{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T20:16:34Z","timestamp":1783628194230,"version":"3.55.0"},"reference-count":60,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100012542","name":"Sichuan Province Science and Technology Support Program","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100012542","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Reliability Engineering &amp; System Safety"],"published-print":{"date-parts":[[2027,1]]},"DOI":"10.1016\/j.ress.2026.113108","type":"journal-article","created":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T16:07:13Z","timestamp":1783440433000},"page":"113108","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"P3","title":["Learnable adaptive sequence compression and spatiotemporal fusion approach for aero-engine remaining useful life prediction"],"prefix":"10.1016","volume":"277","author":[{"given":"Xiaoqi","family":"Huang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3926-7234","authenticated-orcid":false,"given":"Jianyu","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Heng","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Enrico","family":"Zio","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8879-7266","authenticated-orcid":false,"given":"Qiang","family":"Miao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.ress.2026.113108_b1","series-title":"2008 international conference on prognostics and health management","first-page":"1","article-title":"Damage propagation modeling for aircraft engine run-to-failure simulation","author":"Saxena","year":"2008"},{"key":"10.1016\/j.ress.2026.113108_b2","article-title":"A comprehensive review of remaining useful life prediction for aero-engines: Data-driven deep learning approaches","volume":"241","author":"Wang","year":"2024","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2026.113108_b3","series-title":"2008 international conference on prognostics and health management","first-page":"1","article-title":"Metrics for evaluating performance of prognostic techniques","author":"Saxena","year":"2008"},{"key":"10.1016\/j.ress.2026.113108_b4","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2020.107929","article-title":"Deep learning for prognostics and health management: State of the art, challenges, and opportunities","volume":"163","author":"Rezaeianjouybari","year":"2020","journal-title":"Measurement"},{"key":"10.1016\/j.ress.2026.113108_b5","series-title":"IEEE transactions on instrumentation and measurement","article-title":"Remaining useful life prediction of turbofan engines using CNN-LSTM-SAM approach","author":"Zhang","year":"2023"},{"issue":"1","key":"10.1016\/j.ress.2026.113108_b6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ejor.2010.11.018","article-title":"Remaining useful life estimation\u2014a review on the statistical data driven approaches","volume":"213","author":"Si","year":"2011","journal-title":"European J Oper Res"},{"issue":"1","key":"10.1016\/j.ress.2026.113108_b7","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1109\/TR.2014.2299152","article-title":"Review of hybrid prognostics approaches for remaining useful life prediction of engineered systems, and an application to battery life prediction","volume":"63","author":"Liao","year":"2014","journal-title":"IEEE Trans Reliab"},{"key":"10.1016\/j.ress.2026.113108_b8","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2020.103678","article-title":"Potential, challenges and future directions for deep learning in prognostics and health management applications","volume":"92","author":"Fink","year":"2020","journal-title":"Eng Appl Artif Intell"},{"issue":"7","key":"10.1016\/j.ress.2026.113108_b9","doi-asserted-by":"crossref","first-page":"1751","DOI":"10.1016\/j.engappai.2013.02.006","article-title":"Remaining useful life estimation based on nonlinear feature reduction and support vector regression","volume":"26","author":"Benkedjouh","year":"2013","journal-title":"Eng Appl Artif Intell"},{"issue":"12","key":"10.1016\/j.ress.2026.113108_b10","doi-asserted-by":"crossref","first-page":"7186","DOI":"10.3390\/app13127186","article-title":"Remaining useful life prediction of aircraft turbofan engine based on random forest feature selection and multi-layer perceptron","volume":"13","author":"Wang","year":"2023","journal-title":"Appl Sci"},{"key":"10.1016\/j.ress.2026.113108_b11","article-title":"Deep learning for prognostics and health management: A comprehensive review of recent advancements","volume":"58","author":"Nascimento","year":"2024","journal-title":"Annu Rev Control"},{"key":"10.1016\/j.ress.2026.113108_b12","article-title":"Machine remaining useful life prediction method based on global-local attention compensation network","volume":"251","author":"Ding","year":"2025","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2026.113108_b13","first-page":"1","article-title":"Subdomain adaptation order network for fault diagnosis of brushless DC motors","volume":"73","author":"Luo","year":"2024","journal-title":"IEEE