{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T17:27:47Z","timestamp":1774718867682,"version":"3.50.1"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"24","license":[{"start":{"date-parts":[[2025,7,2]],"date-time":"2025-07-02T00:00:00Z","timestamp":1751414400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,7,2]],"date-time":"2025-07-02T00:00:00Z","timestamp":1751414400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2025,8]]},"DOI":"10.1007\/s00521-025-11422-z","type":"journal-article","created":{"date-parts":[[2025,7,2]],"date-time":"2025-07-02T06:15:24Z","timestamp":1751436924000},"page":"19751-19772","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Accurate multi-phase unsupervised and supervised approach to fault detection in power transmission networks"],"prefix":"10.1007","volume":"37","author":[{"given":"Nermeen G.","family":"Rezk","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3340-4031","authenticated-orcid":false,"given":"Ragab A.","family":"El-Sehiemy","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abdel-Fatah","family":"Attia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Heba","family":"El-Behery","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,7,2]]},"reference":[{"key":"11422_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00202-024-02334-7","volume":"106","author":"A Deb","year":"2024","unstructured":"Deb A, Jain AK (2024) An effective data-driven machine learning hybrid approach for fault detection and classification in a standalone low-voltage DC microgrid. Electr Eng 106:1\u201314. https:\/\/doi.org\/10.1007\/s00202-024-02334-7","journal-title":"Electr Eng"},{"key":"11422_CR2","doi-asserted-by":"publisher","DOI":"10.2139\/ssrn.4570914","volume":"226","author":"S Roy","year":"2024","unstructured":"Roy S, Debnath S (2024) A wavelet-based PSD approach for fault detection and classification in grid connected inverter interfaced microgrid. Measurement 226:114079. https:\/\/doi.org\/10.2139\/ssrn.4570914","journal-title":"Measurement"},{"issue":"1","key":"11422_CR3","doi-asserted-by":"publisher","first-page":"154","DOI":"10.3390\/electricity5010009","volume":"5","author":"Y Taleb","year":"2024","unstructured":"Taleb Y, Lamrani R, Abbou A (2024) Measurement and evaluation of voltage unbalance in 2\u00d7 25 kV 50 Hz high-speed trains using variable integration period. Electricity 5(1):154\u2013173. https:\/\/doi.org\/10.3390\/electricity5010009","journal-title":"Electricity"},{"issue":"2","key":"11422_CR4","doi-asserted-by":"publisher","first-page":"774","DOI":"10.3390\/app11020774","volume":"11","author":"AS Abbas","year":"2021","unstructured":"Abbas AS, El-Sehiemy RA, Abou El-Ela A, Ali ES, Mahmoud K, Lehtonen M, Darwish MM (2021) Optimal harmonic mitigation in distribution systems with inverter based distributed generation. Appl Sci 11(2):774. https:\/\/doi.org\/10.3390\/app11020774","journal-title":"Appl Sci"},{"key":"11422_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.epsr.2022.108757","volume":"213","author":"MA Gabr","year":"2022","unstructured":"Gabr MA, El-Sehiemy RA, Megahed TF, Ebihara Y, Abdelkader SM (2022) Optimal settings of multiple inverter-based distributed generation for restoring coordination of DOCRs in mesh distribution networks. Electric Power Syst Res 213:108757. https:\/\/doi.org\/10.1016\/j.epsr.2022.108757","journal-title":"Electric Power Syst Res"},{"key":"11422_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijepes.2021.107102","volume":"133","author":"SR Fahim","year":"2021","unstructured":"Fahim SR, Sarker SK, Muyeen SM, Das SK, Kamwa I (2021) A deep learning based intelligent approach in detection and classification of transmission line faults. Int J Electr Power Energy Syst 133:107102","journal-title":"Int J Electr Power Energy Syst"},{"key":"11422_CR7","doi-asserted-by":"publisher","first-page":"2397","DOI":"10.1109\/TII.2023.3245678","volume":"20","author":"A Ahmed","year":"2023","unstructured":"Ahmed A et al (2023) Spatio-temporal deep graph network for event detection, localization and classification in cyber-physical electric