Ventilator-associated pneumonia (VAP) is a leading cause of morbidity and mortality in critically ill patients receiving mechanical ventilation for over 48 hours. This study aims to explore the ...

GFedCL studies federated continual learning where multiple clients learn a sequence of tasks while coordinating through a central server. The code focuses on the main training traces and final task ...

Conventional temporal-based deep learning models often fail to extract inter- channel information from electromyographic (EMG) signals. Existing spatio-temporal approaches typically sequentially ...

This college project focuses on forecasting flood risk by modeling both spatial relationships between geographic locations and temporal changes in hydrological patterns. We use a spatio-temporal deep ...

ABSTRACT: The spatio-temporal evolution of wall-bounded turbulence is characterized by high nonlinearity, multi-scale dynamics, and chaotic nature, making its accurate prediction a significant ...

ABSTRACT: The spatio-temporal evolution of wall-bounded turbulence is characterized by high nonlinearity, multi-scale dynamics, and chaotic nature, making its accurate prediction a significant ...

Abstract: This paper proposes a spatio-temporal graph convolutional network incorporating knowledge graph embeddings for hydrological time series prediction. A knowledge graph is constructed to ...

Abstract: Traffic flow prediction is fundamental to intelligent transportation systems, requiring accurate modeling of complex spatio-temporal dependencies. Existing methods typically assume ...