Trans Instrum Meas"},{"issue":"8","key":"10.1016\/j.ress.2026.113108_b14","doi-asserted-by":"crossref","first-page":"1798","DOI":"10.1109\/TPAMI.2013.50","article-title":"Representation learning: A review and new perspectives","volume":"35","author":"Bengio","year":"2013","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"10.1016\/j.ress.2026.113108_b15","doi-asserted-by":"crossref","DOI":"10.1016\/j.apenergy.2025.125986","article-title":"Adaptive diagnosis and prognosis for lithium-ion batteries via lebesgue time model with multiple hidden state variables","volume":"392","author":"Zhang","year":"2025","journal-title":"Appl Energy","ISSN":"https:\/\/id.crossref.org\/issn\/0306-2619","issn-type":"print"},{"key":"10.1016\/j.ress.2026.113108_b16","article-title":"A Bayesian adversarial ProbSparse transformer model for long-term remaining useful life prediction","volume":"62","author":"Cheng","year":"2024","journal-title":"Adv Eng Inform"},{"key":"10.1016\/j.ress.2026.113108_b17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ress.2017.11.021","article-title":"Remaining useful life estimation in prognostics using deep convolution neural networks","volume":"172","author":"Li","year":"2018","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2026.113108_b18","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2025.103319","article-title":"Zero-shot fault diagnosis using soft semantic embedding of diffusion-encoded probability","volume":"65","author":"Li","year":"2025","journal-title":"Adv Eng Inform"},{"key":"10.1016\/j.ress.2026.113108_b19","first-page":"1","article-title":"Micro transfer learning mechanism for cross-domain equipment rul prediction","author":"Xiang","year":"2024","journal-title":"IEEE Trans Autom Sci Eng"},{"issue":"8","key":"10.1016\/j.ress.2026.113108_b20","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput"},{"key":"10.1016\/j.ress.2026.113108_b21","first-page":"1","article-title":"Fault diagnosis of motors under varying operating conditions with only phase current and limited samples","volume":"74","author":"Zhao","year":"2025","journal-title":"IEEE Trans Instrum Meas"},{"key":"10.1016\/j.ress.2026.113108_b22","series-title":"2008 international conference on prognostics and health management","first-page":"1","article-title":"Recurrent neural networks for remaining useful life estimation","author":"Heimes","year":"2008"},{"key":"10.1016\/j.ress.2026.113108_b23","series-title":"2017 IEEE international conference on prognostics and health management","first-page":"88","article-title":"Long short-term memory network for remaining useful life estimation","author":"Zheng","year":"2017"},{"key":"10.1016\/j.ress.2026.113108_b24","article-title":"Remaining useful life prediction of machinery based on time-frequency feature extraction and convolutional neural networks","volume":"213","author":"Wang","year":"2021","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2026.113108_b25","series-title":"Proceedings of the 21st international conference on database systems for advanced applications","first-page":"214","article-title":"Deep convolutional neural network based regression approach for estimation of remaining useful life","author":"Babu","year":"2016"},{"issue":"3","key":"10.1016\/j.ress.2026.113108_b26","doi-asserted-by":"crossref","first-page":"2521","DOI":"10.1109\/TIE.2020.2972443","article-title":"Machine remaining useful life prediction via an attention-based deep learning approach","volume":"68","author":"Chen","year":"2021","journal-title":"IEEE Trans Ind Electron"},{"key":"10.1016\/j.ress.2026.113108_b27","first-page":"5998","article-title":"Attention is all you need","volume":"vol. 30","author":"Vaswani","year":"2017"},{"key":"10.1016\/j.ress.2026.113108_b28","article-title":"A dual-task deep LSTM networks with self-attention for remaining useful life prediction","volume":"215","author":"Miao","year":"2021","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2026.113108_b29","article-title":"RUL prediction of rolling bearings using a novel attention-based deep learning approach","volume":"216","author":"Zhang","year":"2021","journal-title":"Reliab Eng Syst Saf"},{"issue":"7","key":"10.1016\/j.ress.2026.113108_b30","doi-asserted-by":"crossref","first-page":"1997","DOI":"10.1007\/s10845-021-01750-x","article-title":"Remaining useful life estimation via transformer encoder enhanced by a gated convolutional unit","volume":"32","author":"Mo","year":"2021","journal-title":"J Intell Manuf"},{"issue":"2","key":"10.1016\/j.ress.2026.113108_b31","first-page":"1128","article-title":"Attention-based deep learning framework for remaining useful life prediction","volume":"17","author":"Chen","year":"2020","journal-title":"IEEE Trans Ind Inform"},{"key":"10.1016\/j.ress.2026.113108_b32","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2024.102767","article-title":"A prognostic model for multi-stage degraded equipment under zero life label combining CDBN and Bayesian bidirectional