distribution system. IEEE Trans Ind Inf 20:2397. https:\/\/doi.org\/10.1109\/TII.2023.3245678","journal-title":"IEEE Trans Ind Inf"},{"key":"11422_CR8","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1016\/j.measurement.2018.12.030","volume":"136","author":"MI Zaki","year":"2019","unstructured":"Zaki MI et al (2019) An investigated reactive power measurements-based fault-identification scheme for teed transmission lines. Measurement 136:185\u2013200. https:\/\/doi.org\/10.1016\/j.measurement.2018.12.030","journal-title":"Measurement"},{"issue":"15","key":"11422_CR9","doi-asserted-by":"publisher","first-page":"3252","DOI":"10.1049\/iet-gtd.2018.5016","volume":"13","author":"MI Zaki","year":"2019","unstructured":"Zaki MI et al (2019) Sensitive\/stable complementary fault identification scheme for overhead transmission lines. IET Gener Transm Distrib 13(15):3252\u20133263. https:\/\/doi.org\/10.1049\/iet-gtd.2018.5016","journal-title":"IET Gener Transm Distrib"},{"key":"11422_CR10","doi-asserted-by":"publisher","first-page":"5799","DOI":"10.1007\/s10462-022-10296-0","volume":"56","author":"FM Shakiba","year":"2023","unstructured":"Shakiba FM, Azizi SM, Zhou M et al (2023) Application of machine learning methods in fault detection and classification of power transmission lines: a survey. Artif Intell Rev 56:5799\u20135836. https:\/\/doi.org\/10.1007\/s10462-022-10296-0","journal-title":"Artif Intell Rev"},{"key":"11422_CR11","doi-asserted-by":"publisher","first-page":"112346","DOI":"10.1016\/j.measurement.2022.112346","volume":"206","author":"Z Zhu","year":"2023","unstructured":"Zhu Z et al (2023) A review of the application of deep learning in intelligent fault diagnosis of rotating machinery. Measurement 206:112346. https:\/\/doi.org\/10.1016\/j.measurement.2022.112346","journal-title":"Measurement"},{"key":"11422_CR12","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-025-11035-6","author":"M Abdelsattar","year":"2025","unstructured":"Abdelsattar M, AbdelMoety A, Emad-Eldeen A (2025) Advanced machine learning techniques for predicting power generation and fault detection in solar photovoltaic systems. Neural Comput Appl. https:\/\/doi.org\/10.1007\/s00521-025-11035-6","journal-title":"Neural Comput Appl"},{"issue":"5","key":"11422_CR13","doi-asserted-by":"publisher","first-page":"2406","DOI":"10.3390\/app15052406","volume":"15","author":"NS Maseko","year":"2025","unstructured":"Maseko NS, Thango BA, Mabunda N (2025) Fault detection in power transformers using frequency response analysis and machine learning models. Appl Sci 15(5):2406. https:\/\/doi.org\/10.3390\/app15052406","journal-title":"Appl Sci"},{"key":"11422_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.aej.2025.01.027","author":"HM El-Arwash","year":"2025","unstructured":"El-Arwash HM, Gafar M, El-Sehiemy RA (2025) Accurate harmonics mitigation for water desalination plant supplied by multi-energy sources based on deep learning algorithms. Alex Eng J. https:\/\/doi.org\/10.1016\/j.aej.2025.01.027","journal-title":"Alex Eng J"},{"key":"11422_CR15","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2024.1234567","author":"AM Abed","year":"2024","unstructured":"Abed AM, El-Sehiemy RA, Touati BB, El Arwash HM (2024) Accurate identification of harmonic distortion for micro-grids using artificial intelligence-based predictive models. IEEE Access. https:\/\/doi.org\/10.1109\/ACCESS.2024.1234567","journal-title":"IEEE Access"},{"issue":"5","key":"11422_CR16","doi-asserted-by":"publisher","first-page":"8165","DOI":"10.1109\/jsen.2025.3528034","volume":"25","author":"CM Leung","year":"2025","unstructured":"Leung CM, Lin S, Yang J (2025) Non-contact fault classification in three-core cables using random forest with magnetic field analysis. IEEE Sens J 25(5):8165\u20138174. https:\/\/doi.org\/10.1109\/jsen.2025.3528034","journal-title":"IEEE Sens J"},{"issue":"1","key":"11422_CR17","doi-asserted-by":"publisher","first-page":"20","DOI":"10.3390\/eng6010020","volume":"6","author":"K Chahine","year":"2025","unstructured":"Chahine K (2025) Tree-based algorithms and incremental