{"id":"https://openalex.org/W7006663693","doi":null,"title":"Utilizaci\u00f3n de algoritmos de Machine Learning / Deep Learning (ML/DL) para la predicci\u00f3n de par\u00e1metros de gesti\u00f3n del transporte a\u00e9reo","display_name":"Utilizaci\u00f3n de algoritmos de Machine Learning / Deep Learning (ML/DL) para la predicci\u00f3n de par\u00e1metros de gesti\u00f3n del transporte a\u00e9reo","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W7006663693"},"language":"es","primary_location":{"id":"pmh:oai:digibug.ugr.es:10481/92738","doi":null,"is_oa":true,"landing_page_url":"https://hdl.handle.net/10481/92738","pdf_url":"https://digibug.ugr.es/bitstream/10481/92738/4/95223.pdf","source":{"id":"https://openalex.org/S4306400567","display_name":"Institutional Repository of the University of Granada (University of Granada)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I173304897","host_organization_name":"Universidad de Granada","host_organization_lineage":["https://openalex.org/I173304897"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/doctoralThesis"},"volume":null,"type":"dissertation","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://digibug.ugr.es/bitstream/10481/92738/4/95223.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5079289744","display_name":"Juan Gerardo Muros Anguita","orcid":"https://orcid.org/0000-0002-4408-7959","observed_orcids":["https://orcid.org/0000-0002-4408-7959"]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Muros Anguita, Juan Gerardo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5079289744"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":{"id":"https://openalex.org/T11918","display_name":"Forecasting Techniques and Applications","score":0.5327000021934509,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11918","display_name":"Forecasting Techniques and Applications","score":0.5327000021934509,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11599","display_name":"Aviation Industry Analysis and Trends","score":0.16779999434947968,"subfield":{"id":"https://openalex.org/subfields/2000","display_name":"General Economics, Econometrics and Finance"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11489","display_name":"Air Traffic Management and Optimization","score":0.1273999959230423,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace 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/aviation-management","display_name":"aviation management","score":0.4790000021457672},{"id":"https://openalex.org/keywords/passenger-flow-prediction","display_name":"passenger demand forecasting","score":0.7649999856948853},{"id":"https://openalex.org/keywords/time-series-forecasting","display_name":"time series forecasting","score":0.7149999737739563},{"id":"https://openalex.org/keywords/convlstm2d","display_name":"ConvLSTM2D","score":0.75},{"id":"https://openalex.org/keywords/hybrid-convolutional-recurrent-neural-network","display_name":"hybrid convolutional recurrent neural network","score":0.7160000205039978},{"id":"https://openalex.org/keywords/chatgpt","display_name":"ChatGPT","score":0.5260000228881836},{"id":"https://openalex.org/keywords/chatgpt-3","display_name":"ChatGPT-3","score":0.6779999732971191},{"id":"https://openalex.org/keywords/chatgpt-4","display_name":"ChatGPT-4","score":0.7319999933242798},{"id":"https://openalex.org/keywords/chatgpt-5","display_name":"ChatGPT-5","score":0.6110000014305115}],"concepts":[{"id":"https://openalex.org/C15708023","wikidata":"https://www.wikidata.org/wiki/Q80083","display_name":"Humanities","level":1,"score":0.573199987411499},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5042999982833862},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39430001378059387},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.35510000586509705},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.29089999198913574},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.20730000734329224},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.20080000162124634},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.18780000507831573},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.15870000422000885},{"id":"https://openalex.org/C2988886741","wikidata":"https://www.wikidata.org/wiki/Q25304494","display_name":"Dictionary