{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T19:05:47Z","timestamp":1784228747445,"version":"3.55.0"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2025,2,15]],"date-time":"2025-02-15T00:00:00Z","timestamp":1739577600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,2,15]],"date-time":"2025-02-15T00:00:00Z","timestamp":1739577600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2022YFB3707800"],"award-info":[{"award-number":["2022YFB3707800"]}]},{"DOI":"10.13039\/100014718","name":"Innovative Research Group Project of the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 62172267"],"award-info":[{"award-number":["No. 62172267"]}],"id":[{"id":"10.13039\/100014718","id-type":"DOI","asserted-by":"publisher"}]},{"name":"State Key Program of National Natural Science Foundation of China","award":["Grant No. 61936001"],"award-info":[{"award-number":["Grant No. 61936001"]}]},{"name":"the Project of Key Laboratory of Silicate Cultural Relics Conservation","award":["No. SCRC2023ZZ02ZD"],"award-info":[{"award-number":["No. SCRC2023ZZ02ZD"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2025,5]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>With the widespread application of time series data, the study of classification techniques has become an important topic. Although existing multivariate time series classification (MTSC) methods have made progress, they often rely on one-dimensional (1D) time series, which limits their ability to capture complex temporal dynamics and multiscale features. To address these challenges, a Spectral Convolutional Network (SCNet) is introduced in this work. SCNet effectively transforms 1D time series data into the frequency domain using an enhanced Discrete Fourier Transform (enhanced_DFT), revealing periodicity and key frequency components while reshaping the data into a two-dimensional (2D) time series for better representation. Furthermore, it uses a Spectral Energy Prioritization method to optimize frequency domain energy distribution and a multiscale convolutional module to capture features at different scales, improving the model\u2019s ability to analyze short-term and long-term trends. To validate the effectiveness and superiority, we conducted extensive experiments on 10 sub-datasets from the well-known UEA dataset. The results show that our proposed SCNet achieved the highest average accuracy of 74.3%, which is 2.2% higher than the current state-of-the-art models, demonstrating its potential for practical application and efficiency in MTSC task.<\/jats:p>","DOI":"10.1007\/s10489-025-06352-1","type":"journal-article","created":{"date-parts":[[2025,2,15]],"date-time":"2025-02-15T06:04:27Z","timestamp":1739599467000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Scnet: spectral convolutional networks for multivariate time series classification"],"prefix":"10.1007","volume":"55","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5331-022X","authenticated-orcid":false,"given":"Xing","family":"Wu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyu","family":"Xing","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junfeng","family":"Yao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Quan","family":"Qian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Song","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,2,15]]},"reference":[{"key":"6352_CR1","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1016\/j.ins.2020.05.066","volume":"538","author":"X Wu","year":"2020","unstructured":"Wu X, Chen H, Wang J et al (2020) Adaptive stock trading strategies with deep