{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,31]],"date-time":"2025-07-31T00:50:41Z","timestamp":1753923041983,"version":"3.35.0"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,1,4]],"date-time":"2025-01-04T00:00:00Z","timestamp":1735948800000},"content-version":"vor","delay-in-days":3,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Complex Intell. Syst."],"published-print":{"date-parts":[[2025,1]]},"DOI":"10.1007\/s40747-024-01738-z","type":"journal-article","created":{"date-parts":[[2025,1,4]],"date-time":"2025-01-04T09:35:44Z","timestamp":1735983344000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A novel graph convolution and frequency domain filtering approach for hyperspectral anomaly detection"],"prefix":"10.1007","volume":"11","author":[{"given":"Yang","family":"Ding","sequence":"first","affiliation":[]},{"given":"Hao","family":"Yan","sequence":"additional","affiliation":[]},{"given":"Jingyuan","family":"He","sequence":"additional","affiliation":[]},{"given":"Juanjuan","family":"Yin","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0791-3254","authenticated-orcid":false,"given":"A.","family":"Ruhan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,1,4]]},"reference":[{"key":"1738_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejcon.2024.101020","volume":"78","author":"MS Aslam","year":"2024","unstructured":"Aslam MS, Shamrooz S, Bilal H (2024) Fuzzy pd-sliding mode control design for networked system with time delays. Eur J Control 78:101020","journal-title":"Eur J Control"},{"issue":"8","key":"1738_CR2","doi-asserted-by":"publisher","first-page":"4987","DOI":"10.1007\/s00500-023-08026-x","volume":"27","author":"H Bilal","year":"2023","unstructured":"Bilal H, Yin B, Aslam MS, Anjum Z, Rohra A, Wang Y (2023) A practical study of active disturbance rejection control for rotary flexible joint robot manipulator. Soft Comput 27(8):4987\u20135001","journal-title":"Soft Comput"},{"key":"1738_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2024.108131","volume":"133","author":"MS Aslam","year":"2024","unstructured":"Aslam MS, Bilal H, Band SS, Ghasemi P (2024) Modeling of nonlinear supply chain management with lead-times based on takagi-sugeno fuzzy control model. Eng Appl Artif Intell 133:108131","journal-title":"Eng Appl Artif Intell"},{"issue":"1","key":"1738_CR4","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1007\/s00500-023-09442-9","volume":"28","author":"MS Aslam","year":"2024","unstructured":"Aslam MS, Bilal H, Hayajneh M (2024) Lqr-based pid controller with variable load tuned with data-driven methods for double inverted pendulum. Soft Comput 28(1):325\u2013338","journal-title":"Soft Comput"},{"issue":"7","key":"1738_CR5","doi-asserted-by":"publisher","first-page":"4029","DOI":"10.1007\/s00500-023-07923-5","volume":"27","author":"H Bilal","year":"2023","unstructured":"Bilal H, Yin B, Kumar A, Ali M, Zhang J, Yao J (2023) Jerk-bounded trajectory planning for rotary flexible joint manipulator: an experimental approach. Soft Comput 27(7):4029\u20134039","journal-title":"Soft Comput"},{"key":"1738_CR6","doi-asserted-by":"publisher","first-page":"4","DOI":"10.61185\/SMIJ.2023.22104","volume":"2","author":"J Alenizi","year":"2023","unstructured":"Alenizi J, Alrashdi I (2023) Sfmr-sh: Secure framework for mitigating ransomware attacks in smart healthcare using blockchain technology. Sustainable Machine Intelligence Journal 2:4\u20131","journal-title":"Sustainable Machine Intelligence Journal"},{"issue":"21","key":"1738_CR7","doi-asserted-by":"publisher","first-page":"16373","DOI":"10.1007\/s00500-023-09164-y","volume":"27","author":"H Dou","year":"2023","unstructured":"Dou H, Liu Y, Chen S, Zhao H, Bilal H (2023) A hybrid ceemd-gmm scheme for enhancing the detection of traffic flow on highways. Soft Comput 27(21):16373\u201316388","journal-title":"Soft Comput"},{"issue":"23","key":"1738_CR8","doi-asserted-by":"publisher","first-page":"18195","DOI":"10.1007\/s00500-023-09278-3","volume":"27","author":"Q Wu","year":"2023","unstructured":"Wu