Results 61 to 70 of about 4,486,622 (282)

Solar Cycle Prediction Using a Temporal Convolutional Network Deep-learning Model with a One-step Pattern

The Astrophysical Journal Supplement Series
The human living environment is influenced by intense solar activity that exhibits periodicity and regularity. Although many deep-learning models are currently used for solar cycle prediction, most of them are based on a multistep pattern.
Cui Zhao, Kun Liu, Shangbin Yang, Jinchao Xia, Jingxia Chen, Jie Ren, Shiyuan Liu, Fangyuan He   +7 more
doaj   +1 more source

PREDICTION OF MAIN PARAMETERS OF 24 SOLAR CYCLE

Odessa Astronomical Publications, 2017
%B It is shown that there are certain rules which connect a height of previous solar activity cycle with the entropy of the next one. For even and odd cycles these rules are asymmetrical.
O. V. Chumak
doaj   +1 more source

A New Declining Phase Precursor and an Early Prediction of Cycle 26 Maximum

The Astrophysical Journal
The overall level of solar activity as seen in the sunspot number (SSN) record paces both the state of the ionosphere and the amplitude and occurrence rates of extreme space weather events.
S. C. Chapman
doaj   +1 more source

Prediction of solar cycle 25: A non-linear approach

, 2018
Predicting the solar activity is an important task for space weather and solar physics. There are various approaches to predict the solar activity and these predictions are used in various areas such as planning space missions, approximating the ...
Yurchyshyn, V., Ozguc, A., Kilcik, A., Sarp, V., Rozelot, J. P.   +4 more
core   +1 more source

A solar cycle 25 prediction based on 4D-var data assimilation approach

, 2020
International audienceBased on our modern 4D-var data assimilation pipeline Solar Predict we present in this short proceeding paper our prediction for the next solar cycle 25.
Pui Hung, Ching, Fournier, Alexandre, Jouve, Laurène, Brun, Allan Sacha, Hazra, Soumitra, Strugarek, Antoine, Talagrand, Olivier   +6 more
core   +1 more source

Organic Materials of Tomorrow: Horizons of Artificial Intelligence

Advanced Materials, EarlyView.
This review examines machine learning techniques accelerating the discovery of organic semiconductors by linking molecular structure to properties. Key methods include graph neural networks, generative models, and active learning. Applications to organic photovoltaics demonstrate practical impact.
Harold Mena, J. Terence Blaskovits, Kun‐Han Lin, Denis Andrienko   +3 more
wiley   +1 more source

Large‐Scale Determination of Frontier Orbital Energies of Disordered Small‐Molecule Organic Semiconductors Using Exciplex Emission Spectra

Advanced Materials, EarlyView.
ABSTRACT Accurately knowing the frontier orbital energies of the structurally disordered small‐molecule organic semiconductors that are used in optoelectronic devices such as organic light‐emitting diodes is required to rationally improve their performance. Here, we show that these energies can be deduced with a large accuracy from the peak energies of
Christian B. McDonald, Hiroki Tomita, Giacomo Cotelli, Louka L. Brüll, Nanno G. de Rooij, Lucas R.L. Norg, Milo Clément, Reinder Coehoorn   +7 more
wiley   +1 more source

Solar Cycle Prediction at NOAA's Space Weather Prediction Center

Space Weather
Predicting the level of solar activity years or even decades into the future has been both one of the most pressing and one of the most formidable challenges of space weather forecasting since its scientific origins over a century ago.
Mark S. Miesch
doaj   +1 more source

Solar Cycle Key Node Prediction Using NodeFusion

Universe
The solar cycle (SC) is an important timescale for describing the long-term evolution of solar activity, and understanding its evolutionary patterns is of great significance for space weather research and related applications. However, due to the complex
Mengyao Zhang, Bo Liang
doaj   +1 more source

Phase Engineering of Nanomaterials (PEN): Evolution, Current Challenges, and Future Opportunities

Advanced Materials, EarlyView.
This review summarizes the synthesis, phase transition, advanced characterization spanning ex situ to in situ and operando techniques, and diverse applications of phase engineering of nanomaterials (PEN). It further outlines key challenges and future opportunities, such as phase stability, architecture control, and artificial intelligence (AI)‐driven ...
Ye Chen, Zhanxi Fan, Jingjie Ge, Qiyuan He, Zhuangchai Lai, Jiawei Liu, Chaoliang Tan, Hua Zhang   +7 more
wiley   +1 more source