As Smart Cities development grows, deploying advanced technologies, such as the Internet
of Things (IoT), Cyber–Physical Systems, and particularly, Artificial Intelligence (AI) …

The high cost, low yield, and low stability of nanomaterials significantly hinder the
application and development of memristors. To promote the application of memristors …

In recent years, significant achievements have been made in time series forecasting using
deep learning methods, particularly the Long Short-Term Memory Network (LSTM) …

Accurate wind speed forecasting is essential to improve the scheduling and the utilization
ratio of wind power. However, it is challenging to accurately forecast the wind speed …

In wastewater treatment plants (WWTPs), the prediction of effluent ammonia nitrogen (NH4-
N) concentration is vital, which is a major cause of lake eutrophication. To solve this …

To fully utilize the advantage of reservoir computing in deep network modeling, a deep echo
state network with projection-encoding (DEESN) is newly proposed for multi-step time series …

In this study, we introduce an innovative method for load forecasting that capitalizes on the
concept of task affinity score to measure the similarity between various tasks. The task …

Developing a high-accuracy regional reconstitution model for the critical frequency of the
ionospheric F2 layer (foF2), utilizing data from ionospheric monitoring stations, is important …

Short-term electricity load forecasting plays a crucial role in modern container terminal. In
this work, we design a short-term forecasting approach aimed at port load under the …

Measuring nitrogen oxide (NOx) concentration accurately and timely is critical for pollution
control during cement clinker calcination. However, due to the harsh environment and …