{"id":"https://openalex.org/W4386602406","doi":"https://doi.org/10.3390/e25091316","title":"Prediction of Contact Fatigue Performance Degradation Trends Based on Multi-Domain Features and Temporal Convolutional Networks","display_name":"Prediction of Contact Fatigue Performance Degradation Trends Based on Multi-Domain Features and Temporal Convolutional Networks","publication_year":2023,"publication_date":"2023-09-09","ids":{"openalex":"https://openalex.org/W4386602406","doi":"https://doi.org/10.3390/e25091316","pmid":"https://pubmed.ncbi.nlm.nih.gov/37761615"},"language":"en","primary_location":{"id":"doi:10.3390/e25091316","is_oa":true,"landing_page_url":"https://doi.org/10.3390/e25091316","pdf_url":"https://www.mdpi.com/1099-4300/25/9/1316/pdf?version=1694396067","source":{"id":"https://openalex.org/S195231649","display_name":"Entropy","issn_l":"1099-4300","issn":["1099-4300"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/1099-4300/25/9/1316/pdf?version=1694396067","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5114546997","display_name":"Yu Liu","orcid":"https://orcid.org/0009-0009-7947-157X"},"institutions":[{"id":"https://openalex.org/I50632499","display_name":"Chongqing University of Technology","ror":"https://ror.org/04vgbd477","country_code":"CN","type":"education","lineage":["https://openalex.org/I50632499"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yu Liu","raw_affiliation_strings":["College of Mechanical Engineering, Chongqing University of Technology, Chongqing 400054, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Mechanical Engineering, Chongqing University of Technology, Chongqing 400054, China","institution_ids":["https://openalex.org/I50632499"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048105342","display_name":"Yuanbo Liu","orcid":"https://orcid.org/0000-0002-8780-927X"},"institutions":[{"id":"https://openalex.org/I50632499","display_name":"Chongqing University of Technology","ror":"https://ror.org/04vgbd477","country_code":"CN","type":"education","lineage":["https://openalex.org/I50632499"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuanbo Liu","raw_affiliation_strings":["College of Mechanical Engineering, Chongqing University of Technology, Chongqing 400054, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Mechanical Engineering, Chongqing University of Technology, Chongqing 400054, China","institution_ids":["https://openalex.org/I50632499"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101785878","display_name":"Yan Yang","orcid":"https://orcid.org/0000-0001-6227-5235"},"institutions":[{"id":"https://openalex.org/I50632499","display_name":"Chongqing University of Technology","ror":"https://ror.org/04vgbd477","country_code":"CN","type":"education","lineage":["https://openalex.org/I50632499"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yan Yang","raw_affiliation_strings":["College of Mechanical Engineering, Chongqing University of Technology, Chongqing 400054, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Mechanical Engineering, Chongqing University of Technology, Chongqing 400054, China","institution_ids":["https://openalex.org/I50632499"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5101785878"],"corresponding_institution_ids":["https://openalex.org/I50632499"],"apc_list":{"value":2000,"currency":"CHF","value_usd":2165},"apc_paid":{"value":2000,"currency":"CHF","value_usd":2165},"fwci":0.4907,"has_fulltext":true,"cited_by_count":3,"citation_normalized_percentile":{"value":0.6375228,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"25","issue":"9","first_page":"1316","last_page":"1316"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9986000061035156,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9986000061035156,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11062","display_name":"Gear and Bearing Dynamics Analysis","score":0.9954000115394592,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13213","display_name":"Mechanical Failure Analysis and Simulation","score":0.9927999973297119,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.6911330819129944},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.658585786819458},{"id":"https://openalex.org/keywords/degradation","display_name":"Degradation (telecommunications)","score":0.567448616027832},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.549419641494751},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5372578501701355},{"id":"https://openalex.org/keywords/time-domain","display_name":"Time domain","score":0.517498254776001},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5113093852996826},{"id":"https://openalex.org/keywords/kernel-principal-component-analysis","display_name":"Kernel principal component