{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,27]],"date-time":"2025-09-27T08:10:22Z","timestamp":1758960622269,"version":"3.44.0"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"15","license":[{"start":{"date-parts":[[2025,9,27]],"date-time":"2025-09-27T00:00:00Z","timestamp":1758931200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,27]],"date-time":"2025-09-27T00:00:00Z","timestamp":1758931200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"This research is supported by the Research Foundation of Education Bureau of Liaoning Province","award":["Grant No. LZD202001"],"award-info":[{"award-number":["Grant No. LZD202001"]}]},{"name":"the Science and Technology Project of Department of Science & Technology of Liaoning Province","award":["Grant No. 2021JH1\/10400029"],"award-info":[{"award-number":["Grant No. 2021JH1\/10400029"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"DOI":"10.1007\/s11227-025-07827-1","type":"journal-article","created":{"date-parts":[[2025,9,27]],"date-time":"2025-09-27T07:55:08Z","timestamp":1758959708000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["WFocusedGait: wavelet-inspired focused multimodal feature fusion for gait recognition"],"prefix":"10.1007","volume":"81","author":[{"given":"Diyuan","family":"Guan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chunsheng","family":"Hua","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoheng","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,9,27]]},"reference":[{"issue":"1","key":"7827_CR1","doi-asserted-by":"publisher","first-page":"264","DOI":"10.1109\/TPAMI.2022.3151865","volume":"45","author":"A Sepas-Moghaddam","year":"2023","unstructured":"Sepas-Moghaddam A, Etemad A (2023) Deep gait recognition: a survey. IEEE Trans Pattern Anal Mach Intell 45(1):264\u2013284. https:\/\/doi.org\/10.1109\/TPAMI.2022.3151865","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"7827_CR2","doi-asserted-by":"crossref","unstructured":"Chao H, He Y, Zhang J, Feng J (2019) GaitSet: regarding gait as a set for cross-view gait recognition. In: The 33rd AAAI Conference on Artificial Intelligence, AAAI Press, pp 8126\u20138133","DOI":"10.1609\/aaai.v33i01.33018126"},{"key":"7827_CR3","doi-asserted-by":"crossref","unstructured":"Fan C, Peng Y, Cao C, Liu X, Hou S, Chi J, et\u00a0al (2020) GaitPart: temporal part-based model for gait recognition. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2020), Seattle, WA, USA, June 13\u201319. Computer Vision Foundation\/IEEE, pp 14213\u201314221","DOI":"10.1109\/CVPR42600.2020.01423"},{"key":"7827_CR4","doi-asserted-by":"crossref","unstructured":"Wang Z, Hou S, Zhang M, Liu X, Cao C, Huang Y, et\u00a0al (2024) QAGait: revisit gait recognition from a quality perspective. In: 38th AAAI Conference on Artificial Intelligence, AAAI, pp 5785\u20135793","DOI":"10.1609\/aaai.v38i6.28391"},{"issue":"2","key":"7827_CR5","doi-asserted-by":"publisher","first-page":"1375","DOI":"10.1007\/S11760-023-02851-1","volume":"18","author":"P Nithyakani","year":"2024","unstructured":"Nithyakani P, Ukrit MF (2024) Deep multi-convolutional stacked capsule network fostered human gait recognition from enhanced gait energy image. Signal Image Video Process 18(2):1375\u20131382. https:\/\/doi.org\/10.1007\/S11760-023-02851-1","journal-title":"Signal Image Video Process"},{"key":"7827_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/J.ESWA.2024.123181","volume":"246","author":"SS Kumar","year":"2024","unstructured":"Kumar SS, Singh B, Chattopadhyay P, Halder A, Wang L (2024) BGaitR-Net: an effective neural model for occlusion reconstruction in gait sequences by exploiting the key pose information. Expert Syst Appl 246:123181. https:\/\/doi.org\/10.1016\/J.ESWA.2024.123181","journal-title":"Expert Syst