Q, Li X, Wang K, Bilal H (2023) Regional feature fusion for on-road detection of objects using camera and 3d-lidar in high-speed autonomous vehicles. Soft Comput 27(23):18195\u201318213","journal-title":"Soft Comput"},{"key":"1738_CR9","doi-asserted-by":"publisher","first-page":"6","DOI":"10.61185\/SMIJ.2023.33106","volume":"3","author":"M Ismail","year":"2023","unstructured":"Ismail M, Abd El-Gawad AF (2023) Revisiting zero-trust security for internet of things. Sustainable Machine Intelligence Journal 3:6\u20131","journal-title":"Sustainable Machine Intelligence Journal"},{"key":"1738_CR10","doi-asserted-by":"publisher","first-page":"4","DOI":"10.61185\/SMIJ.HPAO9103","volume":"1","author":"A Abdel-Monem","year":"2022","unstructured":"Abdel-Monem A, Abouhawwash M (2022) A machine learning solution for securing the internet of things infrastructures. Sustainable Machine Intelligence Journal 1:4\u20131","journal-title":"Sustainable Machine Intelligence Journal"},{"key":"1738_CR11","doi-asserted-by":"publisher","first-page":"4","DOI":"10.61185\/SMIJ.2023.33104","volume":"3","author":"B Alanazi","year":"2023","unstructured":"Alanazi B, Alrashdi I (2023) Anomaly detection in smart agriculture systems on network edge using deep learning technique. Sustainable Machine Intelligence Journal 3:4\u20131","journal-title":"Sustainable Machine Intelligence Journal"},{"issue":"3","key":"1738_CR12","doi-asserted-by":"publisher","first-page":"443","DOI":"10.3390\/rs10030443","volume":"10","author":"F Chen","year":"2018","unstructured":"Chen F, Ren R, De Voorde TV, Xu W, Zhou G, Zhou Y (2018) Fast automatic airport detection in remote sensing images using convolutional neural networks. Remote Sensing 10(3):443","journal-title":"Remote Sensing"},{"key":"1738_CR13","doi-asserted-by":"crossref","unstructured":"Ma, D., Yuan, Y., Wang, Q.: Hyperspectral anomaly detection based on separability-aware sample cascade. Remote Sensing 11(21) (2019) 10.3390\/rs11212537","DOI":"10.3390\/rs11212537"},{"issue":"1","key":"1738_CR14","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1109\/TGRS.2019.2936308","volume":"58","author":"S Li","year":"2020","unstructured":"Li S, Zhang K, Duan P, Kang X (2020) Hyperspectral anomaly detection with kernel isolation forest. IEEE Trans Geosci Remote Sens 58(1):319\u2013329. https:\/\/doi.org\/10.1109\/TGRS.2019.2936308","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"1738_CR15","doi-asserted-by":"crossref","unstructured":"Ma, D., Rehman, T.U., Zhang, L., Maki, H., Tuinstra, M.R., Jin, J.: Modeling of diurnal changing patterns in airborne crop remote sensing images. Remote Sensing 13(9) (2021) 10.3390\/rs13091719","DOI":"10.3390\/rs13091719"},{"issue":"21","key":"1738_CR16","doi-asserted-by":"publisher","first-page":"7444","DOI":"10.3390\/app10217444","volume":"10","author":"D Hao","year":"2020","unstructured":"Hao D, Yao Y, Fu J, Michalski JR, Song K (2020) The laboratory-based hyspex features of chlorite as the exploration tool for high-grade iron ore in anshan-benxi area, liaoning province, northeast china. Appl Sci 10(21):7444. https:\/\/doi.org\/10.3390\/app10217444","journal-title":"Appl Sci"},{"key":"1738_CR17","doi-asserted-by":"crossref","unstructured":"Duan, P., Ghamisi, P., Jackisch, R., Kang, X., Gloaguen, R., Li, S.: Intrinsic image decomposition-based resolution enhancement for mineral mapping. In: IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, pp. 4112\u20134115 (2020). 