Keywords
materials
systems
health
Abstract
This review examines explainable channel ranking and resilience in multisensor catalyst platforms through a mechanism-to-decision framework. It asks which sensor channels materially improve localization of degradation. Evidence is organized around representation, validation, uncertainty, and operational control, with no invented experiments or unreported quantitative results. Particular attention is given to feature attribution being mistaken for causal diagnosis. The synthesis shows that credible translation depends on explicit system boundaries, source-level traceability, failure-aware evaluation, and revalidation triggers. These principles provide a disciplined basis for operando monitoring and resilient test platforms while keeping component promise distinct from system readiness.
References
Du, C., & Jin, K. (2025). Multi-object grounding via hierarchical contrastive Siamese transformers. In 2025 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). IEEE. https://doi.org/10.1109/IJCNN64981.2025.11228633
Feng, Q., Liu, M., Dui, H., Cai, B., Fan, D., Ren, Y., & Wang, Z. (2024). A general design-oriented resilience measurement and evaluation method for engineering systems: Resilience cube. Reliability Engineering & System Safety, 245, 110038. https://doi.org/10.1016/j.ress.2024.110038
Hashemi, S., Peng, G., Nourani, M., Nofal, O., & Harvey, J. (2025). AI-driven SEEG channel ranking for epileptogenic zone localization. In 2025 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 1-6). IEEE. https://doi.org/10.1109/EMBC58623.2025.11254564
Kipf, T. N., & Welling, M. (2017). Semi-supervised classification with graph convolutional networks. In International Conference on Learning Representations.
Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30.
Norskov, J. K., Rossmeisl, J., Logadottir, A., Lindqvist, L., Kitchin, J. R., Bligaard, T., & Jonsson, H. (2004). Origin of the overpotential for oxygen reduction at a fuel-cell cathode. The Journal of Physical Chemistry B, 108(46), 17886-17892. https://doi.org/10.1021/jp047349j
Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian processes for machine learning. MIT Press.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30.
Vieira, C. L. Z., Koutrakis, P., Huang, S., Grady, S., Hart, J. E., Coull, B. A., Laden, F., Requia, W., Schwartz, J., & Garshick, E. (2019). Short-term effects of particle gamma radiation activities on pulmonary function in COPD patients. Environmental Research, 175, 221-227. https://doi.org/10.1016/j.envres.2019.05.032
Xia, F., Li, B., An, B., Zachman, M. J., Xie, X., Liu, Y., Xu, S., Saha, S., Wu, Q., Gao, S., Abdul Razak, I. B., Brown, D. E., Ramani, V., Wang, R., Marks, T. J., Shao, Y., & Cheng, Y. (2024). Cooperative atomically dispersed Fe-N4 and Sn-Nx moieties for durable and more active oxygen electroreduction in fuel cells. Journal of the American Chemical Society, 146(49), 33569-33578. https://doi.org/10.1021/jacs.4c11121