{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T05:43:28Z","timestamp":1769838208690,"version":"3.49.0"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2024,12,31]],"date-time":"2024-12-31T00:00:00Z","timestamp":1735603200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,12,31]],"date-time":"2024-12-31T00:00:00Z","timestamp":1735603200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100016386","name":"Conselleria de Innovaci\u00f3n, Universidades, Ciencia y Sociedad Digital, Generalitat Valenciana","doi-asserted-by":"publisher","award":["CIGE\/2023\/52"],"award-info":[{"award-number":["CIGE\/2023\/52"]}],"id":[{"id":"10.13039\/501100016386","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100009092","name":"Universidad de Alicante","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100009092","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2025,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>According to the World Health Organization, thousands of people die every year in road traffic accidents. A crucial problem is the prediction of medical assistance in these accidents. For this purpose, we propose a new deep learning model whose goal is to distinguish whether a traffic accident requires medical assistance. The proposed perspective is general, so the model is valid for any dataset from any city. For this purpose, we present a model divided into three differentiated stages. In the first pre-processing stage, a general data treatment is performed, from data collection and cleaning to balancing. Secondly, the post-processing stage employs genetic and boosting algorithms to obtain the importance of all the data set variables used in the prediction. In the last stage, Model Training, a new model based on two-dimensional convolutional neural networks is applied to obtain a prediction of the need for medical assistance in traffic accidents. Finally, we test the effectiveness and accuracy of the proposed model by applying it to traffic accident datasets in six different cities. The obtained experimental results show that our framework achieves higher accuracy in all cities compared to six state-of-the-art models, confirming its suitability and applicability, even in real time.<\/jats:p>","DOI":"10.1007\/s00521-024-10939-z","type":"journal-article","created":{"date-parts":[[2024,12,31]],"date-time":"2024-12-31T17:14:16Z","timestamp":1735665256000},"page":"5343-5368","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A novel approach to predict the traffic accident assistance based on deep learning"],"prefix":"10.1007","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2990-1879","authenticated-orcid":false,"given":"Jos\u00e9 F.","family":"Vicent","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2307-1760","authenticated-orcid":false,"given":"Manuel","family":"Curado","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0216-0393","authenticated-orcid":false,"given":"Jos\u00e9 L.","family":"Oliver","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luis","family":"P\u00e9rez-Sala","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,12,31]]},"reference":[{"key":"10939_CR1","doi-asserted-by":"crossref","unstructured":"Pereira L, Tamilselvi P (2024) Modelling of fusion artificial neural networks for assessment of air pollution in smart city environment. In: 7th international conference on circuit power and computing technologies (ICCPCT), vol\u00a01, pp 1392\u20131399","DOI":"10.1109\/ICCPCT61902.2024.10672881"},{"issue":"26","key":"10939_CR2","doi-asserted-by":"publisher","first-page":"19117","DOI":"10.1007\/s00521-023-08726-3","volume":"35","author":"SR Vankdoth","year":"2023","unstructured":"Vankdoth SR, Arock M (2023) Deep intelligent transportation system for travel time estimation on spatio-temporal data. Neural Comput Appl 35(26):19117\u201319129","journal-title":"Neural Comput Appl"},{"key":"10939_CR3","doi-asserted-by":"crossref","unstructured":"Li J, Guo F, Zhou Y, Yang W, Ni D (2023) Predicting the severity of traffic accidents on mountain freeways with dynamic traffic and weather data. Transp Saf Environ tdad001","DOI":"10.1093\/tse\/tdad001"},{"issue":"4","key":"10939_CR4","doi-asserted-by":"publisher","first-page":"1790","DOI":"10.3390\/app12041790","volume":"12","author":"H