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Abstract Bayesian inference based on computational simulations plays a crucial role in model-informed damage diagnostics and the design of reliable engineering systems, such as the miter gates studied in this article. While Bayesian inference for damage diagnostics has shown success in some applications, the current method relies on monitoring data from solely the asset of interest and may be affected by imperfections in the computational simulation model. To address these limitations, this article introduces a novel approach called Bayesian inference-based damage diagnostics enhanced through domain translation (BiEDT). The proposed BiEDT framework incorporates historical damage inspection and monitoring data from similar yet different miter gates, aiming to provide alternative data-driven methods for damage diagnostics. The proposed framework first translates observations from different miter gates into a unified analysis domain using two domain translation techniques, namely, cycle-consistent generative adversarial network (CycleGAN) and domain-adversarial neural network (DANN). Following the domain translation, a conditional invertible neural network (cINN) is employed to estimate the damage state, with uncertainty quantified in a Bayesian manner. Additionally, a Bayesian model averaging and selection method is developed to integrate the posterior distributions from different methods and select the best model for decision-making. A practical miter gate structural system is employed to demonstrate the efficacy of the BiEDT framework. Results indicate that the alternative damage diagnostics approaches based on domain translation can effectively enhance the performance of Bayesian inference-based damage diagnostics using computational simulations. 

Miter gates are vital civil infrastructure components in inland waterway transportation networks. To provide risk-informed insights for decisions related to repair and maintenance, sensors have been installed on some miter gates for monitoring. Despite the monitoring system's ability in collecting a large volume of monitoring data, accurately diagnosing damage state in such large structures remains challenging due to the lack of labeled monitoring data, since these structures are designed with high reliability and for a long operation life. This paper addresses this challenge by proposing a damage diagnostics approach for miter gates based on domain adaptation. The proposed approach consists of two main modules. In the first module, Cycle-Consistent generative adversarial network (CycleGAN) is employed to map monitoring data of a miter gate of interest and other similar yet different miter gates into the same analysis domain. Subsequently, a normalizing flow-based likelihood-free inference model is constructed within this common domain using data from source miter gates whose damage states are labeled from historical inspections. The trained normalizing flow model is then used to predict the damage state of the target miter gate based on the translated monitoring data. A case study is presented to demonstrate the effectiveness of the proposed method. The results indicate that the proposed method in general can accurately estimate the damage state of the target miter gate in the presence of uncertainty. 

Abstract Diagnosing lithium-ion battery health and predicting future degradation is essential for driving design improvements in the laboratory and ensuring safe and reliable operation over a product’s expected lifetime. However, accurate battery health diagnostics and prognostics is challenging due to the unavoidable influence of cell-to-cell manufacturing variability and time-varying operating circumstances experienced in the field. Machine learning approaches informed by simulation, experiment, and field data show enormous promise to predict the evolution of battery health with use; however, until recently, the research community has focused on deterministic modeling methods, largely ignoring the cell-to-cell performance and aging variability inherent to all batteries. To truly make informed decisions regarding battery design in the lab or control strategies for the field, it is critical to characterize the uncertainty in a model’s predictions. After providing an overview of lithium-ion battery degradation, this paper reviews the current state-of-the-art probabilistic machine learning models for health diagnostics and prognostics. Details of the various methods, their advantages, and limitations are discussed in detail with a primary focus on probabilistic machine learning and uncertainty quantification. Last, future trends and opportunities for research and development are discussed. 

Abstract Plant cellulose microfibrils are increasingly employed to produce functional nanofibers and nanocrystals for biomaterials, but their catalytic formation and conversion mechanisms remain elusive. Here, we characterize length-reduced cellulose nanofibers assembly in situ accounting for the high density of amorphous cellulose regions in the natural rice fragile culm 16 ( Osfc16 ) mutant defective in cellulose biosynthesis using both classic and advanced atomic force microscopy (AFM) techniques equipped with a single-molecular recognition system. By employing individual types of cellulases, we observe efficient enzymatic catalysis modes in the mutant, due to amorphous and inner-broken cellulose chains elevated as breakpoints for initiating and completing cellulose hydrolyses into higher-yield fermentable sugars. Furthermore, effective chemical catalysis mode is examined in vitro for cellulose nanofibers conversion into nanocrystals with reduced dimensions. Our study addresses how plant cellulose substrates are digestible and convertible, revealing a strategy for precise engineering of cellulose substrates toward cost-effective biofuels and high-quality bioproducts. 

The FASER experiment is a new small and inexpensive experiment that is being placed 480 meters downstream of the ATLAS experiment at the CERN LHC. The experiment will shed light on currently unexplored phenomena, having the potential to make a revolutionary discovery. FASER is designed to capture decays of exotic particles, produced in the very forward region, out of the ATLAS detector acceptance. This talk will present the physics prospects, the detector design, and the construction progress of FASER. The experiment has been successfully installed and will take data during the LHC Run-3.