feature optimization for fault detection and diagnosis in photovoltaic systems. Eng 6(1):20. https:\/\/doi.org\/10.3390\/eng6010020","journal-title":"Eng"},{"key":"11422_CR18","doi-asserted-by":"publisher","first-page":"116936","DOI":"10.1016\/j.measurement.2025.116936","volume":"248","author":"Z Guo","year":"2025","unstructured":"Guo Z et al (2025) Fault diagnosis of rotating machinery with high-dimensional imbalance samples based on wavelet random forest. Measurement 248:116936. https:\/\/doi.org\/10.1016\/j.measurement.2025.116936","journal-title":"Measurement"},{"key":"11422_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2024.109503","volume":"131","author":"H Noura","year":"2024","unstructured":"Noura H et al (2024) Explainable artificial intelligence of tree-based algorithms for fault detection and diagnosis in grid-connected photovoltaic systems. Eng Appl Artif Intell 131:109503. https:\/\/doi.org\/10.1016\/j.engappai.2024.109503","journal-title":"Eng Appl Artif Intell"},{"key":"11422_CR20","doi-asserted-by":"publisher","first-page":"9672","DOI":"10.1038\/s41598-025-93514-3","volume":"15","author":"A Anwarsha","year":"2025","unstructured":"Anwarsha A, Babu TN (2025) Fault detection of taper roller bearings using tunable Q-factor wavelet transform and fault classification using long\u2013short-term memory network. Sci Rep 15:9672. https:\/\/doi.org\/10.1038\/s41598-025-93514-3","journal-title":"Sci Rep"},{"key":"11422_CR21","doi-asserted-by":"publisher","first-page":"1443","DOI":"10.3390\/electronics12061443","volume":"12","author":"C Suo","year":"2023","unstructured":"Suo C et al (2023) Non-contact measurement method of phase current based on magnetic field decoupling calculation for three-phase four-core cable. Electronics 12:1443","journal-title":"Electronics"},{"key":"11422_CR22","doi-asserted-by":"publisher","unstructured":"Jingzhong F, et al. (2024) \"Special Issue on Artificial Intelligence for Machinery Diagnostics and Prognostics. Engineering Applications of Artificial Intelligence 131 109700 https:\/\/doi.org\/10.1016\/j.engappai.2023.109700","DOI":"10.1016\/j.engappai.2023.109700"},{"issue":"4","key":"11422_CR23","doi-asserted-by":"publisher","first-page":"1965","DOI":"10.1007\/s10845-021-01884-y","volume":"34","author":"K Zhou","year":"2023","unstructured":"Zhou K et al (2023) Deep graph feature learning-based diagnosis approach for rotating machinery using multi-sensor data. J Intell Manuf 34(4):1965\u20131974. https:\/\/doi.org\/10.1007\/s10845-021-01884-y","journal-title":"J Intell Manuf"},{"issue":"2","key":"11422_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.asej.2023.102427","volume":"15","author":"PK Shukla","year":"2024","unstructured":"Shukla PK, Deepa K (2024) Deep learning techniques for transmission line fault classification\u2013A comparative study. Ain Shams Eng J 15(2):102427. https:\/\/doi.org\/10.1016\/j.asej.2023.102427","journal-title":"Ain Shams Eng J"},{"key":"11422_CR25","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2024.3384761","author":"S Kanwal","year":"2024","unstructured":"Kanwal S, Jiriwibhakorn S (2024) Advanced fault detection, classification, and localization in transmission lines: a comparative study of ANFIS, neural networks, and hybrid methods. IEEE Access. https:\/\/doi.org\/10.1109\/ACCESS.2024.3384761","journal-title":"IEEE Access"},{"key":"11422_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2022.108107","volume":"101","author":"IA Fran\u00e7a","year":"2022","unstructured":"Fran\u00e7a IA et al (2022) A machine learning-based approach for comprehensive fault diagnosis in transmission lines. Comput Electr Eng 101:108107. https:\/\/doi.org\/10.1016\/j.compeleceng.2022.108107","journal-title":"Comput Electr Eng"},{"issue":"15","key":"11422_CR27","doi-asserted-by":"publisher","first-page":"5475","DOI":"10.3390\/en15155475","volume":"15","author":"K Al Kharusi","year":"2022","unstructured":"Al Kharusi K, El Haffar A, Mesbah M (2022) Fault detection and classification in transmission lines connected to inverter-based generators using machine learning. Energies 15(15):5475","journal-title":"Energies"},{"key":"11422_CR28","doi-asserted-by":"publisher","unstructured":"Rafi MAI et al. (2024). Enhancing power transmission line fault detection with a hybrid ANN-SVM machine learning model: a comparative study. 