Jeong","year":"2022","unstructured":"Jeong H, Kim I, Han K, Kim J (2022) Comprehensive analysis of traffic accidents in seoul: major factors and types affecting injury severity. Appl Sci 12(4):1790","journal-title":"Appl Sci"},{"issue":"3","key":"10939_CR5","doi-asserted-by":"publisher","first-page":"369","DOI":"10.1016\/S0001-4575(02)00013-1","volume":"35","author":"SS Zajac","year":"2003","unstructured":"Zajac SS, Ivan JN (2003) Factors influencing injury severity of motor vehicle-crossing pedestrian crashes in rural connecticut. Accid Anal Prevent 35(3):369\u2013379","journal-title":"Accid Anal Prevent"},{"key":"10939_CR6","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1016\/j.jsr.2020.02.008","volume":"73","author":"MA Vajari","year":"2020","unstructured":"Vajari MA, Aghabayk K, Sadeghian M, Shiwakoti N (2020) A multinomial logit model of motorcycle crash severity at australian intersections. J Saf Res 73:17\u201324","journal-title":"J Saf Res"},{"key":"10939_CR7","unstructured":"Behboudi N, Moosavi S, Ramnath R (2024) Recent advances in traffic accident analysis and prediction: a comprehensive review of machine learning techniques. arXiv preprint arXiv:2406.13968"},{"key":"10939_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.ssci.2021.105522","volume":"146","author":"Z Yang","year":"2022","unstructured":"Yang Z, Zhang W, Feng J (2022) Predicting multiple types of traffic accident severity with explanations: a multi-task deep learning framework. Saf Sci 146:105522","journal-title":"Saf Sci"},{"issue":"5","key":"10939_CR9","first-page":"77","volume":"27","author":"S Olugbade","year":"2022","unstructured":"Olugbade S, Ojo S, Imoize AL, Isabona J, Alaba MO (2022) A review of artificial intelligence and machine learning for incident detectors in road transport systems. Math Comput Appl 27(5):77","journal-title":"Math Comput Appl"},{"key":"10939_CR10","doi-asserted-by":"publisher","first-page":"478","DOI":"10.1016\/j.aap.2011.08.016","volume":"45","author":"Z Li","year":"2012","unstructured":"Li Z, Liu P, Wang W, Chengcheng X (2012) Using support vector machine models for crash injury severity analysis. Accid Anal Prevent 45:478\u2013486","journal-title":"Accid Anal Prevent"},{"issue":"2","key":"10939_CR11","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1016\/j.ijtst.2019.10.002","volume":"9","author":"M Rezapour","year":"2020","unstructured":"Rezapour M, Molan AM, Ksaibati K (2020) Analyzing injury severity of motorcycle at-fault crashes using machine learning techniques, decision tree and logistic regression models. Int J Transp Sci Technol 9(2):89\u201399","journal-title":"Int J Transp Sci Technol"},{"issue":"4","key":"10939_CR12","doi-asserted-by":"publisher","first-page":"425","DOI":"10.1080\/13588265.2016.1275431","volume":"22","author":"H-AH Seyed","year":"2017","unstructured":"Seyed H-AH, Seyed MHH (2017) Traffic accident severity prediction using a novel multi-objective genetic algorithm. Int J Crashworthiness 22(4):425\u2013440","journal-title":"Int J Crashworthiness"},{"issue":"11","key":"10939_CR13","doi-asserted-by":"publisher","first-page":"10051","DOI":"10.1007\/s12652-020-02759-5","volume":"12","author":"H Ospina-Mateus","year":"2021","unstructured":"Ospina-Mateus H, Jim\u00e9nez LAQ, Lopez-Valdes FJ, Garcia SB, Barrero LH, Sana SS (2021) Extraction of decision rules using genetic algorithms and simulated annealing for prediction of severity of traffic accidents by motorcyclists. J Ambient Intell Humaniz Comput 12(11):10051\u201310072","journal-title":"J Ambient Intell Humaniz Comput"},{"key":"10939_CR14","doi-asserted-by":"crossref","unstructured":"Beshah T, Ejigu D, Kr\u00f6mer P, Snel V, Plato J, Abraham A (2012) Learning the classification of traffic accident types. In: 2012 fourth international conference on intelligent networking and collaborative systems, pp 463\u2013468","DOI":"10.1109\/iNCoS.2012.75"},{"key":"10939_CR15","doi-asserted-by":"publisher","first-page":"353","DOI":"10.3846\/16484142.2011.635465","volume":"26","author":"M Kunt","year":"2011","unstructured":"Kunt M, Aghayan I, Noii N (2011) Prediction for traffic accident severity: comparing the artificial neural network, genetic algorithm, combined genetic algorithm and pattern search methods. Transport 