{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T08:09:33Z","timestamp":1785312573840,"version":"3.55.0"},"reference-count":56,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T00:00:00Z","timestamp":1778803200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T00:00:00Z","timestamp":1785283200000},"content-version":"vor","delay-in-days":75,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022YFB3305302"],"award-info":[{"award-number":["2022YFB3305302"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J. King Saud Univ. Comput. Inf. Sci."],"published-print":{"date-parts":[[2026,8]]},"DOI":"10.1007\/s44443-026-00836-x","type":"journal-article","created":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T17:24:57Z","timestamp":1778865897000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Energy-efficient adaptive sampling for IIoT via deep perception and reinforcement learning"],"prefix":"10.1007","volume":"38","author":[{"given":"Shuangying","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peishun","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jintao","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyue","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruixin","family":"Hua","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,15]]},"reference":[{"key":"836_CR1","doi-asserted-by":"publisher","unstructured":"Algabroun H, H\u00e5kansson L (2025) Parametric machine learning-based adaptive sampling algorithm for efficient IoT data collection in environmental monitoring. J Netw Syst Manage 33(5):5. https:\/\/doi.org\/10.1007\/s10922-024-09881-1","DOI":"10.1007\/s10922-024-09881-1"},{"key":"836_CR2","unstructured":"authors TG (2016) GPyOpt: A bayesian optimization framework in python. http:\/\/github.com\/SheffieldML\/GPyOpt"},{"key":"836_CR3","doi-asserted-by":"crossref","unstructured":"Ben-Aboud Y, Licea DB, Ghogho M, Kobbane A (2021) On adaptive sampling algorithms for IoT devices. IEEE Int Conf Commun (ICC), pp 1\u20137","DOI":"10.1109\/ICC42927.2021.9500326"},{"key":"836_CR4","doi-asserted-by":"publisher","unstructured":"Bharal R, Krishna OVV (2021) Social media sentiment analysis using CNN-BiLSTM. Int J Sci Res 10(9):656\u2013661. https:\/\/doi.org\/10.21275\/SR21913110537","DOI":"10.21275\/SR21913110537"},{"key":"836_CR5","unstructured":"Cao D, Jia F, Ar\u0131k S \u00d6, Pfister T, Zheng Y, Ye W, Liu Y (2023) TEMPO: Prompt-based generative pre-trained transformer for time series forecasting. In: Proceedings of the international conference on learning representations (ICLR)"},{"key":"836_CR6","doi-asserted-by":"crossref","unstructured":"Cho K, van Merri\u00ebnboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y (2014) Learning phrase representations using RNN Encoder Decoder for statistical machine translation. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pp 1724\u20131734","DOI":"10.3115\/v1\/D14-1179"},{"issue":"1","key":"836_CR7","first-page":"3","volume":"6","author":"RB Cleveland","year":"1990","unstructured":"Cleveland RB, Cleveland WS, McRae JE, Terpenning I (1990) STL: A seasonal-trenddecomposition procedure based on Loess. Journal of Official Statistics 6(1):3\u201373","journal-title":"Journal of Official Statistics"},{"issue":"3","key":"836_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3598301","volume":"4","author":"BU Demirel","year":"2023","unstructured":"Demirel BU, Chen L, Al Faruque MA (2023) Data-driven energy-efficient adaptive sampling using Deep Reinforcement Learning. ACM Trans Comput Healthcare 4(3):1\u201319. https:\/\/doi.org\/10.1145\/3598301","journal-title":"ACM Trans Comput Healthcare"},{"key":"836_CR9","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1007\/s44443-025-00367-x","volume":"38","author":"K Dou","year":"2026","unstructured":"Dou K, Han Y, Li X, Zhang D (2026) A lightweight threshold signature scheme for internet of things devices. J King Saud Univ- Comput Inf Sci 38:38. https:\/\/doi.org\/10.1007\/s44443-025-00367-x","journal-title":"J King Saud Univ- Comput Inf