Appl"},{"key":"7827_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIM.2024.3497164","volume":"74","author":"M Saad Shakeel","year":"2025","unstructured":"Saad Shakeel M, Liu K, Liao X, Kang W (2025) Tag: a temporal attentive gait network for cross-view gait recognition. IEEE Trans Instrum Meas 74:1\u201314. https:\/\/doi.org\/10.1109\/TIM.2024.3497164","journal-title":"IEEE Trans Instrum Meas"},{"key":"7827_CR8","doi-asserted-by":"publisher","DOI":"10.1111\/EXSY.13244","author":"C Zhang","year":"2023","unstructured":"Zhang C, Chen X, Han G, Liu X (2023) Spatial transformer network on skeleton-based gait recognition. Expert Syst J Knowl Eng. https:\/\/doi.org\/10.1111\/EXSY.13244","journal-title":"Expert Syst J Knowl Eng"},{"issue":"2","key":"7827_CR9","doi-asserted-by":"publisher","first-page":"1123","DOI":"10.1109\/TCSVT.2024.3476384","volume":"35","author":"W Zhai","year":"2025","unstructured":"Zhai W, Li H, Zheng C, Xing X (2025) Multi-view gait recognition with joint local multi-scale and global contextual spatio-temporal features. IEEE Trans Circuits Syst Video Technol 35(2):1123\u20131135. https:\/\/doi.org\/10.1109\/TCSVT.2024.3476384","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"7827_CR10","first-page":"357","volume-title":"Metagait: learning to learn an omni sample adaptive representation for gait recognition. In: European Conference on Computer Vision","author":"H Dou","year":"2022","unstructured":"Dou H, Zhang P, Su W, Yu Y, Li X (2022) Metagait: learning to learn an omni sample adaptive representation for gait recognition. In: European Conference on Computer Vision. Springer, Berlin, pp 357\u2013374"},{"issue":"12","key":"7827_CR11","doi-asserted-by":"publisher","first-page":"17606","DOI":"10.1007\/s11227-024-06089-7","volume":"80","author":"S Wei","year":"2024","unstructured":"Wei S, Liu W, Wei F, Wang C, Xiong NN (2024) Gaitdlf: global and local fusion for skeleton-based gait recognition in the wild. J Supercomput 80(12):17606\u201317632. https:\/\/doi.org\/10.1007\/s11227-024-06089-7","journal-title":"J Supercomput"},{"key":"7827_CR12","doi-asserted-by":"crossref","unstructured":"Wei B, Ling H, Shi Y (2024) Importance-aware spatial\u2013temporal representation learning for gait recognition. In: International Joint Conference on Neural Networks (IJCNN 2024), Yokohama, Japan, June 30\u2013July 5. IEEE, pp 1\u20138","DOI":"10.1109\/IJCNN60899.2024.10650643"},{"key":"7827_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2024.128313","volume":"605","author":"PH Progga","year":"2024","unstructured":"Progga PH, Rahman MJ, Biswas S, Ahmed MS, Anwary AR, Shatabda S (2024) A bidirectional Siamese recurrent neural network for accurate gait recognition using body landmarks. Neurocomputing 605:128313","journal-title":"Neurocomputing"},{"key":"7827_CR14","doi-asserted-by":"crossref","unstructured":"Fan C, Liang J, Shen C, Hou S, Huang Y, Yu S (2023) Opengait: revisiting gait recognition towards better practicality. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 9707\u20139716","DOI":"10.1109\/CVPR52729.2023.00936"},{"issue":"2","key":"7827_CR15","doi-asserted-by":"publisher","first-page":"1535","DOI":"10.1007\/s10489-022-03543-y","volume":"53","author":"G Li","year":"2023","unstructured":"Li G, Guo L, Zhang R, Qian J, Gao S (2023) Transgait: multimodal-based gait recognition with set transformer. Appl Intell 53(2):1535\u20131547","journal-title":"Appl Intell"},{"key":"7827_CR16","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2025.3576283","author":"C Fan","year":"2025","unstructured":"Fan C, Hou S, Liang J, Shen C, Ma J, Jin D et al (2025) OpenGait: a comprehensive benchmark study for gait recognition towards better practicality. IEEE Trans Pattern Anal Mach Intell. https:\/\/doi.org\/10.1109\/TPAMI.2025.3576283","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"7827_CR17","first-page":"1","volume":"63","author":"H