reinforcement learning methods. Inf Sci 538:142\u2013158. https:\/\/doi.org\/10.1016\/j.ins.2020.05.066","journal-title":"Inf Sci"},{"key":"6352_CR2","doi-asserted-by":"publisher","first-page":"108123","DOI":"10.1016\/j.patcog.2021.108123","volume":"120","author":"J Wang","year":"2021","unstructured":"Wang J, Guo X, Li W et al (2021) Statistical mechanical analysis for unweighted and weighted stock market networks. Pattern Recogn 120:108123. https:\/\/doi.org\/10.1016\/j.patcog.2021.108123","journal-title":"Pattern Recogn"},{"issue":"11","key":"6352_CR3","doi-asserted-by":"publisher","first-page":"7933","DOI":"10.1007\/s10489-021-02309-2","volume":"51","author":"J Liao","year":"2021","unstructured":"Liao J, Liu D, Su G et al (2021) Recognizing diseases with multivariate physiological signals by a deepcnn-lstm network. Appl Intell 51(11):7933\u20137945. https:\/\/doi.org\/10.1007\/s10489-021-02309-2","journal-title":"Appl Intell"},{"issue":"3","key":"6352_CR4","doi-asserted-by":"publisher","first-page":"1296","DOI":"10.1007\/s10489-020-01862-6","volume":"51","author":"J Shuja","year":"2021","unstructured":"Shuja J, Alanazi E, Alasmary W et al (2021) Covid-19 open source data sets: a comprehensive survey. Appl Intell 51(3):1296\u20131325. https:\/\/doi.org\/10.1007\/s10489-020-01862-6","journal-title":"Appl Intell"},{"issue":"19","key":"6352_CR5","doi-asserted-by":"publisher","first-page":"21723","DOI":"10.1007\/s10489-023-04594-5","volume":"53","author":"WH Suh","year":"2023","unstructured":"Suh WH, Oh S, Ahn CW (2023) Metaheuristic-based time series clustering for anomaly detection in manufacturing industry. Appl Intell 53(19):21723\u201321742. https:\/\/doi.org\/10.1007\/s10489-023-04594-5","journal-title":"Appl Intell"},{"issue":"3","key":"6352_CR6","doi-asserted-by":"publisher","first-page":"2346","DOI":"10.1007\/s10489-021-02441-z","volume":"52","author":"H Wang","year":"2022","unstructured":"Wang H, Lu W, Tang S et al (2022) Predict industrial equipment failure with time windows and transfer learning. Appl Intell 52(3):2346\u20132358. https:\/\/doi.org\/10.1007\/s10489-021-02441-z","journal-title":"Appl Intell"},{"issue":"19","key":"6352_CR7","doi-asserted-by":"publisher","first-page":"22803","DOI":"10.1007\/s10489-023-04729-8","volume":"53","author":"L Chen","year":"2023","unstructured":"Chen L, Peng C, Yang C et al (2023) Domain adversarial-based multi-source deep transfer network for cross-production-line time series forecasting. Appl Intell 53(19):22803\u201322817. https:\/\/doi.org\/10.1007\/s10489-023-04729-8","journal-title":"Appl Intell"},{"issue":"20","key":"6352_CR8","doi-asserted-by":"publisher","first-page":"23039","DOI":"10.1007\/s10489-023-04756-5","volume":"53","author":"X Wu","year":"2023","unstructured":"Wu X, Tao C, Zhang J et al (2023) Space or time for video classification transformers. Appl Intell 53(20):23039\u201323048. https:\/\/doi.org\/10.1007\/s10489-023-04756-5","journal-title":"Appl Intell"},{"key":"6352_CR9","doi-asserted-by":"publisher","DOI":"10.1109\/MIS.2024.3378921","author":"J Wang","year":"2024","unstructured":"Wang J, Liu X, Li W et al (2024) Te-tfn: A text-enhanced transformer fusion network for multimodal knowledge graph completion. IEEE Intell Syst. https:\/\/doi.org\/10.1109\/MIS.2024.3378921","journal-title":"IEEE Intell Syst"},{"key":"6352_CR10","doi-asserted-by":"publisher","unstructured":"Zerveas G, Jayaraman S, Patel D, et\u00a0al (2021) A transformer-based framework for multivariate time series representation learning. In: Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining, pp 