Results 121 to 130 of about 30,985 (264)
Hybrid FSO/RF Networks with Neural Prediction of RSSI and Weather
This paper investigates neural network models for predicting weather parameters and received signal strength indicator (RSSI) to enable adaptive handover in hybrid free space optics (FSO)/radio frequency (RF) systems.
Liščinská Zuzana +2 more
doaj +1 more source
A Comprehensive Review of AI‐Powered Energy Systems
The role of Artificial Intelligence (AI) in developing next‐generation energy systems is getting more day by day. Therefore, incorporating AI enables real‐time decision‐making and advanced grid management, which are essential for optimizing the use of intermittent renewable sources like wind and solar power.
Armin Razmjoo +5 more
wiley +1 more source
This graphical abstract illustrates a reproducible pipeline that combines gradient‐boosting‐based feature selection with a CNN–BiLSTM–Transformer model to forecast solar irradiance across multi‐site satellite and ground datasets, delivering robust, high‐accuracy predictions that support sustainable grid planning and reliable PV integration.
Muhammad Farhan Hanif +5 more
wiley +1 more source
Graph Neural Network‐Based Prediction of Building Energy Consumption
A graph neural network that encodes a multi‐zone building as a graph accurately predicts hourly cooling and heating loads across three distinct climates, outperforming Random Forest and XGBoost baselines and serving as a fast surrogate to EnergyPlus simulations for scalable building energy management.
Ali Maboudi Reveshti +4 more
wiley +1 more source
Point and Risk estImation Using an enSemble of Models for Nowcasting: PRISM‐Now
ABSTRACT We propose PRISM‐Now, a novel ensemble forecasting system for near‐term GDP projection. Recognizing that relevant economic information evolves over time, we treat forecasts from multiple base models as draws from a mixture distribution of “good” and “bad” estimates, whose composition changes continuously and cannot be identified ex ante.
Beomseok Seo, Hyungbae Cho, Dongjae Lee
wiley +1 more source
Predicting EU Emissions Allowance Prices Using Macroeconomic Indicators and Hybrid AI Models
ABSTRACT Predicting carbon allowance prices has grown more crucial in relation to carbon market regulation, financial strategy, and environmental policy development. This study examines a hybrid forecasting system that combines deep learning with ensemble machine learning models to forecast the price fluctuations of EU Emissions Allowance (EUAs) within
Saptarshi Ganguly +2 more
wiley +1 more source
Research progress on the depth of anesthesia monitoring based on the electroencephalogram
Electroencephalogram (EEG) can noninvasive, continuous, and real‐time monitor the state of brain electrical activity, and the monitoring of EEG can reflect changes in the depth of anesthesia (DOA). The development of artificial intelligence can enable anesthesiologists to extract, analyze, and quantify DOA from complex EEG data.
Xiaolan He, Tingting Li, Xiao Wang
wiley +1 more source
Solid–liquid triboelectric nanogenerators are conceptualized as dynamic physicochemical encoders that encode intrinsic liquid properties into distinguishable triboelectric fingerprints. This review provides a unified framework for these platforms, covering sensing mechanisms in droplet impact, continuous flow, and immersion modes.
Mingrui Wang +8 more
wiley +1 more source
Nanopore direct RNA sequencing and the epitranscriptome: Advances in mapping native RNA landscapes
Nanopore direct RNA sequencing advances transcriptomics by capturing full‐length transcripts and multiple RNA modifications; this review details its principles, workflows, tools, applications, challenges, and future research potential. Abstract Nanopore direct RNA sequencing (DRS) has transformed transcriptomics by enabling single‐molecule, long‐read ...
Tianyuan Zhang +27 more
wiley +1 more source

