{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,29]],"date-time":"2026-03-29T17:47:46Z","timestamp":1774806466926,"version":"3.50.1"},"reference-count":68,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Discov Artif Intell"],"DOI":"10.1007\/s44163-025-00369-8","type":"journal-article","created":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T11:32:05Z","timestamp":1751369525000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Improving classifier decision boundaries and interpretability using nearest neighbors"],"prefix":"10.1007","volume":"5","author":[{"given":"Johannes","family":"Schneider","sequence":"first","affiliation":[]},{"given":"Arianna","family":"Casanova","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,7,1]]},"reference":[{"key":"369_CR1","doi-asserted-by":"crossref","unstructured":"Fukunaga K. Introduction to Statistical Pattern Recognition. 2nd ed. USA: Academic Press Professional Inc; 1990.","DOI":"10.1016\/B978-0-08-047865-4.50007-7"},{"key":"369_CR2","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1007\/BF00994018","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes C, Vapnik V. Support-vector networks. Machine Learning. 1995;20:273\u201397.","journal-title":"Machine Learning"},{"issue":"7553","key":"369_CR3","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436\u201344.","journal-title":"Nature"},{"issue":"5","key":"369_CR4","first-page":"1","volume":"13","author":"MA Chandra","year":"2021","unstructured":"Chandra MA, Bedi S. Survey on svm and their application in image classification. Int J Inf Technol. 2021;13(5):1\u201311.","journal-title":"Int J Inf Technol"},{"key":"369_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1561\/116.00000050","volume":"11","author":"J Li","year":"2022","unstructured":"Li J, et al. Recent advances in end-to-end automatic speech recognition. APSIPA Trans Signal Inf Process. 2022;11:1.","journal-title":"APSIPA Trans Signal Inf Process"},{"key":"369_CR6","doi-asserted-by":"crossref","unstructured":"Yan S, Xiong X, Arnab A, Lu Z, Zhang M, Sun C, Schmid C. Multiview transformers for video recognition. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 2022; 3333\u201343.","DOI":"10.1109\/CVPR52688.2022.00333"},{"issue":"1","key":"369_CR7","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1007\/s10462-022-10183-8","volume":"56","author":"JY-L Chan","year":"2023","unstructured":"Chan JY-L, Bea KT, Leow SMH, Phoong SW, Cheng WK. State of the art: a review of sentiment analysis based on sequential transfer learning. Artif Intell Rev. 2023;56(1):749\u201380.","journal-title":"Artif Intell Rev"},{"issue":"2","key":"369_CR8","doi-asserted-by":"publisher","first-page":"1627","DOI":"10.1007\/s10462-022-10209-1","volume":"56","author":"P Ma","year":"2023","unstructured":"Ma P, Li C, Rahaman MM, Yao Y, Zhang J, Zou S, Zhao X, Grzegorzek M. A state-of-the-art survey of object detection techniques in microorganism image analysis: from classical methods to deep learning approaches. Artif Intell Rev. 2023;56(2):1627\u201398.","journal-title":"Artif Intell Rev"},{"key":"369_CR9","unstructured":"Goodfellow IJ, Shlens J, Szegedy C. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 (2014)"},{"key":"369_CR10","doi-asserted-by":"crossref","unstructured":"Su D, Zhang H, Chen H, Yi J, Chen PY, Gao Y. Is robustness the cost of accuracy?\u2013a comprehensive study on the robustness of 18 deep image classification models. In: Proceedings of the European Conference on Computer Vision (ECCV),2018. pp. 631\u2013648 .","DOI":"10.1007\/978-3-030-01258-8_39"},{"key":"369_CR11","unstructured":"Tram\u00e8r F, Kurakin A, Papernot N, Goodfellow I, Boneh D, McDaniel P. Ensemble adversarial training: Attacks and defenses. arXiv preprint arXiv:1705.07204 2017."