26:353\u2013366","journal-title":"Transport"},{"key":"10939_CR16","doi-asserted-by":"publisher","first-page":"105468","DOI":"10.1016\/j.aap.2020.105468","volume":"138","author":"AM Amiri","year":"2020","unstructured":"Amiri AM, Sadri A, Nadimi N, Shams M (2020) A comparison between artificial neural network and hybrid intelligent genetic algorithm in predicting the severity of fixed object crashes among elderly drivers. Accid Anal Prevent 138:105468","journal-title":"Accid Anal Prevent"},{"issue":"1","key":"10939_CR17","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1177\/1748301817729953","volume":"12","author":"G Xiaoning","year":"2018","unstructured":"Xiaoning G, Li T, Wang Y, Zhang L, Wang Y, Yao J (2018) Traffic fatalities prediction using support vector machine with hybrid particle swarm optimization. J Algorithms Comput Technol 12(1):20\u201329","journal-title":"J Algorithms Comput Technol"},{"key":"10939_CR18","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/s41095-020-0191-7","volume":"7","author":"J Yuan","year":"2021","unstructured":"Yuan J, Chen C, Yang W, Liu M, Xia J, Liu S (2021) A survey of visual analytics techniques for machine learning. Comput Vis Media 7:3\u201336","journal-title":"Comput Vis Media"},{"key":"10939_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2022.111987","volume":"157","author":"K Li","year":"2022","unstructured":"Li K, Haocheng X, Liu X (2022) Analysis and visualization of accidents severity based on lightgbm-tpe. Chaos Solitons Fractals 157:111987","journal-title":"Chaos Solitons Fractals"},{"key":"10939_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2021.126804","volume":"591","author":"L Chen","year":"2022","unstructured":"Chen L, Sun J, Li K, Li Q (2022) Research on the effectiveness of monitoring mechanism for \u201cyield to pedestrian\u2019\u2019 based on system dynamicsliu2022grey. Physica A 591:126804","journal-title":"Physica A"},{"issue":"26","key":"10939_CR21","doi-asserted-by":"publisher","first-page":"19465","DOI":"10.1007\/s00521-023-08767-8","volume":"35","author":"S Babbar","year":"2023","unstructured":"Babbar S, Bedi J (2023) Real-time traffic, accident, and potholes detection by deep learning techniques: a modern approach for traffic management. Neural Comput Appl 35(26):19465\u201319479","journal-title":"Neural Comput Appl"},{"issue":"2","key":"10939_CR22","doi-asserted-by":"publisher","first-page":"1535","DOI":"10.1007\/s00521-022-07769-2","volume":"35","author":"K Sattar","year":"2023","unstructured":"Sattar K, Oughali FC, Assi K, Ratrout N, Jamal A, Rahman SM (2023) Transparent deep machine learning framework for predicting traffic crash severity. Neural Comput Appl 35(2):1535\u20131547","journal-title":"Neural Comput Appl"},{"key":"10939_CR23","doi-asserted-by":"publisher","first-page":"180","DOI":"10.1016\/j.aap.2019.01.007","volume":"124","author":"J Wang","year":"2019","unstructured":"Wang J, Kong Y, Ting F (2019) Expressway crash risk prediction using back propagation neural network: a brief investigation on safety resilience. Accid Anal Prevent 124:180\u2013192","journal-title":"Accid Anal Prevent"},{"key":"10939_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.aap.2021.106090","volume":"154","author":"MA Rahim","year":"2021","unstructured":"Rahim MA, Hassan HM (2021) A deep learning based traffic crash severity prediction framework. Accid Anal Prevent 154:106090","journal-title":"Accid Anal Prevent"},{"issue":"10","key":"10939_CR25","doi-asserted-by":"publisher","first-page":"1533","DOI":"10.1109\/TASLP.2014.2339736","volume":"22","author":"O Abdel-Hamid","year":"2014","unstructured":"Abdel-Hamid O, Mohamed A, Jiang H, Deng L, Penn G, Dong Yu (2014) Convolutional neural networks for speech recognition. IEEE\/ACM Trans Audio Speech Lang Process 22(10):1533\u20131545","journal-title":"IEEE\/ACM Trans Audio Speech Lang Process"},{"issue":"9","key":"10939_CR26","doi-asserted-by":"publisher","first-page":"1120","DOI":"10.1109\/LSP.2014.2325781","volume":"21","author":"P Swietojanski","year":"2014","unstructured":"Swietojanski P, Ghoshal A, Renals S (2014) Convolutional neural networks for distant speech recognition. IEEE Signal Process Lett 21(9):1120\u20131124","journal-title":"IEEE Signal Process