{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T19:01:38Z","timestamp":1775502098514,"version":"3.50.1"},"reference-count":62,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Advanced Engineering Informatics"],"published-print":{"date-parts":[[2026,9]]},"DOI":"10.1016\/j.aei.2026.104593","type":"journal-article","created":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T10:33:54Z","timestamp":1774694034000},"page":"104593","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"PA","title":["Human risk prediction and key factors identification for advanced operation systems based on Attention-STGRU model"],"prefix":"10.1016","volume":"74","author":[{"given":"Yidan","family":"Qiao","sequence":"first","affiliation":[]},{"given":"Haotian","family":"Li","sequence":"additional","affiliation":[]},{"given":"Jing","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Dengkai","family":"Chen","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.aei.2026.104593_b0005","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2020.107385","article-title":"A Bayesian network for reliability assessment of man-machine phased-mission system considering the phase dependencies of human cognitive error","volume":"207","author":"Wang","year":"2021","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"10.1016\/j.aei.2026.104593_b0010","first-page":"27","article-title":"Human error led to sinking of Taiwanese research vessel","volume":"3","author":"Normile","year":"2015","journal-title":"Science"},{"issue":"16","key":"10.1016\/j.aei.2026.104593_b0015","doi-asserted-by":"crossref","first-page":"6173","DOI":"10.1073\/pnas.0708965105","article-title":"Prediction of human errors by maladaptive changes in event-related brain networks","volume":"105","author":"Eichele","year":"2008","journal-title":"Proc. Natl. Acad. Sci."},{"key":"10.1016\/j.aei.2026.104593_b0020","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2022.108750","article-title":"A model fusion strategy for identifying aircraft risk using CNN and Att-BiLSTM","volume":"228","author":"Zhou","year":"2022","journal-title":"Reliab. Eng. Syst. Saf."},{"issue":"1","key":"10.1016\/j.aei.2026.104593_b0025","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.ress.2011.10.005","article-title":"Spacecraft electrical power subsystem: failure behavior, reliability, and multi-state failure analyses","volume":"98","author":"Kim","year":"2012","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"10.1016\/j.aei.2026.104593_b0030","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2023.109865","article-title":"Dynamic assessment method for human factor risk of manned deep submergence operation system based on SPAR-H and SD","volume":"243","author":"Qiao","year":"2024","journal-title":"Reliab. Eng. Syst. Saf."},{"issue":"1","key":"10.1016\/j.aei.2026.104593_b0035","doi-asserted-by":"crossref","first-page":"14507","DOI":"10.1038\/s41598-023-41063-y","article-title":"An evaluation method for HMI of deep-sea manned submersible based on human reliability","volume":"13","author":"Zhou","year":"2023","journal-title":"Sci. Rep."},{"key":"10.1016\/j.aei.2026.104593_b0040","doi-asserted-by":"crossref","DOI":"10.1016\/j.geomorph.2021.107902","article-title":"Geomorphology of a bended submarine canyon in Wanhu Seamount region, northern South China Sea: insights from manned submersible observation and measurement","volume":"392","author":"Luo","year":"2021","journal-title":"Geomorphology"},{"key":"10.1016\/j.aei.2026.104593_b0045","article-title":"Assessment of HRA method predictions against operating crew performance: Part I: Study background, design and methodology","volume":"191","author":"Liao","year":"2019","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"10.1016\/j.aei.2026.104593_b0050","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2024.110208","article-title":"A deep learning methodology based on adaptive multiscale CNN and enhanced highway LSTM for industrial process fault diagnosis","volume":"249","author":"Zhao","year":"2024","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"10.1016\/j.aei.2026.104593_b0055","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2024.110148","article-title":"Predicting