2114\u20132124. https:\/\/doi.org\/10.1145\/3447548.3467401","DOI":"10.1145\/3447548.3467401"},{"key":"6352_CR11","doi-asserted-by":"publisher","unstructured":"Liu S, Yu H, Liao C, et\u00a0al (2021) Pyraformer: Low-complexity pyramidal attention for long-range time series modeling and forecasting. In: International conference on learning representations. https:\/\/doi.org\/10.34726\/2945","DOI":"10.34726\/2945"},{"key":"6352_CR12","doi-asserted-by":"publisher","unstructured":"Yue Z, Wang Y, Duan J, et\u00a0al (2022) Ts2vec: Towards universal representation of time series. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 8980\u20138987. https:\/\/doi.org\/10.1609\/aaai.v36i8.20881","DOI":"10.1609\/aaai.v36i8.20881"},{"key":"6352_CR13","doi-asserted-by":"publisher","first-page":"110158","DOI":"10.1016\/j.knosys.2022.110158","volume":"260","author":"S Hao","year":"2023","unstructured":"Hao S, Wang Z, Alexander AD et al (2023) Micos: Mixed supervised contrastive learning for multivariate time series classification. Knowl-Based Syst 260:110158. https:\/\/doi.org\/10.1016\/j.knosys.2022.110158","journal-title":"Knowl-Based Syst"},{"key":"6352_CR14","doi-asserted-by":"publisher","unstructured":"Wen Q, Sun L, Yang F, et\u00a0al (2020) Time series data augmentation for deep learning: A survey. arXiv preprint arXiv:2002.12478. https:\/\/doi.org\/10.48550\/arXiv.2002.12478","DOI":"10.48550\/arXiv.2002.12478"},{"issue":"6","key":"6352_CR15","doi-asserted-by":"publisher","first-page":"1936","DOI":"10.1007\/s10618-020-00710-y","volume":"34","author":"H Ismail Fawaz","year":"2020","unstructured":"Ismail Fawaz H, Lucas B, Forestier G et al (2020) Inceptiontime: Finding alexnet for time series classification. Data Min Knowl Disc 34(6):1936\u20131962. https:\/\/doi.org\/10.1007\/s10618-020-00710-y","journal-title":"Data Min Knowl Disc"},{"key":"6352_CR16","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1016\/j.neunet.2019.04.014","volume":"116","author":"F Karim","year":"2019","unstructured":"Karim F, Majumdar S, Darabi H et al (2019) Multivariate lstm-fcns for time series classification. Neural Netw 116:237\u2013245. https:\/\/doi.org\/10.1016\/j.neunet.2019.04.014","journal-title":"Neural Netw"},{"key":"6352_CR17","doi-asserted-by":"publisher","unstructured":"Zhang X, Gao Y, Lin J, et\u00a0al (2020) Tapnet: Multivariate time series classification with attentional prototypical network. In: Proceedings of the AAAI conference on artificial intelligence, pp 6845\u20136852. https:\/\/doi.org\/10.1609\/aaai.v34i04.6165","DOI":"10.1609\/aaai.v34i04.6165"},{"key":"6352_CR18","doi-asserted-by":"publisher","unstructured":"Wu H, Hu T, Liu Y, et\u00a0al (2022) Timesnet: Temporal 2d-variation modeling for general time series analysis. arXiv preprint arXiv:2210.02186. https:\/\/doi.org\/10.48550\/arXiv.2210.02186","DOI":"10.48550\/arXiv.2210.02186"},{"key":"6352_CR19","doi-asserted-by":"publisher","first-page":"384","DOI":"10.1016\/j.neucom.2019.06.032","volume":"359","author":"W Chen","year":"2019","unstructured":"Chen W, Shi K (2019) A deep learning framework for time series classification using relative position matrix and convolutional neural network. Neurocomputing 359:384\u2013394. https:\/\/doi.org\/10.1016\/j.neucom.2019.06.032","journal-title":"Neurocomputing"},{"key":"6352_CR20","doi-asserted-by":"publisher","first-page":"106437","DOI":"10.1016\/j.epsr.2020.106437","volume":"187","author":"SR Fahim","year":"2020","unstructured":"Fahim SR, Sarker Y, Sarker SK et al (2020) Self attention convolutional neural network with time series imaging based feature extraction for