Fatigue life prediction is critical for ensuring the safe service and the structural integrity of
mechanical structures. Although data-driven approaches have been proven effective in …

Deep learning has transformed the use of machine learning technologies for the analysis of
large experimental datasets. In science, such datasets are typically generated by large-scale …

In the last few years, electron microscopy has experienced a new methodological paradigm
aimed to fix the bottlenecks and overcome the challenges of its analytical workflow. Machine …

Deep learning is transforming most areas of science and technology, including electron
microscopy. This review paper offers a practical perspective aimed at developers with …

Neutron and x-ray scattering represent two classes of state-of-the-art materials
characterization techniques that measure materials structural and dynamical properties with …

Sustainable development is a key challenge for contemporary human societies; failure to
achieve sustainability could threaten human survival. In this review article, we illustrate how …

Traditional Chinese medicine (TCM) is the treasure of China, and the quality control of TCM
is of crucial importance. In recent years, with the quick rise of artificial intelligence (AI) and …

Machine-assisted object detection and classification of fish species from Baited Remote
Underwater Video Station (BRUVS) surveys using deep learning algorithms presents an …

Deep Learning methods, such as Long Short-Term Memory (LSTM) neural networks prove
capable of predicting real-life time series data. Crucial for this technique to work is a …

Allosteric regulation is a common mechanism employed by complex biomolecular systems
for regulation of activity and adaptability in the cellular environment, serving as an effective …