10.1109\/IGARSS39084.2020.9323470","DOI":"10.1109\/IGARSS39084.2020.9323470"},{"issue":"9","key":"1738_CR18","doi-asserted-by":"publisher","first-page":"1943","DOI":"10.3390\/app9091943","volume":"9","author":"L Wei","year":"2019","unstructured":"Wei L, Yuan Z, Zhong Y, Yang L, Hu X, Zhang Y (2019) An improved gradient boosting regression tree estimation model for soil heavy metal (arsenic) pollution monitoring using hyperspectral remote sensing. Appl Sci 9(9):1943. https:\/\/doi.org\/10.3390\/app9091943","journal-title":"Appl Sci"},{"key":"1738_CR19","doi-asserted-by":"crossref","unstructured":"Ma, Z., Jia, G., Schaepman, M.E., Zhao, H.: Uncertainty analysis for topographic correction of hyperspectral remote sensing images. Remote Sensing 12(4) (2020) 10.3390\/rs12040705","DOI":"10.3390\/rs12040705"},{"key":"1738_CR20","doi-asserted-by":"crossref","unstructured":"Awasthi S, Varade D (2021) Recent advances in the remote sensing of alpine snow: a review. GIScience & Remote Sensing 58(6):852\u2013888","DOI":"10.1080\/15481603.2021.1946938"},{"key":"1738_CR21","doi-asserted-by":"crossref","unstructured":"Chi, J., Kim, H.-C.: Retrieval of daily sea ice thickness from amsr2 passive microwave data using ensemble convolutional neural networks. GIScience & Remote Sensing 58(6), 812\u2013830 (2021) 10.1080\/15481603.2021.1943213","DOI":"10.1080\/15481603.2021.1943213"},{"key":"1738_CR22","unstructured":"Hartling, S., Sagan, V., Maimaitijiang, M.: Urban tree species classification using uav-based multi-sensor data fusion and machine learning. GIScience & Remote Sensing 0(0), 1\u201326 (2021) 10.1080\/15481603.2021.1974275"},{"key":"1738_CR23","doi-asserted-by":"crossref","unstructured":"Lu, B., Proctor, C., He, Y.: Investigating different versions of prospect and prosail for estimating spectral and biophysical properties of photosynthetic and non-photosynthetic vegetation in mixed grasslands. GIScience & Remote Sensing 58(3), 354\u2013371 (2021) 10.1080\/15481603.2021.1877435","DOI":"10.1080\/15481603.2021.1877435"},{"key":"1738_CR24","doi-asserted-by":"crossref","unstructured":"Paz-Kagan, T., Chang, J.G., Shoshany, M., Sternberg, M., Karnieli, A.: Assessment of plant species distribution and diversity along a climatic gradient from mediterranean woodlands to semi-arid shrublands. GIScience & Remote Sensing 58(6), 929\u2013953 (2021) 10.1080\/15481603.2021.1953770","DOI":"10.1080\/15481603.2021.1953770"},{"key":"1738_CR25","doi-asserted-by":"crossref","unstructured":"Jamali, A., Mahdianpari, M., Brisco, B., Granger, J., Mohammadimanesh, F., Salehi, B.: Deep forest classifier for wetland mapping using the combination of sentinel-1 and sentinel-2 data. GIScience & Remote Sensing 0(0), 1\u201318 (2021) 10.1080\/15481603.2021.1965399","DOI":"10.1080\/15481603.2021.1965399"},{"key":"1738_CR26","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1016\/j.isprsjprs.2016.12.009","volume":"124","author":"I Makki","year":"2017","unstructured":"Makki I, Younes R, Francis C, Bianchi T, Zucchetti M (2017) A survey of landmine detection using hyperspectral imaging. ISPRS J Photogramm Remote Sens 124:40\u201353","journal-title":"ISPRS J Photogramm Remote Sens"},{"key":"1738_CR27","doi-asserted-by":"publisher","unstructured":"Li W, Wu G, Du Q (2017) Transferred deep learning for anomaly detection in hyperspectral imagery. IEEE Geoscience and Remote Sensing Letters. https:\/\/doi.org\/10.1109\/lgrs.2017.2657818","DOI":"10.1109\/lgrs.2017.2657818"},{"key":"1738_CR28","doi-asserted-by":"publisher","unstructured":"Ma N, Peng Y, Wang S, Liu D (2018) Hyperspectral image anomaly targets detection with online deep learning. International Instrumentation and Measurement Technology Conference. https:\/\/doi.org\/10.1109\/i2mtc.2018.8409615","DOI":"10.1109\/i2mtc.2018.8409615"},{"key":"1738_CR29","doi-asserted-by":"publisher","unstructured":"Song S, Zhou H, Yang Y, Yang Y, Song J, Song J (2019) Hyperspectral anomaly detection via convolutional neural network and low rank with density-based clustering. