{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T07:27:24Z","timestamp":1740122844074,"version":"3.37.3"},"reference-count":44,"publisher":"Springer Science and Business Media LLC","issue":"16","license":[{"start":{"date-parts":[[2022,12,14]],"date-time":"2022-12-14T00:00:00Z","timestamp":1670976000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,12,14]],"date-time":"2022-12-14T00:00:00Z","timestamp":1670976000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2018AAA0100203"],"award-info":[{"award-number":["2018AAA0100203"]}]},{"name":"Joint Research Fund in Astronomy","award":["U2031136"],"award-info":[{"award-number":["U2031136"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2023,7]]},"DOI":"10.1007\/s11042-022-14087-1","type":"journal-article","created":{"date-parts":[[2022,12,14]],"date-time":"2022-12-14T20:04:48Z","timestamp":1671048288000},"page":"24865-24890","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A progressively-enhanced framework to broad networks for efficient recognition applications"],"prefix":"10.1007","volume":"82","author":[{"given":"Xiaoxuan","family":"Sun","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bo","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"RunDong","family":"Shi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0354-5490","authenticated-orcid":false,"given":"Qian","family":"Yin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ping","family":"Guo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,12,14]]},"reference":[{"issue":"1","key":"14087_CR1","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1109\/TNNLS.2017.2716952","volume":"29","author":"CLP Chen","year":"2018","unstructured":"Chen CLP, Liu Z (2018) Broad learning system: an effective and efficient incremental learning system without the need for deep architecture. IEEE Trans Neural Netw Learn Syst 29(1):10\u201324. https:\/\/doi.org\/10.1109\/TNNLS.2017.2716952","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"4","key":"14087_CR2","doi-asserted-by":"publisher","first-page":"1191","DOI":"10.1109\/TNNLS.2018.2866622","volume":"30","author":"CLP Chen","year":"2019","unstructured":"Chen CLP, Liu Z, Shuang F (2019) Universal approximation capability of broad learning system and its structural variations. IEEE Trans Neural Netw Learn Syst 30 (4):1191\u20131204. https:\/\/doi.org\/10.1109\/TNNLS.2018.2866622","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"14087_CR3","doi-asserted-by":"publisher","first-page":"236","DOI":"10.1016\/j.neucom.2021.01.120","volume":"442","author":"Y Chu","year":"2021","unstructured":"Chu Y, Lin H, Yang L, Sun S, Diao Y, Min C, Fan X, Shen C (2021) Hyperspectral image classification with discriminative manifold broad learning system. Neurocomputing 442:236\u2013248. https:\/\/doi.org\/10.1016\/j.neucom.2021.01.120","journal-title":"Neurocomputing"},{"key":"14087_CR4","doi-asserted-by":"publisher","first-page":"79,307","DOI":"10.1109\/ACCESS.2021.3084610","volume":"9","author":"W Ding","year":"2021","unstructured":"Ding W, Tian Y, Han S, Yuan H (2021) Greedy broad learning system. IEEE Access 9:79,307\u201379,315. https:\/\/doi.org\/10.1109\/ACCESS.2021.3084610","journal-title":"IEEE Access"},{"key":"14087_CR5","doi-asserted-by":"crossref","unstructured":"Duan R, Zhu J, Lu B (2013) .. In: 2013 6th International IEEE\/EMBS Conference on Neural Engineering (NER), pp 81\u201384","DOI":"10.1109\/NER.2013.6695876"},{"issue":"37-38","key":"14087_CR6","doi-asserted-by":"publisher","first-page":"27,057","DOI":"10.1007\/s11042-020-09354-y","volume":"79","author":"Q Gao","year":"2020","unstructured":"Gao Q, Wang C, Wang Z, Song X, Dong E, Song Y (2020) EEG Based emotion recognition using fusion feature extraction method. Multimed Tools Appl 79 (37-38):27,057\u201327,074. https:\/\/doi.org\/10.1007\/s11042-020-09354-y","journal-title":"Multimed