maritime accident risk using Automated Machine Learning","volume":"248","author":"Munim","year":"2024","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"10.1016\/j.aei.2026.104593_b0060","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2023.109218","article-title":"Operational reliability evaluation and analysis framework of civil aircraft complex system based on intelligent extremum machine learning model","volume":"235","author":"Jia-Qi","year":"2023","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"10.1016\/j.aei.2026.104593_b0065","doi-asserted-by":"crossref","unstructured":"Al-Azazi, F. A., & Ghurab, M. (2023). ANN-LSTM: A deep learning model for early student performance prediction in MOOC. heliyon, 9(4).","DOI":"10.1016\/j.heliyon.2023.e15382"},{"issue":"3","key":"10.1016\/j.aei.2026.104593_b0070","doi-asserted-by":"crossref","DOI":"10.1115\/1.4048625","article-title":"An LSTM-based ensemble learning approach for time-dependent reliability analysis","volume":"143","author":"Li","year":"2021","journal-title":"J. Mech. Des."},{"key":"10.1016\/j.aei.2026.104593_b0075","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2024.102408","article-title":"A hybrid SNN-STLSTM method for human error assessment in the high-speed railway system","volume":"60","author":"Zhou","year":"2024","journal-title":"Adv. Eng. Inf."},{"issue":"1","key":"10.1016\/j.aei.2026.104593_b0080","doi-asserted-by":"crossref","first-page":"2185","DOI":"10.1038\/s41467-024-46511-5","article-title":"A multi-demand operating system underlying diverse cognitive tasks","volume":"15","author":"Cai","year":"2024","journal-title":"Nat. Commun."},{"issue":"1","key":"10.1016\/j.aei.2026.104593_b0085","doi-asserted-by":"crossref","first-page":"7231","DOI":"10.1038\/s41467-022-34780-x","article-title":"Path sampling of recurrent neural networks by incorporating known physics","volume":"13","author":"Tsai","year":"2022","journal-title":"Nat. Commun."},{"issue":"7","key":"10.1016\/j.aei.2026.104593_b0090","doi-asserted-by":"crossref","first-page":"920","DOI":"10.1038\/s41562-020-01036-x","article-title":"An implicit memory of errors limits human sensorimotor adaptation","volume":"5","author":"Albert","year":"2021","journal-title":"Nat. Hum. Behav."},{"key":"10.1016\/j.aei.2026.104593_b0095","doi-asserted-by":"crossref","unstructured":"Yin, C., Imms, P., Cheng, M., Amgalan, A., Chowdhury, N. F., Massett, R. J., ... & Alzheimer\u2019s Disease Neuroimaging Initiative. (2023). Anatomically interpretable deep learning of brain age captures domain-specific cognitive impairment.Proceedings of the National Academy of Sciences,120(2), e2214634120.","DOI":"10.1073\/pnas.2214634120"},{"issue":"3","key":"10.1016\/j.aei.2026.104593_b0100","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1038\/s42255-024-00989-x","article-title":"Osteocyte-derived sclerostin impairs cognitive function during ageing and Alzheimer\u2019s disease progression","volume":"6","author":"Shi","year":"2024","journal-title":"Nature Metabolism"},{"issue":"18","key":"10.1016\/j.aei.2026.104593_b0105","doi-asserted-by":"crossref","first-page":"7186","DOI":"10.1073\/pnas.0509550103","article-title":"Computational and neurobiological mechanisms underlying cognitive flexibility","volume":"103","author":"Badre","year":"2006","journal-title":"Proc. Natl. Acad. Sci."},{"issue":"39","key":"10.1016\/j.aei.2026.104593_b0110","doi-asserted-by":"crossref","first-page":"9696","DOI":"10.1073\/pnas.1719452115","article-title":"Predicting human behavior toward members of different social groups","volume":"115","author":"Jenkins","year":"2018","journal-title":"Proc. Natl. Acad. Sci."},{"issue":"2","key":"10.1016\/j.aei.2026.104593_b0115","doi-asserted-by":"crossref","DOI":"10.1126\/sciadv.1500445","article-title":"Lost in transportation: Information measures and cognitive limits in multilayer navigation","volume":"2","author":"Gallotti","year":"2016","journal-title":"Sci. Adv."},{"key":"10.1016\/j.aei.2026.104593_b0120","doi-asserted-by":"crossref","DOI":"10.1016\/j.oceaneng.2024.118153","article-title":"Human reliability analysis of offshore high integrity pressure protection system based on improved CREAM and HCR integration method","volume":"307","author":"Yu","year":"2024","journal-title":"Ocean Eng."