Relieving the stress from energy demand is critical for encouraging the application of built
environment in agriculture, which is the most energy-intensive food-production sector. In this …

The inherent nonlinear and memory-dependent input-output characteristics of piezoelectric
actuators pose challenges to the precision of piezoelectric positioning systems. In order to …

With the increase of energy demand and the continuous development of renewable energy
technology, active distribution networks have become increasingly important. However, the …

Deep learning has been advocated as the predominant modeling method in the next-
generation green building energy systems for energy prediction, predictive maintenance …

With the increasing demand of passenger and freight air transportation and their key role in
energy consumptions, this study developed a hybrid framework integrating machine …

Axial compressors are susceptible to uncertainties during their manufacturing and operation,
resulting in reduced efficiency and performance dispersion. However, uncertainty …

Convolutional neural networks (CNNs) and long short-term memory networks (LSTMs) are
popular deep learning architectures currently used for rapid flood simulations. However …

The rapid advancement of highly flexible and reliable artificial intelligence (AI) holds the
promise of unlocking transformative capabilities in response to imminent energy and …

This study accurately inverts key growth parameters of rice, including Leaf Area Index (LAI),
chlorophyll content (SPAD) value, and height, by integrating multisource remote sensing …

Investigating methods for the detection of internal voids within road structures is a critical
measure to ensure the safety and integrity of roadway operations. The purpose of this …