transmission line fault detection and classification. Electr Power Syst Res 187:106437. https:\/\/doi.org\/10.1016\/j.epsr.2020.106437","journal-title":"Electr Power Syst Res"},{"key":"6352_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ins.2020.02.069","volume":"523","author":"M Fahim","year":"2020","unstructured":"Fahim M, Fraz K, Sillitti A (2020) Tsi: Time series to imaging based model for detecting anomalous energy consumption in smart buildings. Inf Sci 523:1\u201313. https:\/\/doi.org\/10.1016\/j.ins.2020.02.069","journal-title":"Inf Sci"},{"key":"6352_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113680","volume":"160","author":"X Li","year":"2020","unstructured":"Li X, Kang Y, Li F (2020) Forecasting with time series imaging. Expert Syst Appl 160:113680. https:\/\/doi.org\/10.1016\/j.eswa.2020.113680","journal-title":"Expert Syst Appl"},{"key":"6352_CR23","doi-asserted-by":"publisher","unstructured":"Zhang Y, Gan F, Chen X (2020) Motif difference field: An effective image-based time series classification and applications in machine malfunction detection. In: 2020 IEEE 4th Conference on Energy Internet and Energy System Integration (EI2), IEEE, pp 3079\u20133083. https:\/\/doi.org\/10.1109\/EI250167.2020.9346704","DOI":"10.1109\/EI250167.2020.9346704"},{"key":"6352_CR24","doi-asserted-by":"publisher","first-page":"592","DOI":"10.1016\/j.ins.2020.08.089","volume":"547","author":"H Li","year":"2021","unstructured":"Li H (2021) Time works well: Dynamic time warping based on time weighting for time series data mining. Inf Sci 547:592\u2013608. https:\/\/doi.org\/10.1016\/j.ins.2020.08.089","journal-title":"Inf Sci"},{"issue":"21","key":"6352_CR25","doi-asserted-by":"publisher","first-page":"7414","DOI":"10.3390\/s21217414","volume":"21","author":"J Li","year":"2021","unstructured":"Li J, Zhang H, Dong Y et al (2021) An improved self-training method for positive unlabeled time series classification using dtw barycenter averaging. Sensors 21(21):7414. https:\/\/doi.org\/10.3390\/s21217414","journal-title":"Sensors"},{"issue":"7","key":"6352_CR26","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.3390\/app9071345","volume":"9","author":"M Rhif","year":"2019","unstructured":"Rhif M, Ben Abbes A, Farah IR et al (2019) Wavelet transform application for\/in non-stationary time-series analysis: A review. Appl Sci 9(7):1345. https:\/\/doi.org\/10.3390\/app9071345","journal-title":"Appl Sci"},{"key":"6352_CR27","doi-asserted-by":"publisher","unstructured":"Yu Y, Zhu Y, Wan D, et\u00a0al (2019) A novel symbolic aggregate approximation for time series. In: Proceedings of the 13th International Conference on Ubiquitous Information Management and Communication (IMCOM) 2019 13, Springer, pp 805\u2013822. https:\/\/doi.org\/10.1007\/978-3-030-19063-7_65","DOI":"10.1007\/978-3-030-19063-7_65"},{"key":"6352_CR28","doi-asserted-by":"publisher","first-page":"107171","DOI":"10.1016\/j.comnet.2020.107171","volume":"173","author":"H Zhao","year":"2020","unstructured":"Zhao H, Pan Z, Tao W (2020) Regularized shapelet learning for scalable time series classification. Comput Netw 173:107171. https:\/\/doi.org\/10.1016\/j.comnet.2020.107171","journal-title":"Comput Netw"},{"key":"6352_CR29","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1016\/j.neunet.2019.04.014","volume":"116","author":"F Karim","year":"2019","unstructured":"Karim F, Majumdar S, Darabi H et al (2019) Multivariate lstm-fcns for time series classification. Neural Netw 116:237\u2013245. https:\/\/doi.org\/10.1016\/j.neunet.2019.04.014","journal-title":"Neural