},{"key":"369_CR12","first-page":"1","volume":"31","author":"L Schmidt","year":"2018","unstructured":"Schmidt L, Santurkar S, Tsipras D, Talwar K, Madry A. Adversarially robust generalization requires more data. Adv Neural Inf Process Syst. 2018;31:1.","journal-title":"Adv Neural Inf Process Syst"},{"key":"369_CR13","first-page":"29578","volume":"34","author":"Y Li","year":"2021","unstructured":"Li Y, Yang Z, Wang Y, Xu C. Neural architecture dilation for adversarial robustness. Adv Neural Inf Process Syst. 2021;34:29578\u201389.","journal-title":"Adv Neural Inf Process Syst"},{"issue":"3","key":"369_CR14","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1109\/TAI.2021.3134600","volume":"3","author":"Y Zhou","year":"2021","unstructured":"Zhou Y, Yuan X, Zhang X, Liu W, Wu Y, Yen GG, Hu B, Yi Z. Evolutionary neural architecture search for automatic esophageal lesion identification and segmentation. IEEE Trans Artif Intell. 2021;3(3):436\u201350.","journal-title":"IEEE Trans Artif Intell"},{"issue":"4","key":"369_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3637873","volume":"42","author":"H Razgallah","year":"2024","unstructured":"Razgallah H, Vlachos M, Ajalloeian A, Liu N, Schneider J, Steinmann A. Using neural and graph neural recommender systems to overcome choice overload: evidence from a music education platform. ACM Trans Inf Syst. 2024;42(4):1\u201326.","journal-title":"ACM Trans Inf Syst"},{"key":"369_CR16","first-page":"21382","volume":"33","author":"P Wu","year":"2020","unstructured":"Wu P, Zheng S, Goswami M, Metaxas D, Chen C. A topological filter for learning with label noise. Adv Neural Inf Process Syst. 2020;33:21382\u201393.","journal-title":"Adv Neural Inf Process Syst"},{"key":"369_CR17","first-page":"2935","volume":"33","author":"G Ortiz-Jimenez","year":"2020","unstructured":"Ortiz-Jimenez G, Modas A, Moosavi S-M, Frossard P. Hold me tight! influence of discriminative features on deep network boundaries. Adv Neural Inf Process Syst. 2020;33:2935\u201346.","journal-title":"Adv Neural Inf Process Syst"},{"key":"369_CR18","unstructured":"Karimi H, Derr T, Tang J. Characterizing the decision boundary of deep neural networks. arXiv preprint arXiv:1912.11460 2019."},{"key":"369_CR19","first-page":"6223","volume":"33","author":"Y Yang","year":"2020","unstructured":"Yang Y, Khanna R, Yu Y, Gholami A, Keutzer K, Gonzalez JE, Ramchandran K, Mahoney MW. Boundary thickness and robustness in learning models. Adv Neural Inf Process Syst. 2020;33:6223\u201334.","journal-title":"Adv Neural Inf Process Syst"},{"key":"369_CR20","unstructured":"Oyen D, Kucer M, Hengartner N, Singh HS. Robustness to label noise depends on the shape of the noise distribution. Proceedings of the 36th International Conference on Neural Information Processing Systems,2022. pp. 35645\u201335656 ."},{"key":"369_CR21","unstructured":"Papernot N, McDaniel P. Deep k-nearest neighbors: towards confident, interpretable and robust deep learning. arXiv preprint arXiv:1803.04765 2018"},{"key":"369_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2024.102301","volume":"106","author":"L Longo","year":"2024","unstructured":"Longo L, Brcic M, Cabitza F, Choi J, Confalonieri R, Del Ser J, Guidotti R, Hayashi Y, Herrera F, Holzinger A, et al. Explainable artificial intelligence (xai) 2.0 A manifesto of open challenges and interdisciplinary research directions. Information Fus. 2024;106: 102301.","journal-title":"Information Fus"},{"key":"369_CR23","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-84858-7","volume-title":"The Elements of Statistical Learning: Data Mining, Inference, and Prediction","author":"T Hastie","year":"2009","unstructured":"Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. New York: Springer; 2009."},{"key":"369_CR24","first-page":"1","volume":"2","author":"J Schneider","year":"2022","unstructured":"Schneider J, Vlachos M. Explaining classifiers by constructing familiar concepts. Mach Learn. 