Results 131 to 140 of about 1,533 (240)

Disentanglement by Deranking and by Suppression of Correlation

open access: yesAdvanced Quantum Technologies, Volume 9, Issue 3, March 2026.
ABSTRACT The spontaneous disentanglement hypothesis is motivated by some outstanding issues in standard quantum mechanics, including the problem of quantum measurement. The current study compares between some possible methods that can be used to implement the hypothesis.
Eyal Buks
wiley   +1 more source

Toward Real‐Life Applications of Fiber Lithium‐Ion Batteries

open access: yesSmall, Volume 22, Issue 17, 20 March 2026.
Fiber‐shaped lithium‐ion batteries require effective encapsulation to achieve long‐term stability in wearable applications. This work discusses different encapsulation strategies and technologies, and summarizes how encapsulation affects capacity retention, efficiency, and internal resistance.
Mengli Wei   +4 more
wiley   +1 more source

A Generalization Error Bound of Physics‐Informed Neural Networks for Ecological Diffusion Models

open access: yesStat, Volume 15, Issue 1, March 2026.
ABSTRACT Ecological diffusion equations (EDEs) are partial differential equations (PDEs) that model spatiotemporal dynamics, often applied to wildlife diseases. Derived from ecological mechanisms, EDEs are useful for forecasting, inference, and decision‐making, such as guiding surveillance strategies for wildlife diseases.
Juan Francisco Mandujano Reyes   +4 more
wiley   +1 more source

Innovative Solitary Wave Solutions for the (3+1)-Dimensional Boussinesq Kadomtsev-Petviashvili-Type Equation Derived via the Improved Modified Extended Tanh-Function Method

open access: diamond
Wael W. Mohammed   +6 more
openalex   +2 more sources

TSG‐Net: A Multiscale Decomposition and Spatio‐Temporal Graph Neural Network Framework for High‐Precision Wind Power Forecasting

open access: yesWind Energy, Volume 29, Issue 3, March 2026.
ABSTRACT Wind energy's intermittency poses significant challenges for power grid stability. Existing forecasting methods exhibit notable limitations: traditional machine learning models struggle with long‐term temporal dependencies, while deep learning approaches often overlook spatial relationships among turbines.
YuChen Zhang
wiley   +1 more source

Quantifying Historical and Future Surface Soil Moisture Drying Using Deep Learning and Remote Sensing

open access: yesEarth's Future, Volume 14, Issue 3, March 2026.
Abstract Understanding historical and future surface soil moisture (SSM) drying is pivotal due to its close links with droughts, heatwaves, and wildfires, yet debates regarding its evolution persist. In this study, we leverage advanced deep learning techniques to fill gaps of remote sensing‐based SSM data during 1983–2020 and therefore use these gap ...
Yong Bo   +11 more
wiley   +1 more source

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