Sci"},{"issue":"3","key":"836_CR10","doi-asserted-by":"publisher","first-page":"531","DOI":"10.1109\/TSP.2013.2288675","volume":"62","author":"K Dragomiretskiy","year":"2014","unstructured":"Dragomiretskiy K, Zosso D (2014) Variational Mode Decomposition. IEEE Trans Signal Process 62(3):531\u2013544. https:\/\/doi.org\/10.1109\/TSP.2013.2288675","journal-title":"IEEE Trans Signal Process"},{"key":"836_CR11","unstructured":"Eldele E, Ragab MChen Z, Wu M, Li X (2024) TSLANet: Rethinking Transformers for time series representation learning. In: Proceedings of the 41st international conference on machine learning (ICML) 235:12409\u201312428"},{"key":"836_CR12","doi-asserted-by":"publisher","first-page":"2319","DOI":"10.1007\/s10845-021-01765-4","volume":"32","author":"CA Escobar","year":"2021","unstructured":"Escobar CA, McGovern ME, Morales-Menendez R (2021) Quality 4.0: A review of big data challenges in manufacturing. J Intell Manuf 32:2319\u20132334. https:\/\/doi.org\/10.1007\/s10845-021-01765-4","journal-title":"J Intell Manuf"},{"issue":"1","key":"836_CR13","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1007\/s10618-023-00948-2","volume":"38","author":"NM Foumani","year":"2024","unstructured":"Foumani NM, Tan CW, Webb GI, Chen S, Rakshasbhuvankar A, Itskov L, Saleh G (2024) Improving position encoding of Transformers for multivariate time series classification. Data Min Knowl Disc 38(1):22\u201348. https:\/\/doi.org\/10.1007\/s10618-023-00948-2","journal-title":"Data Min Knowl Disc"},{"key":"836_CR14","doi-asserted-by":"crossref","unstructured":"Giouroukis D, Dadiani A, Traub J, Zeuch S, Markl V (2020) A survey of adaptive sampling and filtering algorithms for the Internet of Things. In: Proceedings of the 14th ACM international conference on distributed and event-based systems (DEBS) (pp 27\u201338)","DOI":"10.1145\/3401025.3403777"},{"key":"836_CR15","unstructured":"Haarnoja T, Zhou A, Abbeel P, Levine S (2018) Soft actor-critic: O-policy maximum entropy deep reinforcement learning with a stochastic actor. In: Proceedings of the 35th international conference on machine learning (ICML) (vol 80, pp 1861\u20131870)"},{"key":"836_CR16","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) (pp 770\u2013778)","DOI":"10.1109\/CVPR.2016.90"},{"issue":"8","key":"836_CR17","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J (1997) Long Short-Term Memory. Neural Comput 9(8):1735\u20131780. https:\/\/doi.org\/10.1162\/neco.1997.9.8.1735","journal-title":"Neural Comput"},{"key":"836_CR18","doi-asserted-by":"publisher","unstructured":"Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q, Liu HH (1998) The Empirical Mode Decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. In: Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences 454(1971):903\u2013995. https:\/\/doi.org\/10.1098\/rspa.1998.0193","DOI":"10.1098\/rspa.1998.0193"},{"key":"836_CR19","doi-asserted-by":"publisher","first-page":"1936","DOI":"10.1007\/s10618-020-00710-y","volume":"34","author":"H Ismail Fawaz","year":"2020","unstructured":"Ismail Fawaz H, Lucas B, Forestier G, Pelletier C, Schmidt DF, Weber J, Petitjean F (2020) InceptionTime: Finding AlexNet for time series classification. Data Min Knowl Disc 34:1936\u20131962. https:\/\/doi.org\/10.1007\/s10618-020-00710-y","journal-title":"Data Min Knowl Disc"},{"key":"836_CR20","unstructured":"Janner M, Fu J, Zhang M, Levine S (2019) When to trust your model: Model-based policy optimization. Adv Neural Inf Proc Syst (NeurIPS) (vol 32, pp 12498\u201312509)"},{"key":"836_CR21","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1016\/j.neunet.2019.04.014","volume":"116","author":"F Karim","year":"2019","unstructured":"Karim F, Majumdar S, Darabi H, Harford S (2019) Multivariate LSTM-FCNs for time series classification. Neural Netw 116:237\u2013245. https:\/\/doi.org\/10.1016\/j.neunet.2019.04.014","journal-title":"Neural Netw"},{"issue":"9","key":"836_CR22","doi-asserted-by":"publisher","first-page":"5422","DOI":"10.1021\/acs.jctc.2c00683","volume":"18","author":"DE