Tools Appl"},{"issue":"4","key":"14087_CR7","doi-asserted-by":"publisher","first-page":"945","DOI":"10.1109\/TCDS.2020.2976112","volume":"13","author":"Z Gao","year":"2021","unstructured":"Gao Z, Wang X, Yang Y, Li Y, Ma K, Chen G (2021) A channel-fused dense convolutional network for EEG-based emotion recognition. IEEE Trans Cogn Develop Syst 13(4):945\u2013954. https:\/\/doi.org\/10.1109\/TCDS.2020.2976112","journal-title":"IEEE Trans Cogn Develop Syst"},{"key":"14087_CR8","doi-asserted-by":"publisher","unstructured":"Guo P (2020) On the structure evolutionary of the pseudoinverse learners in synergetic learning systems. Preprint researchgate.net. https:\/\/doi.org\/10.13140\/RG.2.2.12262.45121","DOI":"10.13140\/RG.2.2.12262.45121"},{"issue":"1","key":"14087_CR9","first-page":"71","volume":"32","author":"P Guo","year":"1996","unstructured":"Guo P, Chen CLP, Sun Y (1996) AHLN Algorithm: perfect learning through data representation. Journal of Beijing Normal University (Natural Science Edition) 32(1):71\u201375","journal-title":"Journal of Beijing Normal University (Natural Science Edition)"},{"key":"14087_CR10","unstructured":"Guo P, Yin Q (2020) Synergetic learning systems: concept, architecture, and algorithms. Preprint, arXiv, 01 2020"},{"issue":"9","key":"14087_CR11","doi-asserted-by":"publisher","first-page":"1809","DOI":"10.1109\/TKDE.2018.2866149","volume":"31","author":"M Han","year":"2019","unstructured":"Han M, Feng S, Chen CLP, Xu M, Qiu T (2019) Structured manifold broad learning system: a manifold perspective for large-scale chaotic time series analysis and prediction. IEEE Trans Knowl Data Eng 31(9):1809\u20131821. https:\/\/doi.org\/10.1109\/TKDE.2018.2866149","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"14087_CR12","doi-asserted-by":"publisher","unstructured":"He K, Zhang X, Ren S, Sun J (2016) .. In: 2016 IEEE conference on computer vision and pattern recognition, CVPR 2016, Las Vegas, NV, USA, June 27-30, 2016. https:\/\/doi.org\/10.1109\/CVPR.2016.90, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"issue":"7","key":"14087_CR13","doi-asserted-by":"publisher","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","volume":"18","author":"GE Hinton","year":"2006","unstructured":"Hinton GE, Osindero S, Teh YW (2006) A fast learning algorithm for deep belief nets. Neural Comput 18(7):1527\u20131554. https:\/\/doi.org\/10.1162\/neco.2006.18.7.1527","journal-title":"Neural Comput"},{"issue":"5786","key":"14087_CR14","doi-asserted-by":"publisher","first-page":"504","DOI":"10.1162\/neco.2006.18.7.1527","volume":"313","author":"GE Hinton","year":"2006","unstructured":"Hinton GE, Salakhutdinov RR (2006) Reducing the dimensionality of data with neural networks. Science 313(5786):504\u2013507. https:\/\/doi.org\/10.1162\/neco.2006.18.7.1527","journal-title":"Science"},{"key":"14087_CR15","doi-asserted-by":"publisher","first-page":"800","DOI":"10.1016\/j.ins.2021.06.008","volume":"576","author":"J Jin","year":"2021","unstructured":"Jin J, Li Y, Yang T, Zhao L, Duan J, Chen CLP (2021) Discriminative group-sparsity constrained broad learning system for visual recognition. Inf Sci 576:800\u2013818. https:\/\/doi.org\/10.1016\/j.ins.2021.06.008","journal-title":"Inf Sci"},{"issue":"11","key":"14087_CR16","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1007\/s11432-017-9421-3","volume":"112,209:1\u2013112,2","author":"J Jin","year":"2018","unstructured":"Jin J, Liu Z, Chen CLP (2018) Discriminative graph regularized broad learning system for image recognition. Science China(Information Sciences) 112,209:1\u2013112,209(11):14. https:\/\/doi.org\/10.1007\/s11432-017-9421-3","journal-title":"Science China(Information Sciences)"},{"key":"14087_CR17","doi-asserted-by":"publisher","unstructured":"Keshmiri