},{"issue":"38","key":"10.1016\/j.aei.2026.104593_b0125","doi-asserted-by":"crossref","DOI":"10.1126\/sciadv.abm5952","article-title":"Analysis of sloppiness in model simulations: Unveiling parameter uncertainty when mathematical models are fitted to data","volume":"8","author":"Monsalve-Bravo","year":"2022","journal-title":"Sci. Adv."},{"key":"10.1016\/j.aei.2026.104593_b0130","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.121237","article-title":"A hybrid HFACS model using DEMATEL-ORESTE method with linguistic Z-number for risk analysis of human error factors in the healthcare system","volume":"235","author":"Zheng","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.aei.2026.104593_b0135","unstructured":"Boring, R. L. (2012).Fifty years of THERP and human reliability analysis(No. INL\/CON-12-25623). Idaho National Lab.(INL), Idaho Falls, ID (United States)."},{"key":"10.1016\/j.aei.2026.104593_b0140","series-title":"Reliability, maintainability and risk: practical methods for engineers","author":"Smith","year":"2021"},{"key":"10.1016\/j.aei.2026.104593_b0145","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.ergon.2014.11.004","article-title":"A Bayes-SLIM based methodology for human reliability analysis of lifting operations","volume":"45","author":"Tu","year":"2015","journal-title":"Int. J. Ind. Ergon."},{"key":"10.1016\/j.aei.2026.104593_b0150","unstructured":"Forester, J. A., Bley, D. C., COOPER, S., Kolaczkowski, A. M., Thompson, C., Ramey-Smith, A., & Wreathall, J. (2000).A description of the revised ATHEANA (A Technique for Human Event Analysis)(No. SAND2000-1757C). Sandia National Lab.(SNL-NM), Albuquerque, NM (United States); Sandia National Lab.(SNL-CA), Livermore, CA (United States)."},{"key":"10.1016\/j.aei.2026.104593_b0155","series-title":"Cognitive reliability and error analysis method (CREAM)","author":"Hollnagel","year":"1998"},{"issue":"5","key":"10.1016\/j.aei.2026.104593_b0160","doi-asserted-by":"crossref","first-page":"1058","DOI":"10.1109\/21.179844","article-title":"COSIMO: a cognitive simulation model of human decision making and behavior in accident management of complex plants","volume":"22","author":"Cacciabue","year":"1992","journal-title":"IEEE Trans. Syst. Man Cybern."},{"issue":"8","key":"10.1016\/j.aei.2026.104593_b0165","doi-asserted-by":"crossref","first-page":"997","DOI":"10.1016\/j.ress.2006.05.014","article-title":"Cognitive modeling and dynamic probabilistic simulation of operating crew response to complex system accidents: Part 1: Overview of the IDAC Model","volume":"92","author":"Chang","year":"2007","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"10.1016\/j.aei.2026.104593_b0170","article-title":"Leveraging existing human performance data for quantifying the idheas hra method","author":"Liao","year":"2013","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"10.1016\/j.aei.2026.104593_b0175","unstructured":"Whaley, A. M., Hendrickson, S. M., Boring, R. L., Joe, J. C., Le Blanc, K. L., & Xing, J. (2012). Bridging human reliability analysis and psychology, Part 1: The psychological literature review for the IDHEAS method."},{"key":"10.1016\/j.aei.2026.104593_b0180","unstructured":"Chang, Y. J., Xing, J., & DeJesus Segarra, J. (2021). IDHEAS Suite for Human Reliability Analysis. In Proceedings of the 2021 International Topical Meeting on Probabilistic Safety Assessment and Analysis (PSA 2021) (pp. 1231-1238)."},{"issue":"8","key":"10.1016\/j.aei.2026.104593_b0185","doi-asserted-by":"crossref","first-page":"1061","DOI":"10.1016\/j.ress.2006.05.011","article-title":"Cognitive modeling and dynamic probabilistic simulation of operating crew response to complex system accidents. Part 4: IDAC causal model of operator problem-solving response","volume":"92","author":"Chang","year":"2007","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"10.1016\/j.aei.2026.104593_b0190","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2024.110123","article-title":"Phoenix\u2013A model-based human reliability analysis methodology: Data sources and quantitative analysis procedure","volume":"248","author":"Ekanem","year":"2024","journal-title":"Reliab. Eng. Syst. Saf."