Kleiman","year":"2022","unstructured":"Kleiman DE, Shukla D (2022) Multiagent Reinforcement Learning-based adaptive sampling for conformational dynamics of Proteins. J Chem Theory Comput 18(9):5422\u20135434. https:\/\/doi.org\/10.1021\/acs.jctc.2c00683","journal-title":"J Chem Theory Comput"},{"key":"836_CR23","doi-asserted-by":"crossref","unstructured":"Le X-M, Luo L, Aickelin U, Tran M-T (2024) ShapeFormer: Shapelet Transformer for multivariate time series classification. In: Proceedings of the 30th ACM SIGKDD conference on knowledge discovery and data mining (pp 1484\u20131494)","DOI":"10.1145\/3637528.3671862"},{"key":"836_CR24","doi-asserted-by":"crossref","unstructured":"Lemlouma T (2020) Adaptive sensing algorithm for IoT applications with data and temporal accuracy requirements. In: 2020 IEEE symposium on computers and communications (ISCC) (pp 1\u20137)","DOI":"10.1109\/ISCC50000.2020.9219699"},{"key":"836_CR25","unstructured":"Liu Y, Hu T, Zhang H,Wu H,Wang S, Ma L, Long M (2024) iTransformer: Inverted Transformers are effective for time series forecasting. International conference on learning representations (ICLR)"},{"key":"836_CR26","doi-asserted-by":"publisher","first-page":"597","DOI":"10.1016\/j.jmsy.2025.10.004","volume":"83","author":"Y Liu","year":"2025","unstructured":"Liu Y, Jian T, Guo X, Xu X, Polzer J (2025) Cognitive digital twin frameworks in manufacturing\u2013a critical survey, evaluation criteria, and future directions. J Manuf Syst 83:597\u2013611. https:\/\/doi.org\/10.1016\/j.jmsy.2025.10.004","journal-title":"J Manuf Syst"},{"key":"836_CR27","doi-asserted-by":"crossref","unstructured":"Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) (pp 3431\u20143440)","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"836_CR28","doi-asserted-by":"publisher","unstructured":"Matzka S (2020) AI4I 2020 Predictive Maintenance Dataset. UCI Machine Learning Repository. Retrieved from https:\/\/doi.org\/10.24432\/C5HS5C ([Dataset])","DOI":"10.24432\/C5HS5C"},{"key":"836_CR29","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1038\/nature14236","volume":"518","author":"V Mnih","year":"2015","unstructured":"Mnih V, Kavukcuoglu K, Silver D, Rusu AA, Veness J, Bellemare MG, Hassabis D (2015) Human-level control through deep reinforcement learning. Nature 518:529\u2013533. https:\/\/doi.org\/10.1038\/nature14236","journal-title":"Nature"},{"key":"836_CR30","unstructured":"Mnih V, Badia AP, Mirza M, Graves A, Lillicrap TP, Harley T, Kavukcuoglu K (2016) Asynchronous methods for deep reinforcement learning. In: Proceedings of the 33rd international conference on machine learning (ICML) (vol 48, pp 1928\u20131937)"},{"key":"836_CR31","unstructured":"Oreshkin BN, Carpov D, Chapados N, Bengio Y (2020) N-BEATS: Neural basis expansion analysis for interpretable time series forecasting. Int Conf Learn Represent (ICLR)"},{"key":"836_CR32","doi-asserted-by":"publisher","unstructured":"Pushpalatha S, Math S (2022) Hybrid deep learning framework for human activity recognition. Int J Nonlinear Anal Appl 13(1):1225\u20131237. https:\/\/doi.org\/10.22075\/ijnaa.2022.5673","DOI":"10.22075\/ijnaa.2022.5673"},{"issue":"8","key":"836_CR33","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0304655","volume":"19","author":"S Raja Sekaran","year":"2024","unstructured":"Raja Sekaran S, Pang YH, You LZ, Yin OS (2024) A hybrid TCN-GRU model for classifying human activities using smartphone inertial signals. PLoS ONE 19(8):e0304655. https:\/\/doi.org\/10.1371\/journal.pone.0304655","journal-title":"PLoS ONE"},{"key":"836_CR34","doi-asserted-by":"publisher","unstructured":"Reyes-Ortiz JL, Anguita D, Ghio A, Oneto L, Parra X (2013) Human activity recognition using smartphones. UCI Machine Learning Repository. Retrieved from https:\/\/doi.org\/10.24432\/C54S4K ([Dataset])","DOI":"10.24432\/C54S4K"},{"issue":"3","key":"836_CR35","doi-asserted-by":"publisher","first-page":"212","DOI":"10.1080\/00401706.1999.10485670","volume":"41","author":"PJ Rousseeuw","year":"1999","unstructured":"Rousseeuw