S, Sumioka H, Nakanishi J, Ishiguro H (2017) .. In: 2017 international joint conference on neural networks, IJCNN 2017, Anchorage, AK, USA, May 14-19, 2017. https:\/\/doi.org\/10.1109\/IJCNN.2017.7966409, pp 4371\u20134378","DOI":"10.1109\/IJCNN.2017.7966409"},{"key":"14087_CR18","doi-asserted-by":"publisher","unstructured":"Kohonen T (2001) Self-Organizing Maps springer series in information sciences. Springer. https:\/\/doi.org\/10.1007\/978-3-642-56927-2","DOI":"10.1007\/978-3-642-56927-2"},{"issue":"5","key":"14087_CR19","doi-asserted-by":"publisher","first-page":"685","DOI":"10.3390\/rs10050685","volume":"10","author":"Y Kong","year":"2018","unstructured":"Kong Y, Wang X, Cheng Y, Chen CLP (2018) Hyperspectral imagery classification based on semi-supervised broad learning system. Remote Sens 10(5):685. https:\/\/doi.org\/10.3390\/rs10050685","journal-title":"Remote Sens"},{"issue":"7553","key":"14087_CR20","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 (2015) Deep learning. Nature 521(7553):436\u2013444","journal-title":"Nature"},{"issue":"2","key":"14087_CR21","doi-asserted-by":"publisher","first-page":"368","DOI":"10.1007\/s12559-017-9533-x","volume":"10","author":"J Li","year":"2018","unstructured":"Li J, Zhang Z, He H (2018) Hierarchical convolutional neural networks for EEG-based emotion recognition. Cognit Comput 10(2):368\u2013380. https:\/\/doi.org\/10.1007\/s12559-017-9533-x","journal-title":"Cognit Comput"},{"key":"14087_CR22","doi-asserted-by":"publisher","unstructured":"Li Y, Zheng W, Cui Z, Zhou X (2016) .. In: Neural information processing - 23rd international conference, ICONIP 2016, Kyoto, Japan, October 16-21, 2016, Proceedings, Part IV, vol 9950. https:\/\/doi.org\/10.1007\/978-3-319-46681-1_21, pp 175\u2013182","DOI":"10.1007\/978-3-319-46681-1_21"},{"issue":"8","key":"14087_CR23","doi-asserted-by":"publisher","first-page":"11,187","DOI":"10.1007\/s11042-022-12228-0","volume":"81","author":"C Liang","year":"2022","unstructured":"Liang C, Lao H, Wei T, Zhang X (2022) Alzheimer\u2019s disease classification from hippocampal atrophy based on pcanet-bls. Multimed Tools Appl 81(8):11,187\u201311,203. https:\/\/doi.org\/10.1007\/s11042-022-12228-0","journal-title":"Multimed Tools Appl"},{"issue":"1","key":"14087_CR24","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1109\/TSMC.2020.3043147","volume":"51","author":"Z Liu","year":"2021","unstructured":"Liu Z, Chen CLP, Feng S, Feng Q, Zhang T (2021) Stacked broad learning system: from incremental flatted structure to deep model. IEEE Trans Syst Man Cybern Syst 51(1):209\u2013222. https:\/\/doi.org\/10.1109\/TSMC.2020.3043147","journal-title":"IEEE Trans Syst Man Cybern Syst"},{"key":"14087_CR25","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1016\/j.neucom.2021.02.059","volume":"444","author":"Z Liu","year":"2021","unstructured":"Liu Z, Huang S, Jin W, Mu Y (2021) Broad learning system for semi-supervised learning. Neurocomputing 444:38\u201347. https:\/\/doi.org\/10.1016\/j.neucom.2021.02.059","journal-title":"Neurocomputing"},{"key":"14087_CR26","doi-asserted-by":"publisher","unstructured":"Liu W, Zheng W, Lu B (2016) .. In: Neural information processing - 23rd international conference, ICONIP 2016, Kyoto, Japan, October 16-21, 2016, Proceedings, Part II, vol 9948. https:\/\/doi.org\/10.1007\/978-3-319-46672-9_58, pp 521\u2013529","DOI":"10.1007\/978-3-319-46672-9_58"},{"key":"14087_CR27","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1016\/j.eswa.2017.09.062","volume":"93","author":"B Nakisa","year":"2018","unstructured":"Nakisa B, Rastgoo MN, Tjondronegoro D, Chandran V (2018) Evolutionary computation algorithms for feature selection of eeg-based emotion recognition using mobile sensors. Expert Syst Appl 93:143\u2013155. https:\/\/doi.org\/10.1016\/j.eswa.2017.09.062","journal-title":"Expert