analysis","score":0.5007147789001465},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.48035869002342224},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.46802598237991333},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.45963436365127563},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.44103720784187317},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.43980419635772705},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.42884135246276855},{"id":"https://openalex.org/keywords/frequency-domain","display_name":"Frequency domain","score":0.4182095527648926},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.3309873342514038},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.1597450077533722},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.13517406582832336},{"id":"https://openalex.org/keywords/kernel-method","display_name":"Kernel method","score":0.10420531034469604},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.07206356525421143}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.6911330819129944},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.658585786819458},{"id":"https://openalex.org/C2779679103","wikidata":"https://www.wikidata.org/wiki/Q5251805","display_name":"Degradation (telecommunications)","level":2,"score":0.567448616027832},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.549419641494751},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5372578501701355},{"id":"https://openalex.org/C103824480","wikidata":"https://www.wikidata.org/wiki/Q185889","display_name":"Time domain","level":2,"score":0.517498254776001},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5113093852996826},{"id":"https://openalex.org/C182335926","wikidata":"https://www.wikidata.org/wiki/Q17093020","display_name":"Kernel principal component analysis","level":4,"score":0.5007147789001465},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48035869002342224},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.46802598237991333},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.45963436365127563},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.44103720784187317},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.43980419635772705},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.42884135246276855},{"id":"https://openalex.org/C19118579","wikidata":"https://www.wikidata.org/wiki/Q786423","display_name":"Frequency domain","level":2,"score":0.4182095527648926},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3309873342514038},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.1597450077533722},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13517406582832336},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.10420531034469604},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.07206356525421143},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":5,"locations":[{"id":"doi:10.3390/e25091316","is_oa":true,"landing_page_url":"https://doi.org/10.3390/e25091316","pdf_url":"https://www.mdpi.com/1099-4300/25/9/1316/pdf?version=1694396067","source":{"id":"https://openalex.org/S195231649","display_name":"Entropy","issn_l":"1099-4300","issn":["1099-4300"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy","raw_type":"journal-article"},{"id":"pmid:37761615","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/37761615","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy (Basel, Switzerland)","raw_type":null},{"id":"pmh:oai:pubmedcentral.nih.gov:10527696","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/10527696","pdf_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC10527696/pdf/entropy-25-01316.pdf","source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Entropy (Basel)","raw_type":"Text"},{"id":"pmh:oai:doaj.org/article:359040aff2754f5fb3b921e464c19403","is_oa":true,"landing_page_url":"https://doaj.org/article/359040aff2754f5fb3b921e464c19403","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Entropy, Vol 25, Iss 9, p 1316 (2023)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/1099-4300/25/9/1316/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/e25091316","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Entropy","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/e25091316","is_oa":true,"landing_page_url":"https://doi.org/10.3390/e25091316","pdf_url":"https://www.mdpi.com/1099-4300/25/9/1316/pdf?version=1694396067","source":{"id":"https://openalex.org/S195231649","display_name":"Entropy","issn_l":"1099-4300","issn":["1099-4300"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.41999998688697815,"id":"https://metadata.un.org/sdg/9"}],"awards":[{"id":"https://openalex.org/G2920164138","display_name":null,"funder_award_id":"52075062","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