},{"issue":"1","key":"10.1016\/j.aei.2026.104593_b0195","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/S0951-8320(96)00104-4","article-title":"The IDA cognitive model for the analysis of nuclear power plant operator response under accident conditions. Part I: problem solving and decision-making model","volume":"55","author":"Smidts","year":"1997","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"10.1016\/j.aei.2026.104593_b0200","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2022.108731","article-title":"Bayesian post-processing of Monte Carlo simulation in reliability analysis","volume":"227","author":"Betz","year":"2022","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"10.1016\/j.aei.2026.104593_b0205","doi-asserted-by":"crossref","DOI":"10.1016\/j.oceaneng.2023.116658","article-title":"Real-time spatiotemporal forecast of natural gas jet fire from offshore platform by using deep probability learning","volume":"294","author":"Xie","year":"2024","journal-title":"Ocean Eng."},{"key":"10.1016\/j.aei.2026.104593_b0210","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2023.101964","article-title":"Large group decision-making based on interval rough integrated cloud model","volume":"56","author":"Jiang","year":"2023","journal-title":"Adv. Eng. Inf."},{"key":"10.1016\/j.aei.2026.104593_b0215","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2024.111599","article-title":"An ensemble deep learning model for human activity analysis using wearable sensory data","volume":"159","author":"Batool","year":"2024","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.aei.2026.104593_b0220","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2024.110268","article-title":"Threshold-based earthquake early warning for high-speed railways using deep learning","volume":"250","author":"Zhu","year":"2024","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"10.1016\/j.aei.2026.104593_b0225","article-title":"KRAIL: a knowledge-driven framework for human reliability analysis integrating IDHEAS-DATA and large language models","volume":"111585","author":"Xiao","year":"2025","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"10.1016\/j.aei.2026.104593_b0230","doi-asserted-by":"crossref","DOI":"10.1016\/j.cie.2023.109687","article-title":"Dynamic risk assessment for train brake system considering time-dependent components and human factors","volume":"185","author":"Zhang","year":"2023","journal-title":"Comput. Ind. Eng."},{"key":"10.1016\/j.aei.2026.104593_b0235","series-title":"Predicting Human Errors from Gaze and Cursor Movements","first-page":"1","author":"Saboundji","year":"2020"},{"key":"10.1016\/j.aei.2026.104593_b0240","doi-asserted-by":"crossref","first-page":"467","DOI":"10.1016\/j.psep.2017.11.018","article-title":"Human factors risk assessment and management: Process safety in engineering","volume":"113","author":"Xie","year":"2018","journal-title":"Process Saf. Environ. Prot."},{"key":"10.1016\/j.aei.2026.104593_b0245","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2023.109103","article-title":"Towards objective human performance measurement for maritime safety: a new psychophysiological data-driven machine learning method","volume":"233","author":"Fan","year":"2023","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"10.1016\/j.aei.2026.104593_b0250","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2021.107890","article-title":"Analysis of dependencies among performance shaping factors in human reliability analysis based on a system dynamics approach","volume":"215","author":"Liu","year":"2021","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"10.1016\/j.aei.2026.104593_b0255","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2024.110569","article-title":"An integrated method of extended STPA and BN for safety assessment of man-machine phased-mission system","volume":"253","author":"Lu","year":"2025","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"10.1016\/j.aei.2026.104593_b0260","doi-asserted-by":"crossref","unstructured":"Cunningham, G., & Kitson, A. (2000). An evaluation of the RCN clinical leadership development programme: Part 2.Nursing Standard (through 2013),15(13-15), 34.","DOI":"10.7748\/ns2000.12.15.13.34.c2956"},{"key":"10.1016\/j.aei.2026.104593_b0265","doi-asserted-by":"crossref","first-page":"819","DOI":"10.1016\/j.ins.2022.06.028","article-title":"Deep stochastic configuration networks with different random sampling strategies","volume":"607","author":"Felicetti","year":"2022","journal-title":"Inf. Sci."},{"key":"10.1016\/j.aei.2026.104593_b0270","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2020.106685","article-title":"LAVARNET: Neural network modeling of causal variable relationships for multivariate time series forecasting","volume":"96","author":"Koutlis","year":"2020","journal-title":"Appl. Soft Comput."},{"issue":"4","key":"10.1016\/j.aei.2026.104593_b0275","first-page":"413","article-title":"Causal analysis of the internationalization and performance relationship based on neural networks\u2014advocating the transnational structure","volume":"15","author":"Garbe","year":"2009","journal-title":"J. Int. Manag."},{"key":"10.1016\/j.aei.2026.104593_b0280","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2022.105658","article-title":"Causal variable selection for industrial process quality prediction via attention-based GRU network","volume":"118","author":"Yao","year":"2023","journal-title":"Eng. Appl. Artif. Intel."},{"key":"10.1016\/j.aei.2026.104593_b0285","article-title":"On causal discovery from time series data using FCI","volume":"16","author":"Entner","year":"2010","journal-title":"Probabilistic Graphical Models"},{"issue":"4","key":"10.1016\/j.aei.2026.104593_b0290","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1177\/001872086901100410","article-title":"Quantifying human performance for reliability analysis of systems","volume":"11","author":"Askren","year":"1969","journal-title":"Hum. Factors"},{"issue":"1","key":"10.1016\/j.aei.2026.104593_b0295","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0053488","article-title":"Predicting recovery of cognitive function soon after stroke: differential modeling of logarithmic and linear regression","volume":"8","author":"Suzuki","year":"2013","journal-title":"PLoS One"},{"key":"10.1016\/j.aei.2026.104593_b0300","unstructured":"Chang, Y. J., **ng, J., & DeJesus Segarra, J. (2021). IDHEAS Suite for Human Reliability Analysis. InProceedings of the 2021 International Topical Meeting on Probabilistic Safety Assessment and Analysis (PSA 2021)(pp. 1231-1238)."},{"key":"10.1016\/j.aei.2026.104593_b0305","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.anucene.2016.10.020","article-title":"An experimental investigation on relationship between PSFs and operator performances in the digital main control room","volume":"101","author":"Park","year":"2017","journal-title":"Ann. Nucl. Energy"},{"key":"10.1016\/j.aei.2026.104593_b0310","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2022.108766","article-title":"A Bayesian belief network framework for nuclear power plant human reliability analysis accounting for dependencies among performance shaping factors","volume":"228","author":"Liu","year":"2022","journal-title":"Reliab. Eng. Syst. Saf."}],"container-title":["Advanced Engineering Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1474034626002855?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1474034626002855?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T17:59:34Z","timestamp":1775498374000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1474034626002855"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":62,"alternative-id":["S1474034626002855"],"URL":"https:\/\/doi.org\/10.1016\/j.aei.2026.104593","relation":{},"ISSN":["1474-0346"],"issn-type":[{"value":"1474-0346","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Human risk prediction and key factors identification for advanced operation systems based on Attention-STGRU model","name":"articletitle","label":"Article Title"},{"value":"Advanced Engineering Informatics","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.aei.2026.104593","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"104593"}}