Keywords
materials
structural
Abstract
This review examines multiview diagnostics across mechanical, clinical, and kinase evidence through an evidence-centered design lens. The analysis asks how each view should retain its uncertainty and provenance. It treats the relevant unit as a complete pathway from data or physical observations to representation, model output, human interpretation, and accountable action. The cited literature is synthesized without inventing experiments or unreported performance values. Particular attention is given to alignment suppressing rare but important evidence. The review argues that credible translation requires explicit evidence boundaries, uncertainty-aware evaluation, author-visible traceability, and a documented route for intervention. The resulting framework supports engineering and biomedical diagnostics while distinguishing component promise from system readiness.
References
Bengio, Y., Louradour, J., Collobert, R., & Weston, J. (2009). Curriculum learning. In Proceedings of the 26th International Conference on Machine Learning (pp. 41-48).
Berman, H. M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T. N., Weissig, H., Shindyalov, I. N., & Bourne, P. E. (2000). The Protein Data Bank. Nucleic Acids Research, 28(1), 235-242. https://doi.org/10.1093/nar/28.1.235
Deng, H., Luo, H., Zhu, Y., Li, L., Chen, Z., Zhao, X., Li, M., Zhang, J., Wang, M., Cao, Y., & Kang, Y. (2026). IIB-LPO: Latent policy optimization via iterative information bottleneck. arXiv preprint arXiv:2601.05870.
Fang, R., Zhao, J., Wang, S., Pu, R., Li, B., Cai, J., ... & Wang, B. (2025). Saga: Structural aggregation guided alignment with dynamic view and neighborhood order selection for multiview graph domain adaptation. In The Fourteenth International Conference on Learning Representations.
Han, Z., Chen, W., Han, Y., Mao, R., & Qin, J. (2026). Fast diversified top-k rule discovery via user-guided embeddings. IEEE Transactions on Knowledge and Data Engineering, 38, 1739–1753.
Jardine, A. K. S., Lin, D., & Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, 20(7), 1483-1510. https://doi.org/10.1016/j.ymssp.2005.09.012
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., ... & Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71
Parziale, M., Yeung, Y. F., Youcef-Toumi, K., Giglio, M., & Cadini, F. (2026). Anomaly characterization for the condition monitoring of rotating shafts exploiting data fusion and explainable convolutional neural networks. Structural Health Monitoring, 25(3), 1678–1698.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30.
Ye, M., Lin, X., Liu, W., Calatrava, J., Huang, W., & Wang, H.-L. (2025). Residual ridge height as a potential risk factor for membrane perforation during lateral-window sinus elevation surgery: A systematic review and meta-analysis. BMC Oral Health, 25, 1522.