As the world grapples with the energy crisis and environmental concerns, the focus on renewable energy sources has intensified. Lithium-ion batteries, with their high energy density and low pollution, ...

As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models designed to process graph-structured data. Currently, GNNs are effective ...

Beijing, Feb. 06, 2026 (GLOBE NEWSWIRE) -- WiMi Releases Hybrid Quantum-Classical Neural Network (H-QNN) Technology for Efficient MNIST Binary Image Classification ...

Neural networks have revolutionised the landscape of machine learning, yielding unprecedented performance in complex tasks ranging from image recognition to natural language processing. At the heart ...

Deep learning is increasingly used in financial modeling, but its lack of transparency raises risks. Using the well-known Heston option pricing model as a benchmark, researchers show that global ...

We propose a new machine-learning-based approach for forecasting Value-at-Risk (VaR) named CoFiE-NN where a neural network (NN) is combined with Cornish-Fisher expansions (CoFiE). CoFiE-NN can capture ...

Accurately tracking atmospheric greenhouse gases requires not only fast predictions but also reliable estimates of ...

New forms of fentanyl are created every day. For law enforcement, that poses a challenge: How do you identify a chemical you've never seen before? Researchers at Lawrence Livermore National Laboratory ...

The semiconductor industry is entering an era of unprecedented complexity, driven by advanced architectures such as Gate-All-Around (GAA) transistors, wide-bandgap materials like GaN and SiC, and ...

Over the last several decades, urban planners and municipalities have sought to identify and better manage the socioeconomic dynamics associated with rapid development in established neighborhoods.