Sun","year":"2025","unstructured":"Sun H, Yao Z, Du B, Wan J, Ren D, Tong L (2025) Spatial\u2013frequency residual-guided dynamic perceptual network for remote sensing image haze removal. IEEE Trans Geosci Remote Sens 63:1\u201316","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"7827_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2024.110763","volume":"156","author":"H Sun","year":"2024","unstructured":"Sun H, Luo Z, Ren D, Du B, Chang L, Wan J (2024) Unsupervised multi-branch network with high-frequency enhancement for image dehazing. Pattern Recognit 156:110763","journal-title":"Pattern Recognit"},{"issue":"4","key":"7827_CR19","doi-asserted-by":"publisher","first-page":"539","DOI":"10.1109\/TBIOM.2024.3384704","volume":"6","author":"S Zou","year":"2024","unstructured":"Zou S, Xiong J, Fan C, Shen C, Yu S, Tang J (2024) A multi-stage adaptive feature fusion neural network for multimodal gait recognition. IEEE Trans Biom Behav Identity Sci 6(4):539\u2013549. https:\/\/doi.org\/10.1109\/TBIOM.2024.3384704","journal-title":"IEEE Trans Biom Behav Identity Sci"},{"issue":"6","key":"7827_CR20","doi-asserted-by":"publisher","first-page":"9777","DOI":"10.1109\/TNNLS.2025.3526815","volume":"36","author":"J Li","year":"2025","unstructured":"Li J, Zhang Y, Zeng Y, Ye C, Xu W, Ben X et al (2025) Rethinking appearance-based deep gait recognition: reviews, analysis, and insights from gait recognition evolution. IEEE Trans Neural Netw Learn Syst 36(6):9777\u20139797. https:\/\/doi.org\/10.1109\/TNNLS.2025.3526815","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"7827_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2024.111219","volume":"161","author":"G Peng","year":"2025","unstructured":"Peng G, Wang Y, Zhang S, Li R, Zhao Y, Li A (2025) RSANet: relative-sequence quality assessment network for gait recognition in the wild. Pattern Recognit 161:111219","journal-title":"Pattern Recognit"},{"key":"7827_CR22","doi-asserted-by":"publisher","unstructured":"Peng G, Wang Y, Zhao Y, Zhang S, Li A (2024) GLGait: a global-local temporal receptive field network for gait recognition in the wild. In: Cai J, Kankanhalli MS, Prabhakaran B, Boll S, Subramanian R, Zheng L et\u00a0al (eds) Proceedings of the 32nd ACM International Conference on Multimedia (MM 2024), Melbourne, VIC, Australia, 28 October\u20131 November. ACM, pp 826\u2013835. https:\/\/doi.org\/10.1145\/3664647.3680812","DOI":"10.1145\/3664647.3680812"},{"key":"7827_CR23","doi-asserted-by":"publisher","unstructured":"Wang M, Guo X, Lin B, Yang T, Zhu Z, Li L, et\u00a0al (2023) DyGait: exploiting dynamic representations for high-performance gait recognition. In: IEEE\/CVF International Conference on Computer Vision (ICCV 2023), Paris, France, October 1\u20136. IEEE, pp 13378\u201313387. https:\/\/doi.org\/10.1109\/ICCV51070.2023.01235","DOI":"10.1109\/ICCV51070.2023.01235"},{"key":"7827_CR24","doi-asserted-by":"publisher","unstructured":"Ma K, Fu Y, Cao C, Hou S, Huang Y, Zheng D (2024) Learning visual prompt for gait recognition. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2024), Seattle, WA, USA, June 16\u201322. IEEE, pp 593\u2013603. https:\/\/doi.org\/10.1109\/CVPR52733.2024.00063","DOI":"10.1109\/CVPR52733.2024.00063"},{"issue":"1","key":"7827_CR25","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1007\/s11760-024-03611-5","volume":"19","author":"L Zhang","year":"2025","unstructured":"Zhang L, Men Z, Xie W (2025) Gaitts: indoor gait recognition with multi-scale temporal\u2013spatial information aggregation. SIViP 19(1):28. https:\/\/doi.org\/10.1007\/s11760-024-03611-5","journal-title":"SIViP"},{"key":"7827_CR26","doi-asserted-by":"publisher","unstructured":"Ye D, Fan C, Ma J, Liu X, Yu S (2024) BigGait: learning gait representation you want by large vision models. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2024), Seattle, WA, USA, June 16\u201322. IEEE, pp 200\u2013210. https:\/\/doi.org\/10.1109\/CVPR52733.2024.00027","DOI":"10.1109\/CVPR52733.2024.00027"},{"key":"7827_CR27","doi-asserted-by":"publisher","unstructured":"Fu Y, Meng S, Hou S, Hu X, Huang Y (2023) GPGait: generalized pose-based gait recognition. In: IEEE\/CVF International Conference on Computer Vision (ICCV 2023), Paris, France, October 1\u20136. IEEE, pp 19538\u201319547. https:\/\/doi.org\/10.1109\/ICCV51070.2023.01795","DOI":"10.1109\/ICCV51070.2023.01795"},{"key":"7827_CR28","unstructured":"Min F, Cai Q, Guo S, Yu Y, Fan H, Dong J (2024) ZipGait: bridging skeleton and silhouette with diffusion model for advancing gait recognition. arXiv preprint arXiv:2408.12111"},{"issue":"9","key":"7827_CR29","doi-asserted-by":"publisher","first-page":"12130","DOI":"10.1109\/TNNLS.2023.3252172","volume":"35","author":"X Gao","year":"2024","unstructured":"Gao X, Yang Y, Wu Y, Du S (2024) Learning heterogeneous spatial\u2013temporal context for skeleton-based action recognition. IEEE Trans Neural Netw Learn Syst 35(9):12130\u201312141. https:\/\/doi.org\/10.1109\/TNNLS.2023.3252172","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"7827_CR30","doi-asserted-by":"publisher","first-page":"405","DOI":"10.1109\/TMM.2021.3127040","volume":"25","author":"X Gao","year":"2023","unstructured":"Gao X, Yang Y, Zhang Y, Li M, Yu JG, Du S (2023) Efficient spatio-temporal contrastive learning for skeleton-based 3-d action recognition. IEEE Trans Multimed 25:405\u2013417. https:\/\/doi.org\/10.1109\/TMM.2021.3127040","journal-title":"IEEE Trans Multimed"},{"key":"7827_CR31","doi-asserted-by":"publisher","first-page":"551","DOI":"10.1016\/J.NEUNET.2023.07.051","volume":"167","author":"X Gao","year":"2023","unstructured":"Gao X, Du S, Yang Y (2023) Glimpse and focus: global and local-scale graph convolution network for skeleton-based action recognition. Neural Netw 167:551\u2013558. https:\/\/doi.org\/10.1016\/J.NEUNET.2023.07.051","journal-title":"Neural Netw"},{"key":"7827_CR32","doi-asserted-by":"crossref","unstructured":"Fan C, Ma J, Jin D, Shen C, Yu S (2024) Skeletongait: gait recognition using skeleton maps. In: Wooldridge MJ, Dy JG, Natarajan S (eds) 38th AAAI Conference on Artificial Intelligence (AAAI 2024), pp 1662\u20131669","DOI":"10.1609\/aaai.v38i2.27933"},{"issue":"1","key":"7827_CR33","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1109\/TBIOM.2024.3435046","volume":"7","author":"Y Sun","year":"2025","unstructured":"Sun Y, Feng X, Liu X, Ma L, Hu L, Nixon MS (2025) TriGait: hybrid fusion strategy for multimodal alignment and integration in gait recognition. IEEE Trans Biom Behav Identity Sci 7(1):82\u201394. https:\/\/doi.org\/10.1109\/TBIOM.2024.3435046","journal-title":"IEEE Trans Biom Behav Identity Sci"},{"key":"7827_CR34","doi-asserted-by":"publisher","unstructured":"Cui Y, Kang Y (2023) Multi-modal gait recognition via effective spatial\u2013temporal feature fusion. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2023), Vancouver, BC, Canada, June 17\u201324. IEEE, pp 17949\u201317957. https:\/\/doi.org\/10.1109\/CVPR52729.2023.01721","DOI":"10.1109\/CVPR52729.2023.01721"},{"issue":"20","key":"7827_CR35","doi-asserted-by":"publisher","first-page":"4362","DOI":"10.3390\/electronics12204362","volume":"12","author":"Y Huo","year":"2023","unstructured":"Huo Y, Gang S, Guan C (2023) FCIHMRT: feature cross-layer interaction hybrid method based on Res2Net and transformer for remote sensing scene classification. Electronics 12(20):4362","journal-title":"Electronics"},{"issue":"10","key":"7827_CR36","doi-asserted-by":"publisher","first-page":"7221","DOI":"10.1007\/S00371-024-03426-Y","volume":"40","author":"J Xiong","year":"2024","unstructured":"Xiong J, Zou