learning","level":3,"score":0.1542000025510788}],"mesh":[],"locations_count":1,"locations":[{"id":"pmh:oai:digibug.ugr.es:10481/92738","doi":null,"is_oa":true,"landing_page_url":"https://hdl.handle.net/10481/92738","pdf_url":"https://digibug.ugr.es/bitstream/10481/92738/4/95223.pdf","source":{"id":"https://openalex.org/S4306400567","display_name":"Institutional Repository of the University of Granada (University of Granada)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I173304897","host_organization_name":"Universidad de Granada","host_organization_lineage":["https://openalex.org/I173304897"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/doctoralThesis"}],"best_oa_location":{"id":"pmh:oai:digibug.ugr.es:10481/92738","doi":null,"is_oa":true,"landing_page_url":"https://hdl.handle.net/10481/92738","pdf_url":"https://digibug.ugr.es/bitstream/10481/92738/4/95223.pdf","source":{"id":"https://openalex.org/S4306400567","display_name":"Institutional Repository of the University of Granada (University of Granada)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I173304897","host_organization_name":"Universidad de Granada","host_organization_lineage":["https://openalex.org/I173304897"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/doctoralThesis"},"sustainable_development_goals":[{"score":0.7207000255584717,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7006663693.pdf","grobid_xml":"https://content.openalex.org/works/W7006663693.grobid-xml"},"referenced_works_count":135,"referenced_works":["https://openalex.org/W111113856","https://openalex.org/W141740236","https://openalex.org/W186039566","https://openalex.org/W633809348","https://openalex.org/W1485009520","https://openalex.org/W1498436455","https://openalex.org/W1759511147","https://openalex.org/W1806891645","https://openalex.org/W1921848483","https://openalex.org/W1963788522","https://openalex.org/W1965549775","https://openalex.org/W1965678940","https://openalex.org/W1969588137","https://openalex.org/W1975877689","https://openalex.org/W1979691156","https://openalex.org/W1992977321","https://openalex.org/W1994616650","https://openalex.org/W2004521717","https://openalex.org/W2013457231","https://openalex.org/W2013533676","https://openalex.org/W2013662174","https://openalex.org/W2013704105","https://openalex.org/W2015444745","https://openalex.org/W2020970536","https://openalex.org/W2026148184","https://openalex.org/W2029663307","https://openalex.org/W2029897328","https://openalex.org/W2033133698","https://openalex.org/W2038478460","https://openalex.org/W2039905553","https://openalex.org/W2044311525","https://openalex.org/W2051175704","https://openalex.org/W2056132907","https://openalex.org/W2060679763","https://openalex.org/W2063833099","https://openalex.org/W2079222894","https://openalex.org/W2084126963","https://openalex.org/W2089296357","https://openalex.org/W2089849905","https://openalex.org/W2102636708","https://openalex.org/W2106883422","https://openalex.org/W2112716550","https://openalex.org/W2112796928","https://openalex.org/W2113214536","https://openalex.org/W2127469782","https://openalex.org/W2133564696","https://openalex.org/W2153667436","https://openalex.org/W