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Advancements in experimental and modeling techniques allow for in-depth analysis of multiphysics phenomena in complex systems with unprecedented sophistication and details at discrete spatial and temporal scales. Energy systems are crucial for reliability, making health monitoring vital to prevent failures. Balancing experimental complexity and computational cost is challenging, leading to the need for predictive capabilities in prognostics and health monitoring (PHM). Using lithium-ion batteries as an example, we summarize PHM predictive modeling for remaining useful life, anomalies, and failure detection. Additionally, we introduce data-driven prognosis (DDP) as a new approach for detecting failures in such systems.

 

Dipyridyl molecular junctions often show intriguing conductance switching behaviors with mechanical modulations, but the mechanisms are still not completely revealed. By applying the ab initio -based adiabatic simulation method, the configuration evolution and electron transport properties of dipyridyl molecular junctions in stretching and compressing processes are systematically investigated. The numerical results reveal that the dipyridyl molecular junctions tend to form specific contact configurations during formation processes. In small electrode gaps, the pyridyls almost vertically adsorb on the second Au layers of the tip electrodes by pushing the top Au atoms aside. These specific contact configurations result in stronger molecule–electrode couplings and larger electronic incident cross-sectional areas, which consequently lead to large breaking forces and high conductance. On further elongating the molecular junctions, the pyridyls shift to the top Au atoms of the tip electrodes. The additional scattering of the top Au atoms dramatically decreases the conductance and switches the molecular junctions to the lower conductive states. Perfect cyclical conductance switches are obtained as observed in the experiments by repeatedly stretching and compressing the molecular junctions. The O atom in the side-group tends to hinder the pyridyl from adsorbing on the second Au layer and further inhibits the conductance switch of the dipyridyl molecular junction. 

Genome-wide profiling of chromatin accessibility by DNase-seq or ATAC-seq has been widely used to identify regulatory DNA elements and transcription factor binding sites. However, enzymatic DNA cleavage exhibits intrinsic sequence biases that confound chromatin accessibility profiling data analysis. Existing computational tools are limited in their ability to account for such intrinsic biases and not designed for analyzing single-cell data. Here, we present Simplex Encoded Linear Model for Accessible Chromatin (SELMA), a computational method for systematic estimation of intrinsic cleavage biases from genomic chromatin accessibility profiling data. We demonstrate that SELMA yields accurate and robust bias estimation from both bulk and single-cell DNase-seq and ATAC-seq data. SELMA can utilize internal mitochondrial DNA data to improve bias estimation. We show that transcription factor binding inference from DNase footprints can be improved by incorporating estimated biases using SELMA. Furthermore, we show strong effects of intrinsic biases in single-cell ATAC-seq data, and develop the first single-cell ATAC-seq intrinsic bias correction model to improve cell clustering. SELMA can enhance the performance of existing bioinformatics tools and improve the analysis of both bulk and single-cell chromatin accessibility sequencing data.