Results 141 to 150 of about 88,361 (282)

Layered Ti1‐xFexS2 Cathode Materials with Anionic Redox Chemistry for Mg Storage

Advanced Energy and Sustainability Research, EarlyView.
The predicted layered Ti‐Fe sulfides exhibit joint cationic and anionic redox and faster Mg2+ kinetics over pyrite forms and are promising cathodes for rechargeable Mg battery. Rechargeable magnesium batteries (RMBs) are a promising alternative to lithium‐ion batteries because of the high capacity and crustal abundance of magnesium.
Arup Chakraborty, Haoxiang Wang, Kaijie Yan, Zinuo Xu, Zejing Lin, Chengliang Wang, Liumin Suo, Minglei Mao, Dan T. Major   +8 more
wiley   +1 more source

Enhancing Robotic Antenna Measurements with Composite-Plane Range Extension and Localized Sparse Sampling. [PDF]

Sensors (Basel)
Fontá Romero C, Arboleya A, Rodríguez Varela F, Sierra Castañer M.   +3 more
europepmc   +1 more source

Domain‐Aware Implicit Network for Arbitrary‐Scale Remote Sensing Image Super‐Resolution

Advanced Intelligent Discovery, EarlyView.
Although existing arbitrary‐scale image super‐resolution methods are flexible to reconstruct images with arbitrary scales, the characteristic of training distribution is neglected that there exists domain shift between samples of various scales. In this work, a Domain‐Aware Implicit Network (DAIN) is proposed to handle it from the perspective of domain
Xiaoxuan Ren, Qian Jiang, Xin Jin, Puming Wang, Xing Chu, Shaowen Yao, Shengfa Miao   +6 more
wiley   +1 more source

Fusion of multi-source precipitation records via coordinate-based generative models. [PDF]

Nat Commun
Sun S, Nai C, Pan B, Li W, Li L, Li X, Foufoula-Georgiou E, Lin Y.   +7 more
europepmc   +1 more source

Predicting High‐Resolution Spatial and Spectral Features in Mass Spectrometry Imaging with Machine Learning and Multimodal Data Fusion

Advanced Intelligent Discovery, EarlyView.
A multimodal fusion pipeline predicts high‐resolution ion distributions in imaging mass spectrometry by integrating Fourier transform ion cyclotron resonance, time‐of‐flight matrix‐assisted laser desorption/ionization, and time‐of‐flight secondary ion mass spectrometry data.
Md Inzamam Ul Haque, Ramakrishnan Kannan, Jacob D. Hinkle, Sylwia A. Stopka, Anton V. Ievlev, Nathalie Y. R. Agar, Olga S. Ovchinnikova, Debangshu Mukherjee   +7 more
wiley   +1 more source

On the degrees of freedom of gridded control points in learning-based medical image registration. [PDF]

Med Phys
Yan W, Yang Q, Wang Y, Punwani S, Emberton M, Stavrinides V, Hu Y, Barratt D.   +7 more
europepmc   +1 more source

Decoding Tattoo and Permanent Makeup Pigments: Linking Physicochemical Properties to Absorption, Distribution, Metabolism, and Elimination Profiles Using Quantitative Structure–Activity Relationship (QSAR)‐Based New Approach Methodologies (NAMs)

Advanced Intelligent Discovery, EarlyView.
This study applies QSAR‐based new approach methodologies to 90 synthetic tattoo and permanent makeup pigments, revealing systemic links between their physicochemical properties and absorption, distribution, metabolism, and elimination profiles. The correlation‐driven analysis using SwissADME, ChemBCPP, and principal component analysis uncovers insights
Girija Bansod, Ajay Vikram Singh, Preeti Bhardwaj, Tulika Rai, Sweta Vijay Nakhale, Amruta Shelar, Rajendra Patil, Peter Laux, Andreas Luch, Christopher J. Osgood, Michael W. Stacey   +10 more
wiley   +1 more source

A study on the refined population spatialization method integrating multi-source data: A case study of Yuxi city. [PDF]

PLoS One
Zhang F, Jiang F, Zhang Y, Xu J, Ye Y, Jia Z.   +5 more
europepmc   +1 more source

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

Advanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source

Reconstruction of Antarctic sea ice thickness from sparse satellite laser altimetry data via deep learning. [PDF]

Sci Data
Ma Z, Yang Q, Xu Y, Shi W, Dong X, Shi Q, Luo H, Liu J, Uotila P, Nie Y.   +9 more
europepmc   +1 more source