PJ, Driessen KV (1999) A fast algorithm for the minimum covariance determinant estimator. Technometrics 41(3):212\u2013223. https:\/\/doi.org\/10.1080\/00401706.1999.10485670","journal-title":"Technometrics"},{"key":"836_CR36","doi-asserted-by":"crossref","unstructured":"Saltelli A, Ratto M, Andres T, Campolongo F, Cariboni J, Gatelli D, Tarantola S (2008) Global sensitivity analysis: The primer. John Wiley & Sons, Ltd","DOI":"10.1002\/9780470725184"},{"key":"836_CR37","unstructured":"Sch\u00f6lkopf B, Williamson RC, Smola AJ, Shawe-Taylor J, Platt JC (2000) Support vector method for novelty detection. Advances in neural information processing systems (NeurIPS) (pp 582\u2013588)"},{"key":"836_CR38","doi-asserted-by":"publisher","unstructured":"Schulman J, Wolski F, Dhariwal P, Radford A, Klimov O (2017) Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347, https:\/\/doi.org\/10.48550\/arXiv.1707.06347","DOI":"10.48550\/arXiv.1707.06347"},{"issue":"11","key":"836_CR39","doi-asserted-by":"publisher","first-page":"2673","DOI":"10.1109\/78.650093","volume":"45","author":"M Schuster","year":"1997","unstructured":"Schuster M, Paliwal K (1997) Bidirectional recurrent neural networks. IEEE Trans Signal Process 45(11):2673\u20132681. https:\/\/doi.org\/10.1109\/78.650093","journal-title":"IEEE Trans Signal Process"},{"key":"836_CR40","unstructured":"Trindade A (2015) Electricity Load Diagrams 2011\u20132014. UCI Machine Learning Repository. Retrieved from https:\/\/doi.org\/10.24432\/C58C86 ([Dataset])"},{"key":"836_CR41","doi-asserted-by":"crossref","unstructured":"van Hasselt H, Guez A, Silver D (2016) Deep reinforcement learning with double Q-learning. In: Proceedings of the AAAI conference on artificial intelligence (vol 30, pp 2094\u20132100)","DOI":"10.1609\/aaai.v30i1.10295"},{"key":"836_CR42","unstructured":"Wang Z, Schaul T, Hessel M, van Hasselt H, Lanctot M, de Freitas N (2016) Dueling network architectures for deep reinforcement learning. In: Proceedings of the 33rd international conference on machine learning (ICML) (Vol 48, pp 1995\u20132003)"},{"key":"836_CR43","doi-asserted-by":"publisher","unstructured":"Wang J, Tian Y, Hu X, Zhao X, Wang W, Fu Y, Zhang J (2024a) Development of grinding intelligent monitoring and big data-driven decision making expert system towards High Efficiency and Low Energy Consumption: experimental approach. J Intell Manuf 35:1013\u20131035. https:\/\/doi.org\/10.1007\/s10845-023-02089-1","DOI":"10.1007\/s10845-023-02089-1"},{"key":"836_CR44","doi-asserted-by":"publisher","unstructured":"Wang S, Lai X, He X, Li K, Lv L, Song X (2024b) Optimal sensor placement for Digital Twin based on mutual information and correlation with multifidelity data. Eng Comput 40:1289\u20131308. https:\/\/doi.org\/10.1007\/s00366-023-01858-z","DOI":"10.1007\/s00366-023-01858-z"},{"key":"836_CR45","unstructured":"Wang S, Wu H, Shi X, Hu T, Luo H, Ma L, Zhou J (2024c) TimeMixer: Decomposable multiscale mixing for time series forecasting. International conference on learning representations (ICLR)"},{"issue":"3","key":"836_CR46","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1007\/BF00992698","volume":"8","author":"CJCH Watkins","year":"1992","unstructured":"Watkins CJCH, Dayan P (1992) Q-learning. Mach Learn 8(3):279\u2013292. https:\/\/doi.org\/10.1007\/BF00992698","journal-title":"Mach Learn"},{"key":"836_CR47","unstructured":"Wu H, Xu J, Wang J, Long M (2021) Autoformer: Decomposition Transformers with Auto-Correlation for long-term series forecasting. Advances in Neural Information Processing Systems (NeurIPS) (vol 34, pp 22419\u201322430)"},{"issue":"2","key":"836_CR48","doi-asserted-by":"publisher","first-page":"369","DOI":"10.3390\/pr11020369","volume":"11","author":"W Yan","year":"2023","unstructured":"Yan W, Wang J, Lu S, Zhou M, Peng X (2023) A review of real-time fault diagnosis methods for industrial smart manufacturing. Processes 11(2):369. https:\/\/doi.org\/10.3390\/pr11020369","journal-title":"Processes"},{"key":"836_CR49","unstructured":"Yu