi.org\/10.3390\/app13189997","journal-title":"Appl Sci"},{"issue":"9","key":"1397_CR35","doi-asserted-by":"publisher","first-page":"1277","DOI":"10.3390\/e25091277","volume":"25","author":"Y Fu","year":"2023","unstructured":"Fu Y, Yang S, Liu B, Xia E, Huang D (2023) Multi-UAV cooperative trajectory planning based on the modified cheetah optimization algorithm. Entropy 25(9):1277. https:\/\/doi.org\/10.3390\/e25091277","journal-title":"Entropy"},{"key":"1397_CR36","doi-asserted-by":"publisher","unstructured":"Kaul S, Kumar Y (2021) Nature-inspired metaheuristic algorithms for constraint handling: challenges, issues, and research perspective. In: Constraint handling in metaheuristics and applications. Springer Singapore, pp 55\u201380. https:\/\/doi.org\/10.1007\/978-981-33-6710-4_3","DOI":"10.1007\/978-981-33-6710-4_3"},{"key":"1397_CR37","doi-asserted-by":"publisher","unstructured":"Raidl GR (2006) A unified view on hybrid metaheuristics. In: Lecture notes in computer science. Springer, Berlin Heidelberg, pp 1\u201312. https:\/\/doi.org\/10.1007\/11890584_1","DOI":"10.1007\/11890584_1"},{"key":"1397_CR38","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.swevo.2017.09.010","volume":"39","author":"S Mahdavi","year":"2018","unstructured":"Mahdavi S, Rahnamayan S, Deb K (2018) Opposition based learning: a literature review. Swarm Evolut Comput 39:1\u201323. https:\/\/doi.org\/10.1016\/j.swevo.2017.09.010","journal-title":"Swarm Evolut Comput"},{"key":"1397_CR39","doi-asserted-by":"publisher","first-page":"100671","DOI":"10.1016\/j.swevo.2020.100671","volume":"54","author":"B Morales-Casta\u00f1eda","year":"2020","unstructured":"Morales-Casta\u00f1eda B, Zald\u00edvar D, Cuevas E, Fausto F, Rodr\u00edguez A (2020) A better balance in metaheuristic algorithms: Does it exist? Swarm Evolut Comput 54:100671. https:\/\/doi.org\/10.1016\/j.swevo.2020.100671","journal-title":"Swarm Evolut Comput"},{"issue":"3","key":"1397_CR40","doi-asserted-by":"publisher","first-page":"1804","DOI":"10.14419\/ijet.v7i3.18.14616","volume":"7","author":"M Goudhaman","year":"2018","unstructured":"Goudhaman M (2018) Cheetah chase algorithm (CCA): a nature-inspired metaheuristic algorithm. Int J Eng Technol 7(3):1804. https:\/\/doi.org\/10.14419\/ijet.v7i3.18.14616","journal-title":"Int J Eng Technol"},{"issue":"4","key":"1397_CR41","doi-asserted-by":"publisher","first-page":"13","DOI":"10.4018\/IJITPM.2020100102","volume":"11","author":"D Saravanan","year":"2020","unstructured":"Saravanan D, Paul PV, Janakiraman S, Dumka A, Jayakumar L (2020) A new bio-inspired algorithm based on the hunting behavior of cheetah. Int J Inf Technol Project Manag 11(4):13\u201330. https:\/\/doi.org\/10.4018\/IJITPM.2020100102","journal-title":"Int J Inf Technol Project Manag"},{"issue":"3","key":"1397_CR42","doi-asserted-by":"publisher","first-page":"977","DOI":"10.12785\/amis\/080306","volume":"8","author":"X-S Yang","year":"2014","unstructured":"Yang X-S, Deb S, Fong S (2014) metaheuristic algorithms: optimal balance of intensification and diversification. Appl Math Inf Sci 8(3):977\u2013983. https:\/\/doi.org\/10.12785\/amis\/080306","journal-title":"Appl Math Inf Sci"},{"key":"1397_CR43","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.engappai.2013.12.004","volume":"29","author":"Q Xu","year":"2014","unstructured":"Xu Q, Wang L, Wang N, Hei X, Zhao L (2014) A review of opposition-based learning from 2005 to 2012. Eng Appl Artif Intell 29:1\u201312. https:\/\/doi.org\/10.1016\/j.engappai.2013.12.004","journal-title":"Eng Appl Artif Intell"},{"key":"1397_CR44","unstructured":"Black PE (ed) (2019) Big-O notation. In: Dictionary of algorithms and data structures. https:\/\/www.nist.gov\/dads\/HTML\/bigOnotation.html. Accessed 23 Sept 2023"},{"issue":"3","key":"1397_CR45","doi-asserted-by":"publisher","first-page":"119","DOI":"10.2307\/3001946","volume":"3","author":"F Wilcoxon","year":"1947","unstructured":"Wilcoxon F (1947) Probability tables for individual comparisons by ranking methods. Biometrics 3(3):119. https:\/\/doi.org\/10.2307\/3001946","journal-title":"Biometrics"},{"key":"1397_CR46","unstructured":"Wu G, Mallipeddi R, Suganthan PN (2017) Problem definitions and evaluation criteria for the CEC 2017 competition and special session on constrained single objective real-parameter optimization. Technical Report"},{"key":"1397_CR47","unstructured":"Ahrari A, Elsayed SM, Sarker R, Essam D, Coello Coello CA (2022) Problem definition and evaluation criteria for the CEC'2022 competition on dynamic multimodal optimization. Technical Report"}],"container-title":["Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00607-024-01397-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00607-024-01397-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00607-024-01397-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,25]],"date-time":"2025-02-25T04:17:20Z","timestamp":1740457040000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00607-024-01397-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,27]]},"references-count":47,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,2]]}},"alternative-id":["1397"],"URL":"https:\/\/doi.org\/10.1007\/s00607-024-01397-5","relation":{},"ISSN":["0010-485X","1436-5057"],"issn-type":[{"value":"0010-485X","type":"print"},{"value":"1436-5057","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,27]]},"assertion":[{"value":"24 June 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 December 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 January 2025","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 no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"62"}}