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. https:\/\/doi.org\/10.1109\/jstars.2019.2926130","DOI":"10.1109\/jstars.2019.2926130"},{"key":"1738_CR30","doi-asserted-by":"publisher","unstructured":"Xie W, Liu B, Li Y, Li Y, Lei J, Du Q (2020) Autoencoder and adversarial-learning-based semisupervised background estimation for hyperspectral anomaly detection. IEEE Transactions on Geoscience and Remote Sensing. https:\/\/doi.org\/10.1109\/tgrs.2020.2965995","DOI":"10.1109\/tgrs.2020.2965995"},{"key":"1738_CR31","doi-asserted-by":"publisher","unstructured":"Wei J, Zhang J, Huang W, Xu L, Wu Z, Wei Z (2022) Hyperspectral anomaly detection based on graph regularized variational autoencoder. IEEE Geoscience and Remote Sensing Letters. https:\/\/doi.org\/10.1109\/lgrs.2022.3198403","DOI":"10.1109\/lgrs.2022.3198403"},{"key":"1738_CR32","doi-asserted-by":"publisher","unstructured":"Jiang T, Xie W, Li Y, Lei J, Du Q (2021) Weakly supervised discriminative learning with spectral constrained generative adversarial network for hyperspectral anomaly detection. IEEE Transactions on Neural Networks. https:\/\/doi.org\/10.1109\/tnnls.2021.3082158","DOI":"10.1109\/tnnls.2021.3082158"},{"key":"1738_CR33","doi-asserted-by":"crossref","unstructured":"Wang, D., Zhuang, L., Gao, L., Sun, X., Huang, M., Plaza, A.: Bocknet: Blind-block reconstruction network with a guard window for hyperspectral anomaly detection. IEEE Transactions on Geoscience and Remote Sensing 61, 1\u201316 (2023) 10.1109\/TGRS.2023.3335484","DOI":"10.1109\/TGRS.2023.3335484"},{"key":"1738_CR34","doi-asserted-by":"crossref","unstructured":"Wang, D., Zhuang, L., Gao, L., Sun, X., Huang, M., Plaza, A.J.: Pdbsnet: Pixel-shuffle downsampling blind-spot reconstruction network for hyperspectral anomaly detection. IEEE Transactions on Geoscience and Remote Sensing 61, 1\u201314 (2023) 10.1109\/TGRS.2023.3276175","DOI":"10.1109\/TGRS.2023.3276175"},{"key":"1738_CR35","doi-asserted-by":"crossref","unstructured":"Li, C., Zhang, B., Hong, D., Yao, J., Chanussot, J.: Lrr-net: An interpretable deep unfolding network for hyperspectral anomaly detection. IEEE Transactions on Geoscience and Remote Sensing 61, 1\u201312 (2023) 10.1109\/TGRS.2023.3279834","DOI":"10.1109\/TGRS.2023.3279834"},{"key":"1738_CR36","unstructured":"Tang, J., Li, J., Gao, Z., Li, J.: Rethinking graph neural networks for anomaly detection. In: International Conference on Machine Learning (2022)"},{"issue":"2","key":"1738_CR37","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1016\/j.acha.2010.04.005","volume":"30","author":"DK Hammond","year":"2011","unstructured":"Hammond DK, Vandergheynst P, Gribonval R (2011) Wavelets on graphs via spectral graph theory. APPLIED AND COMPUTATIONAL HARMONIC ANALYSIS 30(2):129\u2013150. https:\/\/doi.org\/10.1016\/j.acha.2010.04.005","journal-title":"APPLIED AND COMPUTATIONAL HARMONIC ANALYSIS"},{"key":"1738_CR38","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/LGRS.2022.3158652","volume":"19","author":"A Ruhan","year":"2022","unstructured":"Ruhan A, Mu X, He J (2022) Enhance tensor rpca-based mahalanobis distance method for hyperspectral anomaly detection. IEEE Geosci Remote Sens Lett 19:1\u20135","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"1738_CR39","doi-asserted-by":"crossref","unstructured":"Mu X, He J, Zhang J et al (2022) Enhance tensor rpca-lrx anomaly detection algorithm for hyperspectral image. Geocarto Int 37(26):11976\u201311997","DOI":"10.1080\/10106049.2022.2063400"},{"issue":"3","key":"1738_CR40","doi-asserted-by":"publisher","first-page":"1376","DOI":"10.1109\/TGRS.2015.2479299","volume":"54","author":"Y Zhang","year":"2015","unstructured":"Zhang Y, Du B, Zhang L, Wang S (2015) A low-rank and sparse matrix decomposition-based mahalanobis distance method for hyperspectral anomaly detection. IEEE Trans Geosci Remote Sens 54(3):1376\u20131389","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"10","key":"1738_CR41","doi-asserted-by":"publisher","first-page":"5600","DOI":"10.1109\/TGRS.2017.2710145","volume":"55","author":"X Kang","year":"2017","unstructured":"Kang X, Zhang X, Li S, Li K, Li J, Benediktsson JA (2017) Hyperspectral anomaly detection with attribute and edge-preserving filters. IEEE Trans Geosci Remote Sens 55(10):5600\u20135611. https:\/\/doi.org\/10.1109\/TGRS.2017.2710145","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"3","key":"1738_CR42","doi-asserted-by":"publisher","first-page":"1376","DOI":"10.1109\/TGRS.2015.2479299","volume":"54","author":"Y Zhang","year":"2016","unstructured":"Zhang Y, Du B, Zhang L, Wang S (2016) A low-rank