Syst Appl"},{"issue":"2","key":"14087_CR28","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1016\/0925-2312(94)90053-1","volume":"6","author":"Y Pao","year":"1994","unstructured":"Pao Y, Park GH, Sobajic DJ (1994) Learning and generalization characteristics of the random vector functional-link net. Neurocomputing 6(2):163\u2013180. https:\/\/doi.org\/10.1016\/0925-2312(94)90053-1","journal-title":"Neurocomputing"},{"key":"14087_CR29","unstructured":"Rifai S, Vincent P, Muller X, Glorot X, Bengio Y (2011) .. In: Proceedings of the 28th international conference on machine learning, ICML 2011, Bellevue, Washington, USA, June 28 - July 2, 2011. https:\/\/icml.cc\/2011\/papers\/455_icmlpaper.pdf, pp 833\u2013840"},{"issue":"5","key":"14087_CR30","doi-asserted-by":"publisher","first-page":"903","DOI":"10.1007\/s10994-017-5694-9","volume":"107","author":"D Sch\u00e4fer","year":"2018","unstructured":"Sch\u00e4fer D, H\u00fcllermeier E (2018) Dyad ranking using plackett-luce models based on joint feature representations. Mach Learn 107(5):903\u2013941. https:\/\/doi.org\/10.1007\/s10994-017-5694-9","journal-title":"Mach Learn"},{"key":"14087_CR31","first-page":"3371","volume":"11","author":"P Vincent","year":"2010","unstructured":"Vincent P, Larochelle H, Lajoie I, Bengio Y, Manzagol P (2010) Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J Mach Learn Res 11:3371\u20133408","journal-title":"J Mach Learn Res"},{"issue":"6","key":"14087_CR32","doi-asserted-by":"publisher","first-page":"3303","DOI":"10.1109\/TITS.2020.2980555","volume":"22","author":"K Wang","year":"2021","unstructured":"Wang K, Guo P (2021) An ensemble classification model with unsupervised representation learning for driving stress recognition using physiological signals. IEEE Trans Intell Transp Syst 22(6):3303\u20133315. https:\/\/doi.org\/10.1109\/TITS.2020.2980555","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"14087_CR33","doi-asserted-by":"crossref","unstructured":"Wang K, Guo P, Luo A (2017) A new automated spectral feature extraction method and its application in spectral classification and defective spectra recovery. Monthly Notices of the Royal Astronomical Society (4) 4311\u20134324","DOI":"10.1093\/mnras\/stw2894"},{"issue":"2","key":"14087_CR34","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1007\/s40747-020-00139-2","volume":"6","author":"R Xie","year":"2020","unstructured":"Xie R, Wang S (2020) Downsizing and enhancing broad learning systems by feature augmentation and residuals boosting. Complex Intell Syst 6(2):411\u2013429","journal-title":"Complex Intell Syst"},{"key":"14087_CR35","unstructured":"Xu B, Guo P (2018) .. In: IEEE international conference on systems, man, and cybernetics, SMC 2018, Miyazaki, Japan, October 7-10, 2018, pp 4243\u20134247"},{"issue":"9","key":"14087_CR36","doi-asserted-by":"publisher","first-page":"4450","DOI":"10.1109\/TCYB.2020.2978500","volume":"51","author":"H Ye","year":"2021","unstructured":"Ye H, Li H, Chen CLP (2021) Adaptive deep cascade broad learning system and its application in image denoising. IEEE Trans Cybern 51(9):4450\u20134463. https:\/\/doi.org\/10.1109\/TCYB.2020.2978500","journal-title":"IEEE Trans Cybern"},{"issue":"99","key":"14087_CR37","first-page":"1","volume":"PP","author":"Q Yin","year":"2021","unstructured":"Yin Q, Xu B, Zhou K, Guo P (2021) Bayesian pseudoinverse learners: from uncertainty to deterministic learning. IEEE Trans Cybern PP(99):1\u201312","journal-title":"IEEE Trans Cybern"},{"issue":"1","key":"14087_CR38","doi-asserted-by":"publisher","first-page":"334","DOI":"10.1109\/TSMC.2020.2995205","volume":"52","author":"L Zhang","year":"2022","unstructured":"Zhang L, Li J, Lu G, Shen P, Bennamoun M, Shah SAA, Miao Q, Zhu G, Li P, Lu X (2022) Analysis and variants of broad