G, Zou J, Hu X, Aviles-Rivero AI, Qin J, Wang S (2024) Revitalizing multivariate time series forecasting: Learnable decomposition with inter-series dependencies and intra-series variations modeling. In: Proceedings of the 41st international conference on machine learning (ICML) (vol 235, pp 57818\u201357841)"},{"issue":"23","key":"836_CR50","doi-asserted-by":"publisher","first-page":"9427","DOI":"10.3390\/s23239427","volume":"23","author":"H Zhang","year":"2023","unstructured":"Zhang H, Na J, Zhang B (2023) Autonomous Internet of Things (IoT) data reduction based on adaptive threshold. Sensors 23(23):9427. https:\/\/doi.org\/10.3390\/s23239427","journal-title":"Sensors"},{"key":"836_CR51","doi-asserted-by":"publisher","unstructured":"Zhang B, Yang G, Yu F, Pan Q (2025) SWT-CLSTM: A hybrid model for cloud workload prediction combining smooth wavelet transform and contrastive learning. J King Saud Univ Comput Inf Sci 37:320. https:\/\/doi.org\/10.1007\/s44443-025-00316-8","DOI":"10.1007\/s44443-025-00316-8"},{"key":"836_CR52","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2022.108824","volume":"229","author":"J Zhou","year":"2023","unstructured":"Zhou J, Li J (2023) IE-AK: A novel adaptive sampling strategy based on information entropy for Kriging in metamodel-based reliability analysis. Reliability Engineering & System Safety 229:108824. https:\/\/doi.org\/10.1016\/j.ress.2022.108824","journal-title":"Reliability Engineering & System Safety"},{"key":"836_CR53","doi-asserted-by":"crossref","unstructured":"Zhou H, Zhang S, Peng J, Zhang S, Li J, Xiong H, Zhang W (2021) Informer: Beyond efficient Transformer for long sequence time-series forecasting. In: Proceedings of the AAAI conference on artificial intelligence (vol 35, pp 11106\u201311115)","DOI":"10.1609\/aaai.v35i12.17325"},{"key":"836_CR54","unstructured":"Zhou T, Ma Z, Wen Q, Wang X, Sun L, Jin R (2022) FEDformer: Frequency enhanced decomposed Transformer for long-term series forecasting. In: Proceedings of the 39th international conference on machine learning (ICML) (vol 162, pp 27268\u201327286)"},{"issue":"1","key":"836_CR55","doi-asserted-by":"publisher","first-page":"13830","DOI":"10.1038\/s41598-025-98571-2","volume":"15","author":"H Zhou","year":"2025","unstructured":"Zhou H, Zhang X, Feng Y, Li L, Zhao J (2025) Efficient human activity recognition on edge devices using DeepConv LSTM architectures. Sci Rep 15(1):13830. https:\/\/doi.org\/10.1038\/s41598-025-98571-2","journal-title":"Sci Rep"},{"key":"836_CR56","doi-asserted-by":"publisher","first-page":"336","DOI":"10.1007\/s44443-025-00315-9","volume":"37","author":"P Zuo","year":"2025","unstructured":"Zuo P, Miao C, Fu C, Wang X, Liu X, Liu B (2025) SMAPPO: A security-aware multiagent reinforcement learning framework for secure computation offloading in SAGIN. J King Saud Univ Comput Inf Sci 37:336. https:\/\/doi.org\/10.1007\/s44443-025-00315-9","journal-title":"J King Saud Univ Comput Inf Sci"}],"container-title":["Journal of King Saud University Computer and Information Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44443-026-00836-x","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44443-026-00836-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44443-026-00836-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T07:49:57Z","timestamp":1785311397000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44443-026-00836-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,15]]},"references-count":56,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2026,8]]}},"alternative-id":["836"],"URL":"https:\/\/doi.org\/10.1007\/s44443-026-00836-x","relation":{},"ISSN":["1319-1578","2213-1248"],"issn-type":[{"value":"1319-1578","type":"print"},{"value":"2213-1248","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,15]]},"assertion":[{"value":"11 March 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 May 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 May 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"426"}}