2022;2:1\u201334.","journal-title":"Mach Learn"},{"key":"369_CR25","doi-asserted-by":"crossref","unstructured":"Yang C, Yu X, Liu Y. Continuous knn join processing for real-time recommendation. In: 2014 IEEE International Conference on Data Mining, IEEE;2014. pp. 640\u2013649 .","DOI":"10.1109\/ICDM.2014.20"},{"key":"369_CR26","first-page":"11","volume":"9","author":"L Maaten","year":"2008","unstructured":"Maaten L, Hinton G. Visualizing data using t-sne. J Mach Learn Res. 2008;9:11.","journal-title":"J Mach Learn Res"},{"key":"369_CR27","unstructured":"Shlens J. A tutorial on principal component analysis. arXiv preprint arXiv:1404.1100 2014."},{"key":"369_CR28","doi-asserted-by":"crossref","unstructured":"Garcia V, Debreuve E, Barlaud M. Fast k nearest neighbor search using gpu. In: 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, IEEE;2008. pp. 1\u20136.","DOI":"10.1109\/CVPRW.2008.4563100"},{"key":"369_CR29","unstructured":"Andoni A, Indyk P, Laarhoven T, Razenshteyn I, Schmidt L. Practical and optimal lsh for angular distance. Advances in neural information processing systems 2015. 28"},{"issue":"8","key":"369_CR30","first-page":"4139","volume":"44","author":"C Fu","year":"2021","unstructured":"Fu C, Wang C, Cai D. High dimensional similarity search with satellite system graph: Efficiency, scalability, and unindexed query compatibility. IEEE Trans Pattern Anal Mach Intell. 2021;44(8):4139\u201350.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"369_CR31","doi-asserted-by":"publisher","first-page":"972","DOI":"10.1007\/s10618-017-0498-x","volume":"31","author":"J Schneider","year":"2017","unstructured":"Schneider J, Vlachos M. Scalable density-based clustering with quality guarantees using random projections. Data Min Knowl Discov. 2017;31:972\u20131005.","journal-title":"Data Min Knowl Discov"},{"key":"369_CR32","unstructured":"Krizhevsky A, Hinton G. Learning multiple layers of features from tiny images. Technical report (2009)"},{"key":"369_CR33","doi-asserted-by":"crossref","unstructured":"Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248\u2013255 .2009","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"369_CR34","unstructured":"Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. 2014:1\u201314."},{"key":"369_CR35","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. Conference on Computer Vision and Pattern Recognition (CVPR), 2016; 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"369_CR36","doi-asserted-by":"crossref","unstructured":"Howard A, Sandler M, Chu G, Chen L-C, Chen B, Tan M, Wang W, Zhu Y, Pang R, Vasudevan V, et al. Searching for mobilenetv3. Proceedings of the IEEE\/CVF International Conference on Computer Vision. 2019; 1314\u201324.","DOI":"10.1109\/ICCV.2019.00140"},{"key":"369_CR37","doi-asserted-by":"publisher","unstructured":"Liu Z, Lin Y, Cao Y, Hu H, Wei X, Zhang Z, Lin S, Guo B. Swin transformer: Hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV),2021. pp. 10012\u201310022 . https:\/\/doi.org\/10.1109\/ICCV48922.2021.00988 . IEEE","DOI":"10.1109\/ICCV48922.2021.00988"},{"key":"369_CR38","doi-asserted-by":"crossref","unstructured":"Liu Z, Mao H, Wu CY, Feichtenhofer C, Darrell T, Xie S. A convnet for the 2020s. In: Proc. of the Conf. on Computer Vision and Pattern Recognition, 2022; 11966\u201311976","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"369_CR39","unstructured":"Madry A, Makelov A, Schmidt L, Tsipras D, Vladu A. Towards deep learning models resistant to adversarial attacks. arXiv preprint arXiv:1706.06083 2018."},{"key":"369_CR40","first-page":"14886","volume":"38","author":"J Schneider","year":"2024","unstructured":"Schneider J, Prabhushankar M. Understanding and leveraging the learning phases of neural networks. AAAI Conf Artif Intell. 2024;38:14886\u201393.","journal-title":"AAAI Conf Artif Intell"},{"key":"369_CR41","doi-asserted-by":"crossref","unstructured":"McInnes L, Healy J, Melville J. Umap: Uniform manifold approximation and projection for dimension reduction. arXiv preprint arXiv:1802.03426 2018.","DOI":"10.21105\/joss.00861"},{"key":"369_CR42","unstructured":"Kurakin A, Goodfellow IJ, Bengio S. Adversarial examples in the physical world. In: 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Workshop Track Proceedings 2017."