Lett"},{"key":"10939_CR27","doi-asserted-by":"crossref","first-page":"08","DOI":"10.1038\/s41598-018-36957-1","volume":"9","author":"A Sharma","year":"2019","unstructured":"Sharma A, Vans E, Shigemizu D, Boroevich K, Tsunoda T (2019) Deepinsight: a methodology to transform a non-image data to an image for convolution neural network architecture. Sci Rep 9:08","journal-title":"Sci Rep"},{"issue":"9","key":"10939_CR28","doi-asserted-by":"publisher","first-page":"2352","DOI":"10.1162\/neco_a_00990","volume":"29","author":"W Rawat","year":"2017","unstructured":"Rawat W, Wang Z (2017) Deep convolutional neural networks for image classification: a comprehensive review. Neural Comput 29(9):2352\u20132449","journal-title":"Neural Comput"},{"key":"10939_CR29","doi-asserted-by":"crossref","unstructured":"Kiranyaz S, Avci O, Abdeljaber O, Ince T, Gabbouj M, Inman DJ (2019) 1d convolutional neural networks and applications: a survey","DOI":"10.1109\/ICASSP.2019.8682194"},{"issue":"1","key":"10939_CR30","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1109\/72.554195","volume":"8","author":"C Steve Lawrence","year":"1997","unstructured":"Steve Lawrence C, Lee Giles A, Tsoi C, Back AD (1997) Face recognition: a convolutional neural-network approach. IEEE Trans Neural Netw 8(1):98\u2013113","journal-title":"IEEE Trans Neural Netw"},{"key":"10939_CR31","doi-asserted-by":"crossref","unstructured":"Tensmeyer C, Martinez T (2017) Analysis of convolutional neural networks for document image classification","DOI":"10.1109\/ICDAR.2017.71"},{"key":"10939_CR32","unstructured":"Liu Y, Racah E, Correa J, Khosrowshahi A, Lavers DK, Wehner M, Collins W et\u00a0al (2016) Application of deep convolutional neural networks for detecting extreme weather in climate datasets. arXiv preprint arXiv:1605.01156"},{"key":"10939_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.eastsj.2021.100040","volume":"7","author":"ME Shaik","year":"2021","unstructured":"Shaik ME, Islam MM, Hossain QS (2021) A review on neural network techniques for the prediction of road traffic accident severity. Asian Transp Stud 7:100040","journal-title":"Asian Transp Stud"},{"key":"10939_CR34","doi-asserted-by":"publisher","first-page":"761","DOI":"10.1016\/j.neucom.2022.05.072","volume":"500","author":"Y Liu","year":"2022","unstructured":"Liu Y, Chaozhong W, Wen J, Xiao X, Chen Z (2022) A grey convolutional neural network model for traffic flow prediction under traffic accidents. Neurocomputing 500:761\u2013775","journal-title":"Neurocomputing"},{"key":"10939_CR35","doi-asserted-by":"crossref","unstructured":"Wenqi L, Dongyu L, Menghua Y (2017) A model of traffic accident prediction based on convolutional neural network. In: 2017 2nd IEEE international conference on intelligent transportation engineering (ICITE). IEEE, pp 198\u2013202","DOI":"10.1109\/ICITE.2017.8056908"},{"key":"10939_CR36","doi-asserted-by":"publisher","first-page":"20708","DOI":"10.1109\/ACCESS.2019.2896913","volume":"7","author":"J An","year":"2019","unstructured":"An J, Li F, Meng H, Chen W, Zhan J (2019) A novel fuzzy-based convolutional neural network method to traffic flow prediction with uncertain traffic accident information. Ieee Access 7:20708\u201320722","journal-title":"Ieee Access"},{"key":"10939_CR37","doi-asserted-by":"publisher","first-page":"3703","DOI":"10.1016\/j.trpro.2017.05.221","volume":"25","author":"A Laiou","year":"2017","unstructured":"Laiou A, Papadimitriou E, Yannis G, Milotti A (2017) Road safety data and information availability and priorities in south-east european regions. Transp Res Procedia 25:3703\u20133714 (World Conference on Transport Research - WCTR 2016 Shanghai. 10-15 July 2016)","journal-title":"Transp Res Procedia"},{"key":"10939_CR38","doi-asserted-by":"crossref","unstructured":"Fiorentini N, Losa M (2020) Handling imbalanced data in road crash severity prediction by machine learning algorithms. Infrastructures 5(7)","DOI":"10.3390\/infrastructures5070061"},{"key":"10939_CR39","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2023.113245","volume":"169","author":"L P\u00e9rez-Sala","year":"2023","unstructured":"P\u00e9rez-Sala L, Curado M, Tortosa L, Vicent JF (2023) Deep learning model of convolutional neural networks powered by a genetic algorithm for prevention of traffic accidents