and sparse matrix decomposition-based mahalanobis distance method for hyperspectral anomaly detection. IEEE Trans Geosci Remote Sens 54(3):1376\u20131389. https:\/\/doi.org\/10.1109\/TGRS.2015.2479299","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"5","key":"1738_CR43","doi-asserted-by":"publisher","first-page":"731","DOI":"10.1080\/07038992.2021.1959307","volume":"47","author":"X Mu","year":"2021","unstructured":"Mu X, Feng L, He J et al (2021) A fast recursive lrx algorithm with extended morphology profile for hyperspectral anomaly detection. Can J Remote Sens 47(5):731\u2013748","journal-title":"Can J Remote Sens"},{"issue":"10","key":"1738_CR44","doi-asserted-by":"publisher","first-page":"1760","DOI":"10.1109\/29.60107","volume":"38","author":"IS Reed","year":"1990","unstructured":"Reed IS, Yu X (1990) Adaptive multiple-band cfar detection of an optical pattern with unknown spectral distribution. IEEE Trans Acoust Speech Signal Process 38(10):1760\u20131770. https:\/\/doi.org\/10.1109\/29.60107","journal-title":"IEEE Trans Acoust Speech Signal Process"},{"key":"1738_CR45","doi-asserted-by":"publisher","unstructured":"Li W, Du Q (2015) Collaborative representation for hyperspectral anomaly detection. IEEE Trans Geosci Remote Sens 53(3):1463\u20131474. https:\/\/doi.org\/10.1109\/TGRS.2014.2343955","DOI":"10.1109\/TGRS.2014.2343955"},{"issue":"4","key":"1738_CR46","doi-asserted-by":"publisher","first-page":"2263","DOI":"10.1109\/TGRS.2018.2872590","volume":"57","author":"N Huyan","year":"2019","unstructured":"Huyan N, Zhang X, Zhou H, Jiao L (2019) Hyperspectral anomaly detection via background and potential anomaly dictionaries construction. IEEE Trans Geosci Remote Sens 57(4):2263\u20132276. https:\/\/doi.org\/10.1109\/TGRS.2018.2872590","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"1738_CR47","doi-asserted-by":"crossref","unstructured":"Jiang, K., Xie, W., Lei, J., Jiang, T., Li, Y.: Lren: Low-rank embedded network for sample-free hyperspectral anomaly detection. In: Thirty-fifth AAAI conference on artificial intelligence, thirty-third conference on innovative applications of artificial intelligence and the eleventh symposium on educational advances in artificial intelligence. AAAI Conference on Artificial Intelligence, vol. 35, pp. 4139\u20134146 (2021). Assoc Advancement Artificial Intelligence. 35th AAAI Conference on Artificial Intelligence \/ 33rd Conference on Innovative Applications of Artificial Intelligence \/ 11th Symposium on Educational Advances in Artificial Intelligence, ELECTR NETWORK, FEB 02-09, 2021","DOI":"10.1609\/aaai.v35i5.16536"},{"issue":"5","key":"1738_CR48","doi-asserted-by":"publisher","first-page":"597","DOI":"10.1109\/LGRS.2017.2657818","volume":"14","author":"W Li","year":"2017","unstructured":"Li W, Wu G, Du Q (2017) Transferred deep learning for anomaly detection in hyperspectral imagery. IEEE Geosci Remote Sens Lett 14(5):597\u2013601. https:\/\/doi.org\/10.1109\/LGRS.2017.2657818","journal-title":"IEEE Geosci Remote Sens Lett"}],"container-title":["Complex &amp; Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-024-01738-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40747-024-01738-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-024-01738-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,30]],"date-time":"2025-01-30T20:20:39Z","timestamp":1738268439000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40747-024-01738-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1]]},"references-count":48,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["1738"],"URL":"https:\/\/doi.org\/10.1007\/s40747-024-01738-z","relation":{},"ISSN":["2199-4536","2198-6053"],"issn-type":[{"type":"print","value":"2199-4536"},{"type":"electronic","value":"2198-6053"}],"subject":[],"published":{"date-parts":[[2025,1]]},"assertion":[{"value":"18 June 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 October 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 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":"119"}}