},{"key":"369_CR43","unstructured":"Kim B, Wattenberg M, Gilmer J, Cai C, Wexler J, Viegas F, et al: Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In: International Conference on Machine Learning, PMLR;2018; 2668\u20132677."},{"key":"369_CR44","unstructured":"Douze M, Guzhva A, Deng C, Johnson J, Szilvasy G, Mazar\u00e9 PE, Lomeli M, Hosseini L, J\u00e9gou H. The faiss library. arXiv preprint arXiv:2401.08281 (2024)"},{"issue":"1","key":"369_CR45","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1109\/72.554193","volume":"8","author":"C Lee","year":"1997","unstructured":"Lee C, Landgrebe DA. Decision boundary feature extraction for neural networks. IEEE Trans Neural Netw. 1997;8(1):75\u201383.","journal-title":"IEEE Trans Neural Netw"},{"key":"369_CR46","volume-title":"Pattern Recognition and Machine Learning","author":"CM Bishop","year":"2006","unstructured":"Bishop CM. Pattern Recognition and Machine Learning. New York: Springer; 2006."},{"key":"369_CR47","unstructured":"Szegedy C, Zaremba W, Sutskever I, Bruna J, Erhan D, Goodfellow I, Fergus R. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 2013."},{"key":"369_CR48","doi-asserted-by":"crossref","unstructured":"Nguyen A, Yosinski J, Clune J. Deep neural networks are easily fooled: High confidence predictions for unrecognizable images. In: Proceedings of the Conf. on Computer Vision and Pattern Recognition (CVPR), 2015; 427\u2013436","DOI":"10.1109\/CVPR.2015.7298640"},{"key":"369_CR49","unstructured":"Fawzi A, Moosavi-Dezfooli SM, Frossard P, Soatto S. Classification regions of deep neural networks. arXiv preprint arXiv:1705.09552 2017."},{"key":"369_CR50","unstructured":"Li Y, Ding L, Gao X. On the decision boundary of deep neural networks. arXiv preprint arXiv:1808.05385 2018."},{"key":"369_CR51","unstructured":"Lei S, He F, Yuan Y, Tao D. Understanding deep learning via decision boundary. arXiv preprint arXiv:2206.01515 2022."},{"key":"369_CR52","doi-asserted-by":"crossref","unstructured":"Mouton C, Theunissen MW, Davel MH. Input margins can predict generalization too. arXiv preprint arXiv:2308.15466 2023","DOI":"10.1609\/aaai.v38i13.29351"},{"key":"369_CR53","unstructured":"Nar K, Ocal O, Shankar\u00a0Sastry S, Ramchandran K. Cross-entropy loss and low-rank features have responsibility for adversarial examples. arXiv:1901.08360 2019."},{"key":"369_CR54","unstructured":"Tang Y. Deep learning using support vector machines. CoRR, abs\/1306.0239 2013;2(1)"},{"issue":"3","key":"369_CR55","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1145\/3446776","volume":"64","author":"C Zhang","year":"2021","unstructured":"Zhang C, Bengio S, Hardt M, Recht B, Vinyals O. Understanding deep learning (still) requires rethinking generalization. Commun ACM. 2021;64(3):107\u201315.","journal-title":"Commun ACM"},{"key":"369_CR56","doi-asserted-by":"crossref","unstructured":"Zhang H, Berg AC, Maire M, Malik J. SVM-KNN: Discriminative nearest neighbor classification for visual category recognition. 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201906), 2006;2:2126\u20132136","DOI":"10.1109\/CVPR.2006.301"},{"issue":"1","key":"369_CR57","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1109\/TIT.1968.1054098","volume":"14","author":"T Cover","year":"1968","unstructured":"Cover T. Estimation by the nearest neighbor rule. IEEE Transactions on Information Theory. 1968;14(1):50\u20135.","journal-title":"IEEE Transactions on Information Theory"},{"key":"369_CR58","doi-asserted-by":"crossref","unstructured":"Zhuang J, Cai J, Wang R, Zhang J, Zheng, WS. Deep kNN for medical image classification. In: Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2020: 23rd International Conference, Lima, Peru, October 4\u20138, 2020, Proceedings, Part I 23, 2020; 127\u2013136","DOI":"10.1007\/978-3-030-59710-8_13"},{"key":"369_CR59","unstructured":"Khandelwal U, Levy O, Jurafsky D, Zettlemoyer L, Lewis M. Generalization through memorization: Nearest neighbor language models. arXiv preprint arXiv:1911.00172 2019."