severity. Chaos, Solitons Fractals 169:113245","journal-title":"Chaos, Solitons Fractals"},{"key":"10939_CR40","doi-asserted-by":"crossref","unstructured":"Han H, Wang W-Y, Mao B-H (2005) Borderline-smote: a new over-sampling method in imbalanced data sets learning. In: Proceedings of the international conference on intelligent computing, vol 3644. Springer, pp 878\u2013887","DOI":"10.1007\/11538059_91"},{"key":"10939_CR41","doi-asserted-by":"crossref","unstructured":"Kumar M, Husain Dr\u00a0M, Upreti N, Gupta D (2010) Genetic algorithm: Review and application. Available at SSRN 3529843","DOI":"10.2139\/ssrn.3529843"},{"key":"10939_CR42","doi-asserted-by":"crossref","unstructured":"Yacouby R, Axman D (2020) Probabilistic extension of precision, recall, and f1 score for more thorough evaluation of classification models. In: Proceedings of the first workshop on evaluation and comparison of NLP systems, Online, Nov. Association for Computational Linguistics, pp 79\u201391","DOI":"10.18653\/v1\/2020.eval4nlp-1.9"},{"key":"10939_CR43","doi-asserted-by":"crossref","unstructured":"Ruby U, Yendapalli V (2020) Binary cross entropy with deep learning technique for image classification. Int J Adv Trends Comput Sci Eng 9(10)","DOI":"10.30534\/ijatcse\/2020\/175942020"},{"key":"10939_CR44","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2019.105361","volume":"192","author":"S Chen","year":"2020","unstructured":"Chen S, Webb GI, Liu L, Ma X (2020) A novel selective na\u00efve bayes algorithm. Knowl-Based Syst 192:105361","journal-title":"Knowl-Based Syst"},{"key":"10939_CR45","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.105754","volume":"196","author":"S Alam","year":"2020","unstructured":"Alam S, Sonbhadra SK, Agarwal S, Nagabhushan P (2020) One-class support vector classifiers: a survey. Knowl-Based Syst 196:105754","journal-title":"Knowl-Based Syst"},{"key":"10939_CR46","first-page":"13","volume-title":"K-nearest neighbors","author":"O Kramer","year":"2013","unstructured":"Kramer O (2013) K-nearest neighbors. Springer, Berlin, pp 13\u201323"},{"issue":"1","key":"10939_CR47","first-page":"3","volume":"20","author":"S Matthias","year":"2020","unstructured":"Matthias S, Rosie YZ (2020) The random forest algorithm for statistical learning. Stand Genomic Sci 20(1):3\u201329","journal-title":"Stand Genomic Sci"},{"key":"10939_CR48","doi-asserted-by":"crossref","unstructured":"Das A (2021) Logistic regression. In: Encyclopedia of quality of life and well-being research. Springer, pp 1\u20132","DOI":"10.1007\/978-3-319-69909-7_1689-2"},{"key":"10939_CR49","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.115147","volume":"181","author":"FAD Campo","year":"2021","unstructured":"Campo FAD, Neri MCG, Villegas OOV, S\u00e1nchez VGC, de Jes\u00fas H, Dom\u00ednguez O, Jim\u00e9nez VG (2021) Auto-adaptive multilayer perceptron for univariate time series classification. Expert Syst Appl 181:115147","journal-title":"Expert Syst Appl"},{"key":"10939_CR50","unstructured":"Department for Transport. Reported road collisions, vehicles and casualties tables for great britain, 2023. Available at https:\/\/www.gov.uk\/government\/statistical-data-sets\/reported-road-accidents-vehicles-and-casualties-tables-for-great-britain"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10939-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-024-10939-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10939-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,28]],"date-time":"2025-02-28T16:23:59Z","timestamp":1740759839000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-024-10939-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,31]]},"references-count":50,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2025,3]]}},"alternative-id":["10939"],"URL":"https:\/\/doi.org\/10.1007\/s00521-024-10939-z","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,31]]},"assertion":[{"value":"2 August 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 December 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 December 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 economic interests directly or indirectly related to the work submitted for publication. No funding information is applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}