Results 41 to 50 of about 278,436 (276)

Multi-Stage Frequency Attention Network for Progressive Optical Remote Sensing Cloud Removal

Remote Sensing
Cloud contamination significantly impairs optical remote sensing images (RSIs), reducing their utility for Earth observation. The traditional cloud removal techniques, often reliant on deep learning, generally aim for holistic image reconstruction, which
Caifeng Wu, Feng Xu, Xin Li, Xinyuan Wang, Zhennan Xu, Yiwei Fang, Xin Lyu   +6 more
doaj   +1 more source

Atomic Size Misfit for Electrocatalytic Small Molecule Activation

Advanced Functional Materials, EarlyView.
This review explores the application and mechanisms of atomic size misfit in catalysis for small molecule activation, focusing on how structural defects and electronic properties can effectively lower the energy barriers of chemical bonds in molecules like H2O, CO2, and N2.
Ping Hong, Changfan Xu, Huaping Zhao, Yong Lei   +3 more
wiley   +1 more source

Multisource Topographic-Enhanced Cloud Removal for Remote Sensing in Mountainous Landscapes

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
In mountainous landscapes, integrating topographical information is crucial for effective analysis and understanding. Remote sensing becomes indispensable in studying mountains, enabling the monitoring of critical aspects such as grassland degradation ...
Meryeme Boumahdi, Consuelo Gonzalo-Martin, Mario Lillo-Saavedra, Marcelo Somos-Valenzuela, Angel Garcia-Pedrero   +4 more
doaj   +1 more source

Denoising Diffusion Probabilistic Feature-Based Network for Cloud Removal in Sentinel-2 Imagery

Remote Sensing, 2023
Cloud contamination is a common issue that severely reduces the quality of optical satellite images in remote sensing fields. With the rapid development of deep learning technology, cloud contamination is expected to be addressed.
Ran Jing, Fuzhou Duan, Fengxian Lu, Miao Zhang, Wenji Zhao   +4 more
doaj   +1 more source

Enhancing CoFe Catalysts with V2CTX MXene‐Derived Materials for Anion Exchange Membrane Electrolyzers

Advanced Functional Materials, EarlyView.
MXene dervied CoFe composites show increased initial Oxygen Evolution Reaction (OER) activity compared to the pure CoFe and MXene in an Anion Exchange Membrane device. Vanadium vacancies in the MXene plays a role in increased OER activity and hinders Fe leaching in the AEM device over using the pure V2C MXene as a support material for the CoFe ...
Can Kaplan, Karuppasamy Dharmaraj, Thorsten Schultz, Leiqiang Qin, Ningjun Chen, Danielle A. Douglas‐Henry, Bastian Schmiedecke, Merve Buldu‐Akturk, Axel Zuber, Iris Dorbandt, Maximilian Reinhardt, Yael Rodriguez‐Ayllon, Yan Lu, Valeria Nicolosi, Norbert Koch, Johanna Rosen, Michelle P. Browne   +16 more
wiley   +1 more source

Unsupervised remote sensing image thin cloud removal method based on contrastive learning

IET Image Processing
Cloud removal algorithm is a crucial step of remote sensing image preprocessing. The current mainstream remote sensing image cloud removal algorithms are implemented based on deep learning, and most of them are supervised.
Zhan Cong Tan, Xiao Feng Du, Wang Man, Xiao Zhu Xie, Gui Song Wang, Qin Nie   +5 more
doaj   +1 more source

Z‐Scheme Water Splitting Systems Based on Solid‐State Electron Conductors

Advanced Functional Materials, EarlyView.
This review examines the latest advances in Z‐scheme overall water splitting (OWS) systems for solar hydrogen production. These systems consist of suspended or immobilized hydrogen evolution photocatalysts (HEPs) and oxygen evolution photocatalysts (OEPs).
Chen Gu, Takashi Hisatomi, Tsuyoshi Takata, Kazunari Domen   +3 more
wiley   +1 more source

Cloud Removal in Full-disk Solar Images Using Deep Learning

The Astrophysical Journal Supplement Series
Ground-based solar telescopes often encounter cloud interference during observations, leading to varying degrees of cloud cover in solar images. Existing cloud removal methods face several challenges, including incomplete cloud removal, insufficient ...
Zhenhong Shang, Peng Du, Zhenping Qiang, Runxin Li   +3 more
doaj   +1 more source

Missing Data Reconstruction in Remote Sensing image with a Unified Spatial-Temporal-Spectral Deep Convolutional Neural Network

, 2018
Because of the internal malfunction of satellite sensors and poor atmospheric conditions such as thick cloud, the acquired remote sensing data often suffer from missing information, i.e., the data usability is greatly reduced.
Li, Xinghua, Wei, Yancong, Yuan, Qiangqiang, Zeng, Chao, Zhang, Qiang   +4 more
core   +1 more source

Unleashing the Power of Machine Learning in Nanomedicine Formulation Development

Advanced Functional Materials, EarlyView.
A random forest machine learning model is able to make predictions on nanoparticle attributes of different nanomedicines (i.e. lipid nanoparticles, liposomes, or PLGA nanoparticles) based on microfluidic formulation parameters. Machine learning models are based on a database of nanoparticle formulations, and models are able to generate unique solutions
Thomas L. Moore, Cristiano Pesce, Antonietta Greco, Claudia Pisante, Greta Avancini, Valentina Di Francesco, Yosi Shamay, Paolo Decuzzi   +7 more
wiley   +1 more source