2024 International conference on advances in computing, communication, electrical, and smart systems (iCACCESS). https:\/\/doi.org\/10.1109\/iCACCESS.2024.1234567","DOI":"10.1109\/iCACCESS.2024.1234567"},{"issue":"1","key":"11422_CR29","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1007\/s44196-023-00049-6","volume":"17","author":"M Najafzadeh","year":"2024","unstructured":"Najafzadeh M et al (2024) Fault detection, classification and localization along the power grid line using optimized machine learning algorithms. Int J Comput Intell Syst 17(1):49. https:\/\/doi.org\/10.1007\/s44196-023-00049-6","journal-title":"Int J Comput Intell Syst"},{"issue":"9","key":"11422_CR30","doi-asserted-by":"publisher","first-page":"2169","DOI":"10.3390\/en17092169","volume":"17","author":"B Palma","year":"2024","unstructured":"Palma B (2024) Hybrid approach for detection and diagnosis of short-circuit faults in power transmission lines. Energies 17(9):2169. https:\/\/doi.org\/10.3390\/en17092169","journal-title":"Energies"},{"issue":"1","key":"11422_CR31","doi-asserted-by":"publisher","first-page":"791","DOI":"10.21608\/erurj.2024.222668.1055","volume":"3","author":"F Wadie","year":"2024","unstructured":"Wadie F (2024) Smart fault detection algorithm for transmission networks. ERU Res J 3(1):791\u2013803. https:\/\/doi.org\/10.21608\/erurj.2024.222668.1055","journal-title":"ERU Res J"},{"key":"11422_CR32","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2024.3381713","author":"W Ma","year":"2024","unstructured":"Ma W et al (2024) Transmission tower and power line detection based on improved Solov2. IEEE Trans Instrum Meas. https:\/\/doi.org\/10.1109\/TIM.2024.3381713","journal-title":"IEEE Trans Instrum Meas"},{"issue":"12","key":"11422_CR33","doi-asserted-by":"publisher","first-page":"36419","DOI":"10.1007\/s11042-023-15789-0","volume":"83","author":"T Su","year":"2024","unstructured":"Su T, Liu D (2024) Transmission line defect detection based on feature enhancement. Multimed Tools Appl 83(12):36419\u201336431. https:\/\/doi.org\/10.1007\/s11042-023-15789-0","journal-title":"Multimed Tools Appl"},{"key":"11422_CR34","doi-asserted-by":"publisher","first-page":"109634","DOI":"10.1016\/j.ijepes.2023.109634","volume":"155","author":"HA Abd el-Ghany","year":"2024","unstructured":"Abd el-Ghany HA, Soliman IA, Ahmed ES, ElGebaly AE (2024) Generalized wide-area fault detection and location algorithm for transmission system based on optimal PMUs allocation. Int J Elect Power & Energy Syst 155:109634","journal-title":"Int J Elect Power & Energy Syst"},{"issue":"2","key":"11422_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.asej.2023.102369","volume":"15","author":"EA Hailu","year":"2024","unstructured":"Hailu EA, Nyakoe GN, Muriithi CM (2024) Data-driven approach to fuzzy inference system tuning for static security assessment of multi-area power systems with renewable energy. Ain Shams Eng J 15(2):102369. https:\/\/doi.org\/10.1016\/j.asej.2023.102369","journal-title":"Ain Shams Eng J"},{"issue":"2","key":"11422_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.asej.2023.102454","volume":"15","author":"OM Salim","year":"2024","unstructured":"Salim OM, Aboraya A-S (2024) Power quality enhancement of unbalanced Distributed-Resources based on triple-loop compensation. Ain Shams Eng J 15(2):102454. https:\/\/doi.org\/10.1016\/j.asej.2023.102454","journal-title":"Ain Shams Eng J"},{"key":"11422_CR37","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijepes.2024.109771","volume":"156","author":"CC Ukwuoma","year":"2024","unstructured":"Ukwuoma CC et al (2024) Power transmission system\u2019s fault location, detection, and classification: pay close attention to transmission nodes. Int J Electr Power Energy Syst 156:109771. https:\/\/doi.org\/10.1016\/j.ijepes.2024.109771","journal-title":"Int J Electr Power Energy Syst"},{"issue":"2","key":"11422_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.asej.2023.102421","volume":"15","author":"SI Ali","year":"2024","unstructured":"Ali SI et al (2024) Risk prioritization in a core preparation experiment using fuzzy VIKOR integrated with Shannon entropy method. Ain Shams Eng J 15(2):102421. https:\/\/doi.org\/10.1016\/j.asej.2023.102421","journal-title":"Ain Shams Eng J"},{"key":"11422_CR39","unstructured":"Kaggle Dataset: Electrical Fault Detection and Classification Note."