7092672930","display_name":null,"funder_award_id":"No.52075062","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4386602406.pdf"},"referenced_works_count":15,"referenced_works":["https://openalex.org/W1587559447","https://openalex.org/W1994505190","https://openalex.org/W2317595875","https://openalex.org/W2331534005","https://openalex.org/W2404692435","https://openalex.org/W2741768983","https://openalex.org/W2744686084","https://openalex.org/W2773549135","https://openalex.org/W2790565135","https://openalex.org/W2792764867","https://openalex.org/W2883187628","https://openalex.org/W2908441554","https://openalex.org/W2977718750","https://openalex.org/W3121872783","https://openalex.org/W3211623766"],"related_works":["https://openalex.org/W4310873165","https://openalex.org/W2379488555","https://openalex.org/W2753886092","https://openalex.org/W2152632846","https://openalex.org/W1992961908","https://openalex.org/W2014683590","https://openalex.org/W2944973397","https://openalex.org/W3138125914","https://openalex.org/W2359742711","https://openalex.org/W2139392257"],"abstract_inverted_index":{"Contact":[0],"fatigue":[1,23,191,251,291],"is":[2,29,44,231],"one":[3],"of":[4,10,21,35,41,45,83,166,213,224,238,248,288,332,356],"the":[5,32,56,106,135,147,152,164,167,201,214,217,226,239,245,260,285,309,340,352],"most":[6],"common":[7],"failure":[8],"forms":[9],"typical":[11],"basic":[12],"components":[13,28],"such":[14],"as":[15],"bearings":[16],"and":[17,38,69,79,88,95,122,146,178,210,233,272,304,317,324,336,346],"gears.":[18],"Accurate":[19],"prediction":[20,60,63,139,150,187,295,319,330],"contact":[22,190,250,290],"performance":[24,57,89,136,184,286,316,353],"degradation":[25,58,90,137,148,185,246,287,354],"trends":[26,97],"in":[27,216],"conducive":[30],"to":[31,54,115,183,207,350],"scientific":[33],"formulation":[34],"maintenance":[36],"strategies":[37],"health":[39,124],"management":[40],"equipment,":[42],"which":[43],"great":[46],"significance":[47,345],"for":[48,151,188,265,328],"industrial":[49],"production.":[50],"In":[51],"this":[52,162,254],"paper,":[53],"realize":[55],"trend":[59,138,149,186,355],"accurately,":[61],"a":[62,77,129,173,329],"method":[64,112,169,223,255,342],"based":[65,127,141,228,279,297,311],"on":[66,128,142,229,280,298,312],"multi-domain":[67,78,120],"features":[68,121],"temporal":[70],"convolutional":[71,130],"networks":[72,271,303],"(TCNs)":[73],"was":[74,86,113,144,155,170,180],"proposed.":[75],"Firstly,":[76],"high-dimensional":[80],"feature":[81,202,220],"set":[82,203],"vibration":[84],"signals":[85],"constructed,":[87,145],"indexes":[91,125],"with":[92,294],"good":[93],"sensitivity":[94],"strong":[96],"were":[98],"initially":[99],"screened":[100],"using":[101,157,172,197],"comprehensive":[102],"evaluation":[103],"indexes.":[104],"Secondly,":[105],"kernel":[107],"principal":[108],"component":[109],"analysis":[110],"(KPCA)":[111],"used":[114,263],"eliminate":[116],"redundant":[117],"information":[118,215],"among":[119],"construct":[123],"(HIs)":[126],"autoencoder":[131],"(CAE)":[132],"network.":[133],"Then,":[134],"model":[140,278,310],"TCN":[143,281,313],"monitored":[153],"object":[154],"realized":[156],"direct":[158],"multi-step":[159],"prediction.":[160],"On":[161],"basis,":[163],"effectiveness":[165],"proposed":[168,341],"verified":[171],"bearing":[174],"common-use":[175],"data":[176],"set,":[177],"it":[179],"successfully":[181],"applied":[182,349],"rolling":[189,249,289],"specimens.":[192,292],"The":[193,222,277,321],"results":[194],"show":[195],"that":[196],"KPCA":[198],"can":[199,242,282,347],"reduce":[200],"from":[204],"14":[205],"dimensions":[206,209],"4":[208],"retain":[211],"98.33%":[212],"original":[218],"preferred":[219],"set.":[221],"constructing":[225,266],"HI":[227,241],"CAE":[230],"effective,":[232],"change":[234],"processes":[235],"versus":[236],"time":[237],"constructed":[240],"truly":[243],"reflect":[244],"process":[247],"specimen":[252],"performance;":[253],"has":[256,314,343],"obvious":[257],"advantages":[258],"over":[259],"two":[261],"commonly":[262],"methods":[264],"HIs":[267],"including":[268],"auto-encoding":[269],"(AE)":[270],"gaussian":[273],"mixture":[274],"models":[275,296],"(GMMs).":[276],"accurately":[283],"predict":[284,351],"Compared":[293],"long":[299],"short-term":[300],"memory":[301],"(LSTM)":[302],"gating":[305],"recurrent":[306],"units":[307],"(GRUs),":[308],"better":[315],"higher":[318],"accuracy.":[320],"RMS":[322],"error":[323,327],"average":[325],"absolute":[326],"step":[331],"3":[333],"are":[334],"0.0146":[335],"0.0105,":[337],"respectively.":[338],"Overall,":[339],"universal":[344],"be":[348],"other":[357],"mechanical":[358],"equipment/parts.":[359]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}