},{"key":"369_CR60","unstructured":"Borgeaud S, Mensch A, Hoffmann J, Cai T, Rutherford E, Millican K, Van Den\u00a0Driessche GB, Lespiau JB, Damoc B, Clark A, et al: Improving language models by retrieving from trillions of tokens. In: International Conference on Machine Learning, PMLR; 2022; 2206\u20132240."},{"key":"369_CR61","unstructured":"Xu FF, Alon U, Neubig G. Why do nearest neighbor language models work? International Conference on Machine Learning. PMLR; 2023; 38325\u201341."},{"issue":"7626","key":"369_CR62","doi-asserted-by":"publisher","first-page":"471","DOI":"10.1038\/nature20101","volume":"538","author":"A Graves","year":"2016","unstructured":"Graves A, Wayne G, Reynolds M, Harley T, Danihelka I, Grabska-Barwi\u0144ska A, Colmenarejo SG, Grefenstette E, Ramalho T, Agapiou J, et al. Hybrid computing using a neural network with dynamic external memory. Nature. 2016;538(7626):471\u20136.","journal-title":"Nature"},{"key":"369_CR63","first-page":"1","volume":"30","author":"A Vaswani","year":"2017","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I. Attention is all you need. Adv Neural Inf Process Syst. 2017;30:1.","journal-title":"Adv Neural Inf Process Syst"},{"key":"369_CR64","unstructured":"Bahdanau D, Cho K, Bengio Y. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 2014."},{"key":"369_CR65","unstructured":"Koh PW, Liang P. Understanding black-box predictions via influence functions. International Conference on Machine Learning. PMLR; 2017; 1885\u201394."},{"key":"369_CR66","doi-asserted-by":"crossref","unstructured":"Zhou Y, Hu B, Yuan X, Huang K, Yi Z, Yen GG. Multi-objective evolutionary generative adversarial network compression for image translation. IEEE Transactions on Evolutionary Computation 2023.","DOI":"10.1109\/TEVC.2023.3261135"},{"key":"369_CR67","unstructured":"Goodfellow I, Bengio Y, Courville A. Deep Learning. MIT Press, Cambridge, MA, USA (2016). http:\/\/www.deeplearningbook.org"},{"key":"369_CR68","unstructured":"Bertsekas D, Tsitsiklis JN. Introduction to Probability, vol. 1. Nashua, NH, USA: Athena Scientific; 2008."}],"container-title":["Discover Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44163-025-00369-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44163-025-00369-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44163-025-00369-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T11:32:18Z","timestamp":1751369538000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44163-025-00369-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,1]]},"references-count":68,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["369"],"URL":"https:\/\/doi.org\/10.1007\/s44163-025-00369-8","relation":{},"ISSN":["2731-0809"],"issn-type":[{"value":"2731-0809","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,1]]},"assertion":[{"value":"15 January 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 June 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 July 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":"This study does not involve experiments\nwith humans or animals. As such, ethical approval was not required. No participants\nwere involved, and no personal data or sensitive information was collected. All\nmethods were conducted in accordance with relevant guidelines and regulations.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"All authors have provided their consent for the publication of this research.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors have no relevant financial or non-financial interests to disclose.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"128"}}