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All issues Volume 5 / No 2 (2026) Natl Sci Open, 5 2 (2026) 20250060 References
Open Access
Issue
Natl Sci Open
Volume 5, Number 2, 2026
Article Number 20250060
Number of page(s) 16
Section Information Sciences
DOI https://doi.org/10.1360/nso/20250060
Published online 16 January 2026
  • Lenton TM. Early warning of climate tipping points. Nat Clim Change 2011; 1: 201-209. [Article] [Google Scholar]
  • Armstrong McKay DI, Staal A, Abrams JF, et al. Exceeding 1.5°C global warming could trigger multiple climate tipping points. Science 2022; 377: eabn7950. [Article] [Google Scholar]
  • Veraart AJ, Faassen EJ, Dakos V, et al. Recovery rates reflect distance to a tipping point in a living system. Nature 2012; 481: 357-359. [Article] [Google Scholar]
  • Cerini F, Childs DZ, Clements CF. A predictive timeline of wildlife population collapse. Nat Ecol Evol 2023; 7: 320-331. [Article] [Google Scholar]
  • Attia PM, Bills A, Brosa Planella F, et al. Review—“Knees” in lithium-ion battery aging trajectories. J Electrochem Soc 2022; 169: 060517. [Article] [Google Scholar]
  • Engle RF, Ruan T. Measuring the probability of a financial crisis. Proc Natl Acad Sci USA 2019; 116: 18341-18346. [Article] [Google Scholar]
  • Scheffer M, Bascompte J, Brock WA, et al. Early-warning signals for critical transitions. Nature 2009; 461: 53-59. [Article] [Google Scholar]
  • Grziwotz F, Chang CW, Dakos V, et al. Anticipating the occurrence and type of critical transitions. Sci Adv 2023; 9: eabq455. [Article] [Google Scholar]
  • Bury TM, Sujith RI, Pavithran I, et al. Deep learning for early warning signals of tipping points. Proc Natl Acad Sci USA 2021; 118: e2106140118. [Article] [Google Scholar]
  • Scheffer M, Carpenter SR, Lenton TM, et al. Anticipating critical transitions. Science 2012; 338: 344-348. [Article] [Google Scholar]
  • Kim H, Kim I, Kim M, et al. Detection of the knee point in lithium-ion battery degradation using a state-of-charge-dependent parameter. Proc Natl Acad Sci USA 2025; 122: e2424838122. [Article] [Google Scholar]
  • Gruver N, Finzi M, Qiu S, et al. Large language models are zero-shot time series forecasters. In: Proceedings of the 37th Conference on Neural Information Processing Systems (NeurIPS). New Orleans, 2023 [Google Scholar]
  • Zhou T, Niu P, Wang X, et al. One fits all: Power general time series analysis by pretrained LM. In: Proceedings of the 37th Conference on Neural Information Processing Systems (NeurIPS). New Orleans, 2023 [Google Scholar]
  • Jablonka KM, Schwaller P, Ortega-Guerrero A, et al. Leveraging large language models for predictive chemistry. Nat Mach Intell 2024; 6: 161-169. [Article] [Google Scholar]
  • Bran M. A, Cox S, Schilter O, et al. Augmenting large language models with chemistry tools. Nat Mach Intell 2024; 6: 525-535. [Article] [Google Scholar]
  • Madani A, Krause B, Greene ER, et al. Large language models generate functional protein sequences across diverse families. Nat Biotechnol 2023; 41: 1099-1106. [Article] [Google Scholar]
  • Romera-Paredes B, Barekatain M, Novikov A, et al. Mathematical discoveries from program search with large language models. Nature 2024; 625: 468-475. [Article] [Google Scholar]
  • Singhal K, Azizi S, Tu T, et al. Large language models encode clinical knowledge. Nature 2023; 620: 172-180. [Article] [Google Scholar]
  • Thirunavukarasu AJ, Ting DSJ, Elangovan K, et al. Large language models in medicine. Nat Med 2023; 29: 1930-1940. [Article] [Google Scholar]
  • Zeng F, Gan W, Wang Y, et al. Large language models for robotics: A survey. 2311.07226 [Google Scholar]
  • Jin M, Wang S, Ma L, et al. Time-LLM: Time series forecasting by reprogramming large language models. 2310.01728 [Google Scholar]
  • Yuan Y, Ma G, Cheng C, et al. A general end-to-end diagnosis framework for manufacturing systems. Natl Sci Rev 2020; 7: 418-429. [Article] [Google Scholar]
  • Yuan Y, Liu J, Jin D, et al. DeceFL: A principled fully decentralized federated learning framework. Natl Sci Open 2023; 2: 20220043. [Article] [Google Scholar]
  • Zhang H, Wang Q, Zhang W, et al. Estimating comparable distances to tipping points across mutualistic systems by scaled recovery rates. Nat Ecol Evol 2022; 6: 1524-1536. [Article] [Google Scholar]
  • Zheng Z, Ren X, Xue F, et al. Response length perception and sequence scheduling: An LLM-empowered LLM inference pipeline. In: Proceedings of the 37th Conference on Neural Information Processing Systems (NeurIPS). New Orleans, 2023 [Google Scholar]
  • Ma G, Xu S, Jiang B, et al. Real-time personalized health status prediction of lithium-ion batteries using deep transfer learning. Energy Environ Sci 2022; 15: 4083-4094. [Article] [Google Scholar]
  • Raffel C, Shazeer N, Roberts A, et al. Exploring the limits of transfer learning with a unified text-to-text transformer. J Mach Learn Res 2020; 21: 5485–5551 [Google Scholar]
  • Hu EJ, Shen Y, Wallis P, et al. LoRA: Low-rank adaptation of large language models. In: Proceedings of the10th International Conference on Learning Representations (ICLR). Online, 2022 [Google Scholar]
  • Severson KA, Attia PM, Jin N, et al. Data-driven prediction of battery cycle life before capacity degradation. Nat Energy 2019; 4: 383-391. [Article] [Google Scholar]
  • Zhang C, Wang Y, Gao Y, et al. Accelerated fading recognition for lithium-ion batteries with Nickel-Cobalt-Manganese cathode using quantile regression method. Appl Energy 2019; 256: 113841. [Article] [Google Scholar]
  • Gorton G. Financial crises. Annu Rev Financ Econ 2018; 10: 43-58. [Article] [Google Scholar]
  • Tölö E. Predicting systemic financial crises with recurrent neural networks. J Financ Stabil 2020; 49: 100746. [Article] [Google Scholar]
  • Chen S, Huang Y, Ge L. An early warning system for financial crises: A temporal convolutional network approach. Tech Econ Dev Eco 2024; 30: 688-711. [Article] [Google Scholar]
  • Jordà Ò, Schularick M, Taylor AM. Macrofinancial history and the new business cycle facts. NBER Macroecon Annu 2017; 31: 213–263 [Google Scholar]
  • Loss T, Colbrook MJ, Hansen AC. Stratified sampling based compressed sensing for structured signals. IEEE Trans Signal Process 2022; 70: 3530-3539. [Article] [Google Scholar]
  • Qiao G, Weiss BA. Accuracy degradation analysis for industrial robot systems. In: Proceedings of the 12th International Manufacturing Science and Engineering Conference. Los Angeles, 2017 [Google Scholar]
  • Schwertman NC, Owens MA, Adnan R. A simple more general boxplot method for identifying outliers. Comput Stat Data Anal 2004; 47: 165-174. [Article] [Google Scholar]

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