An EVCS load forecasting method is proposed by integrating sequence decomposition and deep learning techniques. First, the interpretable periodic and volatile load features of the multivariate load ...

Oceans are facing a multitude of climate-induced stresses including acidification, sea-level rise, warming waters, and ice melt. The study underscores that traditional monitoring methods, while ...

The integration of Artificial Intelligence (AI) tech-nologies into educational settings has paved the way for inno-vative teaching and learning approaches. In Software Engineering (SE) education, ...

The team used the advanced deep learning-based framework ProteinMPNN for the first time to expand the sequence space of synthetic binding proteins (SBPs).

Researchers have developed a deep learning model called LSTM-SAM that predicts extreme water levels from tropical cyclones more efficiently and accurately, especially in data-scarce coastal ...

To overcome these limitations, the research team developed LSTM-SAM, a deep-learning framework that analyzes patterns from past storms to predict water level rise during future storms.

UML Diagram for the DDD Example in Evans' Book This project uses UML diagrams to illustrate the structure and behavior of the DDD example—a cargo shipping system—from Eric Evans' book (Domain-Driven ...

Deep learning–based synthetic super-resolution magnetic resonance imaging (SynthMRI) may improve the quantitative lesion performance of portable low–field strength magnetic resonance imaging (LF-MRI).

Here we review recent advances in deep generative models for protein design, with a particular focus on sequence-structure co-generation methods. We describe the key methodological and evaluation ...