2156804741","https://openalex.org/W2295598076","https://openalex.org/W2326132194","https://openalex.org/W2475367951","https://openalex.org/W2507242350","https://openalex.org/W2509690574","https://openalex.org/W2538285964","https://openalex.org/W2555745781","https://openalex.org/W2565112041","https://openalex.org/W2567648555","https://openalex.org/W2597044706","https://openalex.org/W2620883027","https://openalex.org/W2626778328","https://openalex.org/W2726647895","https://openalex.org/W2743094428","https://openalex.org/W2773985410","https://openalex.org/W2786485772","https://openalex.org/W2792643794","https://openalex.org/W2801397567","https://openalex.org/W2806710868","https://openalex.org/W2891776610","https://openalex.org/W2896398456","https://openalex.org/W2904327999","https://openalex.org/W2905664363","https://openalex.org/W2907849936","https://openalex.org/W2912205739","https://openalex.org/W2919115771","https://openalex.org/W2922362276","https://openalex.org/W2923625485","https://openalex.org/W2942826701","https://openalex.org/W2945062549","https://openalex.org/W2952016129","https://openalex.org/W2952216799","https://openalex.org/W2963741310","https://openalex.org/W2964121744","https://openalex.org/W2972753905","https://openalex.org/W2977653913","https://openalex.org/W2983189025","https://openalex.org/W2987643707","https://openalex.org/W2995533819","https://openalex.org/W2999446243","https://openalex.org/W3001336289","https://openalex.org/W3002632072","https://openalex.org/W3004487324","https://openalex.org/W3013330736","https://openalex.org/W3015267391","https://openalex.org/W3015692593","https://openalex.org/W3022412706","https://openalex.org/W3031160303","https://openalex.org/W3037673356","https://openalex.org/W3046311996","https://openalex.org/W3047312463","https://openalex.org/W3049359342","https://openalex.org/W3061808290","https://openalex.org/W3086552465","https://openalex.org/W3092407976","https://openalex.org/W3092535461","https://openalex.org/W3094200299","https://openalex.org/W3111585213","https://openalex.org/W3112196191","https://openalex.org/W3112501082","https://openalex.org/W3113964745","https://openalex.org/W3128036147","https://openalex.org/W3128504374","https://openalex.org/W3135765453","https://openalex.org/W3135864020","https://openalex.org/W3138789615","https://openalex.org/W3155112697","https://openalex.org/W3161730892","https://openalex.org/W3167565715","https://openalex.org/W3170786373","https://openalex.org/W3187253536","https://openalex.org/W3198350258","https://openalex.org/W3202471568","https://openalex.org/W3212507741","https://openalex.org/W3217409024","https://openalex.org/W4200144900","https://openalex.org/W4206062825","https://openalex.org/W4210902695","https://openalex.org/W4210922522","https://openalex.org/W4220954082","https://openalex.org/W4221082242","https://openalex.org/W4224992943","https://openalex.org/W4312603504","https://openalex.org/W4364320763","https://openalex.org/W4366525304","https://openalex.org/W4377099193","https://openalex.org/W4401951602"],"related_works":["https://openalex.org/W7061145408","https://openalex.org/W7014597374","https://openalex.org/W7082986569","https://openalex.org/W7054827126","https://openalex.org/W3036212468","https://openalex.org/W7106791154","https://openalex.org/W6995898920","https://openalex.org/W6999640855","https://openalex.org/W4404416271","https://openalex.org/W4366525304"],"abstract_inverted_index":{"Predecir":[0],"los":[1,9,15,74,224,239,274,317,320,384,415,419,434,466,499,530,562,595,658],"par\u00e1metros":[2],"de":[3,31,35,39,41,53,61,73,93,111,122,157,164,168,182,186,194,201,210,213,216,218,228,230,235,244,249,273,277,289,295,307,313,319,322,339,345,349,355,370,389,407,445,447,478,492,506,516