S, Tang J, Tjahjadi T (2024) MCDGait: multimodal co-learning distillation network with spatial\u2013temporal graph reasoning for gait recognition in the wild. Vis Comput 40(10):7221\u20137234. https:\/\/doi.org\/10.1007\/S00371-024-03426-Y","journal-title":"Vis Comput"},{"issue":"3","key":"7827_CR37","doi-asserted-by":"publisher","first-page":"7273","DOI":"10.1007\/S11042-023-15483-X","volume":"83","author":"Y Peng","year":"2024","unstructured":"Peng Y, Ma K, Zhang Y, He Z (2024) Learning rich features for gait recognition by integrating skeletons and silhouettes. Multimed Tools Appl 83(3):7273\u20137294. https:\/\/doi.org\/10.1007\/S11042-023-15483-X","journal-title":"Multimed Tools Appl"},{"issue":"33","key":"7827_CR38","doi-asserted-by":"publisher","first-page":"80225","DOI":"10.1007\/S11042-024-19596-9","volume":"83","author":"L Xue","year":"2024","unstructured":"Xue L, Tao Z (2024) GaitRA: triple-branch multimodal gait recognition with larger effective receptive fields and mixed attention. Multimed Tools Appl 83(33):80225\u201380259. https:\/\/doi.org\/10.1007\/S11042-024-19596-9","journal-title":"Multimed Tools Appl"},{"key":"7827_CR39","doi-asserted-by":"publisher","unstructured":"Min F, Guo S, Fan H, Dong J (2024) GaitMA: pose-guided multi-modal feature fusion for gait recognition. In: IEEE International Conference on Multimedia and Expo (ICME 2024), Niagara Falls, ON, Canada, July 15\u201319. IEEE, pp 1\u20136. https:\/\/doi.org\/10.1109\/ICME57554.2024.10688115","DOI":"10.1109\/ICME57554.2024.10688115"},{"key":"7827_CR40","doi-asserted-by":"crossref","unstructured":"Liu Y, Chen J, Gao Z, Li S (2024) CART-Gait: cross angle refined training of cross-view gait recognition. In: International Joint Conference on Neural Networks (IJCNN 2024), Yokohama, Japan, June 30\u2013July 5. IEEE, pp 1\u20138","DOI":"10.1109\/IJCNN60899.2024.10650831"},{"key":"7827_CR41","unstructured":"Yu S, Tan D, Tan T (2006) A framework for evaluating the effect of view angle, clothing and carrying condition on gait recognition. In: 18th International Conference on Pattern Recognition (ICPR \u201906), vol\u00a04. IEEE, pp 441\u2013444"},{"key":"7827_CR42","unstructured":"Zhu Z, Guo X, Yang T, Huang J, Deng J, Huang G, et\u00a0al (2021) Gait recognition in the wild: a benchmark. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp 14789\u201314799"},{"key":"7827_CR43","doi-asserted-by":"crossref","unstructured":"Finder SE, Amoyal R, Treister E, Freifeld O (2024) Wavelet convolutions for large receptive fields. In: Leonardis A, Ricci E, Roth S, Russakovsky O, Sattler T, Varol G (eds) Proceedings of the 18th European Conference on Computer Vision (ECCV 2024), Milan, Italy, September 29\u2013October 4, part LIV, vol 15112. Lecture Notes in Computer Science. Springer, Belin, pp 363\u2013380","DOI":"10.1007\/978-3-031-72949-2_21"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-025-07827-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-025-07827-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-025-07827-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,27]],"date-time":"2025-09-27T07:55:18Z","timestamp":1758959718000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-025-07827-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,27]]},"references-count":43,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2025,10]]}},"alternative-id":["7827"],"URL":"https:\/\/doi.org\/10.1007\/s11227-025-07827-1","relation":{},"ISSN":["1573-0484"],"issn-type":[{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,27]]},"assertion":[{"value":"11 April 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 September 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 September 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 competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"1387"}}