GRU","volume":"62","author":"Pei","year":"2024","journal-title":"Adv Eng Inform"},{"key":"10.1016\/j.ress.2026.113108_b33","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2025.103805","article-title":"PIAN: A physics-informed assimilation neural network for temporal super-resolution reconstruction of sensor data in satellite attitude control system","volume":"68","author":"Wang","year":"2025","journal-title":"Adv Eng Inform"},{"key":"10.1016\/j.ress.2026.113108_b34","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.jmsy.2023.11.009","article-title":"Sensor-aware CapsNet: Towards trustworthy multisensory fusion for remaining useful life prediction","volume":"72","author":"Li","year":"2024","journal-title":"J Manuf Syst"},{"key":"10.1016\/j.ress.2026.113108_b35","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2023.109096","article-title":"An integrated multi-head dual sparse self-attention network for remaining useful life prediction","volume":"233","author":"Zhang","year":"2023","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2026.113108_b36","article-title":"Dual-task network based on attention-enhanced temporal convolutional network for remaining useful life prediction","volume":"230","author":"Zhang","year":"2023","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2026.113108_b37","article-title":"Mamba-attention: A self-supervised framework for efficient remaining useful life prediction","volume":"254","author":"Han","year":"2025","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2026.113108_b38","article-title":"An improved deformable convolutional residual attention and KAN-based RUL prediction model with early prediction and shape constraints","volume":"255","author":"Zhou","year":"2025","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2026.113108_b39","series-title":"Findings of the association for computational linguistics: EMNLP 2020","first-page":"1393","article-title":"Investigating transferability in pretrained language models","author":"Tamkin","year":"2020"},{"key":"10.1016\/j.ress.2026.113108_b40","series-title":"Proceedings of the 29th ACM SIGKDD conference on knowledge discovery and data mining","first-page":"459","article-title":"TSMixer: Lightweight MLP-Mixer model for multivariate time series forecasting","author":"Ekambaram","year":"2023"},{"issue":"1","key":"10.1016\/j.ress.2026.113108_b41","doi-asserted-by":"crossref","DOI":"10.3390\/data6010005","article-title":"Aircraft engine run-to-failure dataset under real flight conditions for prognostics and diagnostics","volume":"6","author":"Arias Chao","year":"2021","journal-title":"Data","ISSN":"https:\/\/id.crossref.org\/issn\/2306-5729","issn-type":"print"},{"key":"10.1016\/j.ress.2026.113108_b42","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2023.109821","article-title":"A dual attention LSTM lightweight model based on exponential smoothing for remaining useful life prediction","volume":"243","author":"Shi","year":"2024","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2026.113108_b43","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2021.107878","article-title":"Hierarchical attention graph convolutional network to fuse multi-sensor signals for remaining useful life prediction","volume":"215","author":"Li","year":"2021","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2026.113108_b44","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2023.109333","article-title":"A systematic method of remaining useful life estimation based on physics-informed graph neural networks with multisensor data","volume":"237","author":"He","year":"2023","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2026.113108_b45","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2025.111085","article-title":"Operating condition feature representation-based Fourier graph network for civil aircraft state estimation","volume":"261","author":"Liu","year":"2025","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2026.113108_b46","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2022.108330","article-title":"Aircraft engine remaining useful life estimation via a double attention-based data-driven architecture","volume":"221","author":"Liu","year":"2022","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2026.113108_b47","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2023.109662","article-title":"Trend-augmented and temporal-featured transformer network with multi-sensor signals for remaining useful life prediction","volume":"241","author":"Zhang","year":"2024","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2026.113108_b48","article-title":"Structural-guided interaction and attention-enhancing compensation network for machine remaining useful life prediction","volume":"73","author":"Liu","year":"2024","journal-title":"IEEE Trans Instrum Meas"},{"key":"10.1016\/j.ress.2026.113108_b49","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2024.110685","article-title":"Physics-informed spatio-temporal hybrid neural networks for predicting remaining useful life in