,527,529,534,537,552,571,588,592,597,631,643,664,684],"gesti\u00f3n":[4,142],"del":[5,18,29,68,143,226,333,335,397,469,576,623],"transporte":[6,19,144],"a\u00e9reo":[7,20],"en":[8,108,310,329,342,376,509,546,645],"pr\u00f3ximos":[10],"a\u00f1os":[11],"\\nes":[12],"cr\u00edtico":[13],"para":[14,88,238,299,464,474,497,513,620],"principales":[16],"agentes":[17],"como":[21,105,159,166,359,428,451,539,565],"son:":[22],"las":[23,112,278,285,308,311,343,380,387,475,517,535,676,682],"l\u00edneas":[24],"\\na\u00e9reas,":[25],"autoridades":[26],"aeroportuarias,":[27],"proveedores":[28],"servicio":[30],"navegaci\u00f3n":[32],"a\u00e9rea,":[33],"\\nadem\u00e1s":[34],"la":[36,46,94,121,141,155,187,202,211,214,305,324,331,340,394,479,590,646,662],"propia":[37],"industria":[38,47,647],"fabricaci\u00f3n":[40],"aviones,":[42,219,314],"etc.":[43,107,496],"Dado":[44],"que":[45,64,257,403,423,438,501,508,561,579,585,654,670],"\\naeron\u00e1utica":[48],"genera":[49],"casi":[50],"87.7":[51],"millones":[52],"empleos":[54],"(directos":[55],"e":[56,197,206],"inducidos)":[57],"y":[58,124,133,162,191,208,351,400,612,618,640],"3.4":[59],"\\nbillones":[60],"US$":[62],"lo":[63,146,437,669],"representa":[65,635],"un":[66,352,557,610],"4.1%":[67],"PIB":[69],"mundial.":[70],"El":[71],"uso":[72,91,444,551],"algoritmos":[75,425],"\\nde":[76,127,615,678],"Machine":[77],"Learning":[78,80],"/Deep":[79],"(ML/DL),":[81],"donde":[82,282],"recientemente":[83],"ha":[84,148,327,374,411,486,521,602,627],"irrumpido":[85],"\\nel":[86,541],"ChatGPT":[87],"extender":[89],"el":[90,109,184,199,209,271,300,362,443,484,503,510,514,525,550,567,621,629,636,641,671],"masivo":[92],"Inteligencia":[95],"Artificial":[96],"(IA),":[97],"vienen":[98],"\\ndesplazando":[99],"a":[100,154,189,304,337,379,413,609,687],"otros":[101],"m\u00e9todos":[102],"estad\u00edsticos":[103],"cl\u00e1sicos":[104,348],"ARIMA,":[106],"\\npron\u00f3stico":[110,515],"series":[113,279],"temporales.":[114],"\\nEsta":[115,600],"tesis":[116,601],"muestra":[117],"una":[118],"doble":[119],"viabilidad,":[120],"desarrollar":[123],"entrenar":[125],"modelos":[126,233,347,420,563,596,659],"ML/DL":[128,236,660],"con":[129,247,252,261,291,316,489],"reducidos":[130],"medios":[131],"propios,":[132],"por":[134,383,418,540,574,668],"otro":[135],"lado":[136],"demostrar":[137],"su":[138],"aplicabilidad":[139],"\\na":[140],"a\u00e9reo,":[145],"cual":[147,485],"sido":[149,487,522,603,628],"posible":[150,604],"gracias":[151],"sobre":[152],"todo":[153],"\\nexistencia":[156],"librerias":[158],"Keras,":[160],"Tensorflow":[161],"lenguajes":[163],"programaci\u00f3n":[165],"\\nPython,":[167],"c\u00f3digo":[169],"abierto,":[170],"orientados":[171],"al":[172,431,455,460,605],"aprendizaje":[173,245,272,598],"profundo":[174,246],"(DL).":[175],"En":[176],"particular,":[177],"\\nse":[178,426],"abordan":[179],"tres":[180],"casos":[181,242],"estudios:":[183],"pron\u00f3stico":[185,200],"demanda":[188],"corto":[190],"medio":[192,365],"\\nplazo":[193],"pasajeros":[195],"(nacionales":[196],"internacionales),":[198],"carga":[203],"a\u00e9rea":[204],"\\n(domestica":[205],"internacional)":[207],"predicci\u00f3n":[212,306],"demora":[215],"salida":[217,685],"\\ntodos":[220],"ellos":[221],"aplicados":[222],"usando":[223],"datos":[225,318,616],"caso":[227,630],"estudio":[229,632],"Colombia.":[231],"\\nLos":[232],"singulares":[234],"desarrollados":[237],"dos":[240],"primeros":[241],"\\nson":[243],"arquitecturas":[248],"codificadores-decodificadores":[250],"\\nrealizados":[251],"redes":[253,259,262],"neuronales":[254],"artificiales":[255],"hibridas":[256],"mezclan":[258,284],"\\nconvolucionales":[260],"recurrentes":[263],"tipo":[264],"LSTM,":[265],"denominadas":[266],"\u201cConvLSTM2D\u201d":[267],"\\nlas":[268],"cuales":[269],"mejoran":[270],"patrones":[275,435],"temporales":[276],"\\nhist\u00f3ricas":[280],"multivariable,":[281],"se":[283,326,373,410,439,544,580,655],"variables":[286,293,536,677,683],"aeron\u00e1uticas":[287],"objeto":[288],"\\nestudio":[290],"otras":[292],"socioecon\u00f3micas":[294],"contexto.":[296],"Mientras":[297,402],"que,":[298],"\\ntercer":[301],"caso,":[302],"relativo":[303],"demoras":[309],"salidas":[312],"\\naplicada":[315],"aeropuertos":[321],"Colombia,":[323],"investigaci\u00f3n":[325,372,409],"\\norientado":[328],"realizar":[330,524],"selecci\u00f3n":[332],"mejor":[334],"modelo,":[336],"trav\u00e9s":[338],"comparativa":[341,361],"prestaciones":[344],"varios":[346],"ML":[350],"modelo":[353],"simple":[354],"DL":[356],"\\n(MLP),":[357],"tomando":[358],"m\u00e9trica":[360],"error":[363],"cuadr\u00e1tico":[364],"(RMSE).":[366],"\\nUn":[367],"esfuerzo":[368],"importante":[369,406],"esta":[371,408,586],"centrado":[375],"proporcionar":[377],"\\nrobustez":[378],"valoraciones":[381],"obtenidas":[382],"modelos,":[385],"aplicando":[386],"t\u00e9cnicas":[388,446,490],"\\n\u201ck-fold":[390],"cross":[391],"validation\u201d,":[392],"dada":[393],"naturaleza":[395],"estoc\u00e1stica":[396],"problema":[398],"(modelo":[399],"\\ndatos).":[401],"otra":[404],"parte":[405,575],"orientado":[412],"\\nexplicar":[414],"resultados":[416],"obtenidos":[417,459],"ML/DL,":[421],"ya":[422,560,584],"estos":[424],"comportan":[427],"cajas":[429],"negras":[430],"no":[432,520,674],"explicitar":[433],"aprendidos,":[436],"\\nha":[440],"resuelto":[441],"mediante":[442,548],"\u201cpermutation":[448],"importance\u201d,":[449],"as\u00ed":[450],"\\naplicando":[452],"an\u00e1lisis":[453],"variacional":[454],"configurar":[456],"multiples":[457],"escenarios":[458],"\\ncombinar":[461],"diferentes":[462,518],"features,":[463],"seleccionar":[465],"m\u00e1s":[467],"relevantes":[468],"problema.":[470],"\\nEl":[471],"dataset":[472,511,672],"usado":[473,512],"demostraciones":[476],"procede":[477],"Autoridad":[480],"\\nAeron\u00e1utica":[481],"colombiana":[482],"Aerocivil,":[483],"preprocesado":[488,573],"\\nestad\u00edsticas":[491],"escalado,":[493],"normalizado,":[494],"categorizado,":[495],"homogeneizar":[498],"\\ndatos":[500],"faciliten":[502],"aprendizaje.":[504],"Es":[505],"subrayar":[507],"demandas":[519],"necesario":[523],"proceso":[526],"\\n\u201cextracci\u00f3n":[528],"features\u201d":[531],"o":[532],"caracter\u00edsticas":[533],"entrada,":[538],"contrario":[542],"si":[543],"requiere":[545],"f\u00edsica":[547],"p.e.":[549],"n\u00fameros":[553],"adimensionales":[554],"\\npara":[555],"categorizar":[556],"r\u00e9gimen,":[558],"etc.,":[559],"DL,":[564],"es":[566],"ConvLSTM2D":[568],"\\nno":[569],"requieren":[570],"dicho":[572],"ingeniero":[577],"(tarea":[578],"conoce":[581],"\\n\u201cfeature":[582],"engineering\u201d),":[583],"extracci\u00f3n":[587],"features":[589],"realizan":[591],"forma":[593],"\\nautom\u00e1tica":[594],"profundo.":[599],"haber":[606],"podido":[607],"acceder":[608],"gran":[611],"fiable":[613],"volumen":[614],"aeron\u00e1uticos":[617],"socioecon\u00f3micos":[619],"entrenamiento":[622],"modelo.":[624],"Donde":[625],"\\nColombia":[626],"elegido,":[633],"porque":[634],"tercer":[637],"pa\u00eds":[638],"\\nlatinoamericano":[639],"quinto":[642],"am\u00e9rica":[644],"aeron\u00e1utica.":[648],"Por":[649],"\u00faltimo,":[650],"hay":[651],"\\nque":[652],"se\u00f1alar":[653],"han":[656],"entrenado":[657],"bajo":[661],"modalidad":[663],"\\naprendizaje":[665],"supervisado":[666],"(SL),":[667],"conten\u00eda":[673],"solo":[675],"entrada":[679],"sino":[680],"tambi\u00e9n":[681],"(objetivas)":[686],"predecir.":[688]},"counts_by_year":[],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2025-10-10T00:00:00"}