learning system. IEEE Trans Syst Man Cybern Syst 52(1):334\u2013344. https:\/\/doi.org\/10.1109\/TSMC.2020.2995205","journal-title":"IEEE Trans Syst Man Cybern Syst"},{"key":"14087_CR39","doi-asserted-by":"publisher","first-page":"1094","DOI":"10.1016\/j.ins.2015.09.025","volume":"367-368","author":"L Zhang","year":"2016","unstructured":"Zhang L, Suganthan PN (2016) A comprehensive evaluation of random vector functional link networks. Inf Sci 367-368:1094\u20131105. https:\/\/doi.org\/10.1016\/j.ins.2015.09.025","journal-title":"Inf Sci"},{"issue":"3","key":"14087_CR40","doi-asserted-by":"publisher","first-page":"839","DOI":"10.1109\/TCYB.2017.2788081","volume":"49","author":"T Zhang","year":"2019","unstructured":"Zhang T, Zheng W, Cui Z, Zong Y, Li Y (2019) Spatial-temporal recurrent neural network for emotion recognition. IEEE Trans Cybern 49 (3):839\u2013847. https:\/\/doi.org\/10.1109\/TCYB.2017.2788081","journal-title":"IEEE Trans Cybern"},{"issue":"1","key":"14087_CR41","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1016\/j.patcog.2005.08.002","volume":"39","author":"D Zhang","year":"2006","unstructured":"Zhang D, Zhou Z, Chen S (2006) Diagonal principal component analysis for face recognition. Pattern Recogn 39(1):140\u2013142. https:\/\/doi.org\/10.1016\/j.patcog.2005.08.002","journal-title":"Pattern Recogn"},{"issue":"3","key":"14087_CR42","doi-asserted-by":"publisher","first-page":"983","DOI":"10.1109\/TCSI.2019.2959886","volume":"67-I","author":"H Zhao","year":"2020","unstructured":"Zhao H, Zheng J, Deng W, Song Y (2020) Semi-supervised broad learning system based on manifold regularization and broad network. IEEE Trans Circuits Syst I: Regul Pap 67-I(3):983\u2013994. https:\/\/doi.org\/10.1109\/TCSI.2019.2959886","journal-title":"IEEE Trans Circuits Syst I: Regul Pap"},{"issue":"3","key":"14087_CR43","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1109\/TCDS.2016.2587290","volume":"9","author":"W Zheng","year":"2017","unstructured":"Zheng W (2017) Multichannel EEG-based emotion recognition via group sparse canonical correlation analysis. IEEE Trans Cogn Develop Syst 9(3):281\u2013290. https:\/\/doi.org\/10.1109\/TCDS.2016.2587290","journal-title":"IEEE Trans Cogn Develop Syst"},{"issue":"3","key":"14087_CR44","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1109\/TAMD.2015.2431497","volume":"7","author":"W Zheng","year":"2015","unstructured":"Zheng W, Lu B (2015) Investigating critical frequency bands and channels for EEG-based emotion recognition with deep neural networks. IEEE Trans Auton Ment Dev 7(3):162\u2013175. https:\/\/doi.org\/10.1109\/TAMD.2015.2431497","journal-title":"IEEE Trans Auton Ment Dev"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-022-14087-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-022-14087-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-022-14087-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,23]],"date-time":"2023-06-23T18:39:59Z","timestamp":1687545599000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-022-14087-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,14]]},"references-count":44,"journal-issue":{"issue":"16","published-print":{"date-parts":[[2023,7]]}},"alternative-id":["14087"],"URL":"https:\/\/doi.org\/10.1007\/s11042-022-14087-1","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"type":"print","value":"1380-7501"},{"type":"electronic","value":"1573-7721"}],"subject":[],"published":{"date-parts":[[2022,12,14]]},"assertion":[{"value":"27 May 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 August 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 October 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 December 2022","order":4,"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":"<!--Emphasis Type='Bold' removed-->Conflict of Interests"}}]}}