Netw"},{"key":"6352_CR30","doi-asserted-by":"publisher","unstructured":"Li G, Choi B, Xu J, et\u00a0al (2021) Shapenet: A shapelet-neural network approach for multivariate time series classification. In: Proceedings of the AAAI conference on artificial intelligence, pp 8375\u20138383. https:\/\/doi.org\/10.1609\/aaai.v35i9.17018","DOI":"10.1609\/aaai.v35i9.17018"},{"key":"6352_CR31","doi-asserted-by":"publisher","first-page":"8597606","DOI":"10.1155\/2019\/8597606","volume":"1","author":"L Teng","year":"2019","unstructured":"Teng L, Li H (2019) Karim S (2019) Dmcnn: a deep multiscale convolutional neural network model for medical image segmentation. Journal of Healthcare Engineering 1:8597606. https:\/\/doi.org\/10.1155\/2019\/8597606","journal-title":"Journal of Healthcare Engineering"},{"key":"6352_CR32","doi-asserted-by":"publisher","first-page":"102789","DOI":"10.1016\/j.bspc.2021.102789","volume":"69","author":"DS Shibu","year":"2021","unstructured":"Shibu DS, Priyadharsini SS (2021) Multi scale decomposition based medical image fusion using convolutional neural network and sparse representation. Biomed Signal Process Control 69:102789. https:\/\/doi.org\/10.1016\/j.bspc.2021.102789","journal-title":"Biomed Signal Process Control"},{"issue":"1","key":"6352_CR33","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1007\/s10916-019-1494-z","volume":"44","author":"SA Agnes","year":"2020","unstructured":"Agnes SA, Anitha J, Pandian SIA et al (2020) Classification of mammogram images using multiscale all convolutional neural network (ma-cnn). J Med Syst 44(1):30. https:\/\/doi.org\/10.1007\/s10916-019-1494-z","journal-title":"J Med Syst"},{"key":"6352_CR34","doi-asserted-by":"publisher","unstructured":"Zhu W, Omar M (2023) Multiscale audio spectrogram transformer for efficient audio classification. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1\u20135. https:\/\/doi.org\/10.1109\/ICASSP49357.2023.10096513","DOI":"10.1109\/ICASSP49357.2023.10096513"},{"issue":"1","key":"6352_CR35","doi-asserted-by":"publisher","first-page":"8636","DOI":"10.1038\/s41598-022-12121-8","volume":"12","author":"J Liu","year":"2022","unstructured":"Liu J, Zhang Y, Lv D et al (2022) Birdsong classification based on ensemble multi-scale convolutional neural network. Sci Rep 12(1):8636. https:\/\/doi.org\/10.1038\/s41598-022-12121-8","journal-title":"Sci Rep"},{"key":"6352_CR36","doi-asserted-by":"publisher","unstructured":"Wu X, Jin H, Ye X et al (2020) Multiscale convolutional and recurrent neural network for quality prediction of continuous casting slabs. Processes 9(1):33. https:\/\/doi.org\/10.3390\/pr9010033","DOI":"10.3390\/pr9010033"},{"key":"6352_CR37","doi-asserted-by":"publisher","unstructured":"Yang G, Zhong Y, Yang L et al (2020) Fault detection of harmonic drive using multiscale convolutional neural network. IEEE Trans Instrum Meas 70:1\u201311. https:\/\/doi.org\/10.1109\/TIM.2020.3024355","DOI":"10.1109\/TIM.2020.3024355"},{"key":"6352_CR38","doi-asserted-by":"publisher","unstructured":"Shi Y, Deng A, Deng M et al (2020) Enhanced lightweight multiscale convolutional neural network for rolling bearing fault diagnosis. IEEE Access 8:217723\u2013217734. https:\/\/doi.org\/10.1109\/ACCESS.2020.3041735","DOI":"10.1109\/ACCESS.2020.3041735"},{"key":"6352_CR39","doi-asserted-by":"publisher","unstructured":"Cao D, Wang Y, Duan J, et\u00a0al (2021) Spectral temporal graph neural network for multivariate time-series forecasting. arXiv preprint arXiv:2103.07719. https:\/\/doi.org\/10.48550\/arXiv.2103.07719","DOI":"10.48550\/arXiv.2103.07719"},{"key":"6352_CR40","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1016\/j.aca.2020.11.018","volume":"1143","author":"JL