Results 121 to 130 of about 30,985 (264)

Hybrid FSO/RF Networks with Neural Prediction of RSSI and Weather

open access: yesActa Electrotechnica et Informatica
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

open access: yesEnergy Science &Engineering, EarlyView.
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

Tree‐Boost–Guided CNN–BiLSTM–Transformer for Solar Irradiance Forecasting: Cross‐Regional Evidence for Sustainable Energy Planning

open access: yesEnergy Science &Engineering, EarlyView.
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

open access: yesEnergy Science &Engineering, EarlyView.
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

open access: yesJournal of Forecasting, EarlyView.
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

open access: yesJournal of Forecasting, EarlyView.
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

open access: yesIbrain, Volume 11, Issue 1, Page 32-43, Spring 2025.
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 as Physicochemical Encoders for Intelligent Liquid Recognition

open access: yesInterdisciplinary Materials, EarlyView.
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

open access: yesiMeta, EarlyView.
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

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