},{"key":"11422_CR40","doi-asserted-by":"publisher","first-page":"113499","DOI":"10.1109\/ACCESS.2019.2934938","volume":"7","author":"JC Arouche Freire","year":"2019","unstructured":"Arouche Freire JC, Garcez Castro AR, Homci MS, Meiguins BS, De Morais JM (2019) Transmission line fault classification using hidden markov models. IEEE Access 7:113499\u2013113510. https:\/\/doi.org\/10.1109\/ACCESS.2019.2934938","journal-title":"IEEE Access"},{"key":"11422_CR41","doi-asserted-by":"publisher","unstructured":"Khan, Arshad. (2024). Data visualization. In Visual Analytics for Dashboards: A step-by-step guide to principles and practical techniques (pp. 67\u201373). Apress. https:\/\/doi.org\/10.1007\/978-1-4842-1234-5_6","DOI":"10.1007\/978-1-4842-1234-5_6"},{"issue":"13","key":"11422_CR42","doi-asserted-by":"publisher","first-page":"6955","DOI":"10.1007\/s00521-023-08234-6","volume":"36","author":"AM Eid","year":"2024","unstructured":"Eid AM et al (2024) Comparative study of ML models for IIoT intrusion detection: impact of data preprocessing and balancing. Neural Comput Appl 36(13):6955\u20136972. https:\/\/doi.org\/10.1007\/s00521-023-08234-6","journal-title":"Neural Comput Appl"},{"issue":"1","key":"11422_CR43","doi-asserted-by":"publisher","first-page":"29","DOI":"10.59469\/pfj.2024.8","volume":"1","author":"S Larson","year":"2024","unstructured":"Larson S, Overton M (2024) Modeling approach matters, but not as much as preprocessing: Comparison of machine learning and traditional revenue forecasting techniques. Public Finance J 1(1):29\u201348. https:\/\/doi.org\/10.59469\/pfj.2024.8","journal-title":"Public Finance J"},{"key":"11422_CR44","first-page":"353","volume":"47","author":"TM Hakimovich","year":"2024","unstructured":"Hakimovich TM (2024) The system of key performance indicators as A tool improvements management of the organization. Gospodarka i Innowacje 47:353\u2013358","journal-title":"Gospodarka i Innowacje"},{"key":"11422_CR45","doi-asserted-by":"publisher","first-page":"107357","DOI":"10.1016\/j.engappai.2023.107357","volume":"127","author":"Z Mian","year":"2024","unstructured":"Mian Z et al (2024) A literature review of fault diagnosis based on ensemble learning. Eng Appl Artif Intell 127:107357. https:\/\/doi.org\/10.1016\/j.engappai.2023.107357","journal-title":"Eng Appl Artif Intell"},{"issue":"7","key":"11422_CR46","first-page":"41","volume":"10","author":"AM Ehoedy","year":"2023","unstructured":"Ehoedy AM, Mohamed AMK, Elahiwel AAM (2023) Detection and identification of electrical faults using random forest classification. Open Access Repos 10(7):41\u201348","journal-title":"Open Access Repos"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11422-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-025-11422-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11422-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,7]],"date-time":"2025-09-07T00:25:59Z","timestamp":1757204759000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-025-11422-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,2]]},"references-count":46,"journal-issue":{"issue":"24","published-print":{"date-parts":[[2025,8]]}},"alternative-id":["11422"],"URL":"https:\/\/doi.org\/10.1007\/s00521-025-11422-z","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,2]]},"assertion":[{"value":"16 September 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 May 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 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":"All authors have no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}