Xu","year":"2021","unstructured":"Xu JL, Hugelier S, Zhu H et al (2021) Deep learning for classification of time series spectral images using combined multi-temporal and spectral features. Anal Chim Acta 1143:9\u201320. https:\/\/doi.org\/10.1016\/j.aca.2020.11.018","journal-title":"Anal Chim Acta"},{"issue":"2","key":"6352_CR41","doi-asserted-by":"publisher","first-page":"401","DOI":"10.1007\/s10618-020-00727-3","volume":"35","author":"AP Ruiz","year":"2021","unstructured":"Ruiz AP, Flynn M, Large J et al (2021) The great multivariate time series classification bake off: a review and experimental evaluation of recent algorithmic advances. Data Min Knowl Disc 35(2):401\u2013449. https:\/\/doi.org\/10.1007\/s10618-020-00727-3","journal-title":"Data Min Knowl Disc"},{"issue":"7","key":"6352_CR42","doi-asserted-by":"publisher","first-page":"e0254841","DOI":"10.1371\/journal.pone.0254841","volume":"16","author":"BK Iwana","year":"2021","unstructured":"Iwana BK, Uchida S (2021) An empirical survey of data augmentation for time series classification with neural networks. PLoS ONE 16(7):e0254841. https:\/\/doi.org\/10.1371\/journal.pone.0254841","journal-title":"PLoS ONE"},{"issue":"4","key":"6352_CR43","doi-asserted-by":"publisher","first-page":"917","DOI":"10.1007\/s10618-019-00619-1","volume":"33","author":"H Ismail Fawaz","year":"2019","unstructured":"Ismail Fawaz H, Forestier G, Weber J et al (2019) Deep learning for time series classification: a review. Data Min Knowl Disc 33(4):917\u2013963. https:\/\/doi.org\/10.1007\/s10618-019-00619-1","journal-title":"Data Min Knowl Disc"},{"key":"6352_CR44","doi-asserted-by":"publisher","unstructured":"Demirkaya A, Chen J, Oymak S (2020) Exploring the role of loss functions in multiclass classification. In: 2020 54th annual conference on information sciences and systems (ciss), IEEE, pp 1\u20135. https:\/\/doi.org\/10.1109\/CISS48834.2020.1570627167","DOI":"10.1109\/CISS48834.2020.1570627167"},{"key":"6352_CR45","doi-asserted-by":"publisher","unstructured":"Mao A, Mohri M, Zhong Y (2023) Cross-entropy loss functions: Theoretical analysis and applications. arXiv preprint arXiv:2304.07288. https:\/\/doi.org\/10.48550\/arXiv.2304.07288","DOI":"10.48550\/arXiv.2304.07288"},{"key":"6352_CR46","doi-asserted-by":"publisher","unstructured":"Bagnall A, Dau HA, Lines J, et\u00a0al (2018) The uea multivariate time series classification archive, 2018. arXiv preprint arXiv:1811.00075. https:\/\/doi.org\/10.48550\/arXiv.1811.00075","DOI":"10.48550\/arXiv.1811.00075"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-025-06352-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-025-06352-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-025-06352-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T19:34:28Z","timestamp":1758310468000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-025-06352-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,15]]},"references-count":46,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2025,5]]}},"alternative-id":["6352"],"URL":"https:\/\/doi.org\/10.1007\/s10489-025-06352-1","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,15]]},"assertion":[{"value":"9 February 2025","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 February 2025","order":2,"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 Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"All authors give consent to participate.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}}],"article-number":"456"}}