aircraft engine","volume":"256","author":"Zhou","year":"2025","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2026.113108_b50","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2024.110770","article-title":"Spatio-temporal degradation model with graph neural network and structured state space model for remaining useful life prediction","volume":"256","author":"Wu","year":"2025","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2026.113108_b51","series-title":"Uncertainty-aware deep learning framework for remaining useful life prediction in turbofan engines with learned aleatoric uncertainty","author":"Sharma","year":"2025"},{"issue":"2","key":"10.1016\/j.ress.2026.113108_b52","doi-asserted-by":"crossref","first-page":"3720","DOI":"10.1109\/TNNLS.2023.3347227","article-title":"A lightweight group transformer-based time series reduction network for edge intelligence and its application in industrial RUL prediction","volume":"36","author":"Ren","year":"2025","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"10.1016\/j.ress.2026.113108_b53","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2026.112672","article-title":"A k-GNN Wiener process interactive RUL prognosis method for stochastic degrading devices monitored by related multiple sensors","volume":"275","author":"Zeng","year":"2026","journal-title":"Reliab Eng Syst Saf","ISSN":"https:\/\/id.crossref.org\/issn\/0951-8320","issn-type":"print"},{"key":"10.1016\/j.ress.2026.113108_b54","doi-asserted-by":"crossref","DOI":"10.1016\/j.jprocont.2024.103264","article-title":"A novel interactive prognosis framework with nonlinear Wiener process and multi-sensor fusion for remaining useful life prediction","volume":"140","author":"Lin","year":"2024","journal-title":"J Process Control","ISSN":"https:\/\/id.crossref.org\/issn\/0959-1524","issn-type":"print"},{"issue":"4","key":"10.1016\/j.ress.2026.113108_b55","doi-asserted-by":"crossref","first-page":"5505","DOI":"10.1109\/TII.2023.3333933","article-title":"An interpretable neuro-dynamic scheme with feature-temporal attention for remaining useful life estimation","volume":"20","author":"Qin","year":"2024","journal-title":"IEEE Trans Ind Inform"},{"key":"10.1016\/j.ress.2026.113108_b56","series-title":"2017 IEEE international conference on computer vision","first-page":"618","article-title":"Grad-CAM: Visual explanations from deep networks via gradient-based localization","author":"Selvaraju","year":"2017"},{"key":"10.1016\/j.ress.2026.113108_b57","series-title":"2021 IEEE\/CVF conference on computer vision and pattern recognition","first-page":"782","article-title":"Transformer interpretability beyond attention visualization","author":"Chefer","year":"2021"},{"key":"10.1016\/j.ress.2026.113108_b58","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2025.111777","article-title":"A generative dual-input model based on architectural computational optimization and multi-attention mechanism for remaining useful life prediction","volume":"266","author":"Shi","year":"2026","journal-title":"Reliab Eng Syst Saf","ISSN":"https:\/\/id.crossref.org\/issn\/0951-8320","issn-type":"print"},{"key":"10.1016\/j.ress.2026.113108_b59","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2025.112449","article-title":"A survey on graph neural networks for remaining useful life prediction: Methodologies, evaluation and future trends","volume":"229","author":"Wang","year":"2025","journal-title":"Mech Syst Signal Process","ISSN":"https:\/\/id.crossref.org\/issn\/0888-3270","issn-type":"print"},{"key":"10.1016\/j.ress.2026.113108_b60","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2023.109605","article-title":"PAOLTransformer: Pruning-adaptive optimal lightweight transformer model for aero-engine remaining useful life prediction","volume":"240","author":"Zhang","year":"2023","journal-title":"Reliab Eng Syst Saf","ISSN":"https:\/\/id.crossref.org\/issn\/0951-8320","issn-type":"print"}],"container-title":["Reliability Engineering &amp; System Safety"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0951832026009178?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0951832026009178?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T19:23:01Z","timestamp":1783624981000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0951832026009178"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2027,1]]},"references-count":60,"alternative-id":["S0951832026009178"],"URL":"https:\/\/doi.org\/10.1016\/j.ress.2026.113108","relation":{},"ISSN":["0951-8320"],"issn-type":[{"value":"0951-8320","type":"print"}],"subject":[],"published":{"date-parts":[[2027,1]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Learnable adaptive sequence compression and spatiotemporal fusion approach for aero-engine remaining useful life prediction","name":"articletitle","label":"Article Title"},{"value":"Reliability Engineering & System Safety","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ress.2026.113108","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":"113108"}}