Results 41 to 50 of about 975 (221)

Combining Spatial Multi‐Omics Data to Decipher Spatial Domains and Elucidate Cell Heterogeneity Based on Self‐Supervised Graph Learning

Advanced Science, EarlyView.
A self‐supervised multi‐view graph fusion framework integrates spatial multi‐omics, excelling in domain identification and denoising. It reconstructs spatial pseudo‐expression, jointly analyzes multi‐omics data, infers RNA velocity, predicts spatial omics features from single‐cell multi‐omics, and detects spatially dark genes and transcription factors,
Yuejing Lu, Rui Qiao, Ying Li, Junhong Li, Nuoya Yue, Jing Ge, Jiao Wang, Luonan Chen, Peiluan Li   +8 more
wiley   +1 more source

The adaptive composite trapezoidal rule for Hadamard finite-part integrals on an interval

Journal of Computational and Applied Mathematics, 2017
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Dongjie Liu, Jiming Wu, Xiaoping Zhang 0005   +2 more
openaire   +1 more source

Hadamard Matrix Slicing Single‐Pixel Imaging: Establishing a Linear‐Array‐Inspired Paradigm for N‐fold Acceleration Single‐Pixel Imaging

Advanced Science, EarlyView.
To overcome two‐dimensional modulation bottlenecks, Hadamard Matrix Slicing Single‐Pixel Imaging (HMS‐SPI) establishes an efficient one‐dimensional imaging paradigm. By slicing the traditional Hadamard matrix into one‐dimensional encoding vectors and spatially expanding them, the required measurement patterns decrease by a factor of N.
Xiaoxue Li, Jieting Hou, Qizheng Zhao, Yiqi Jia, Xiang Zhong, Mengchao Ma, Huaxia Deng, Xinglong Gong, Ziwei Wang   +8 more
wiley   +1 more source

Resolving Crowded NMR Spectra Based on Longitudinal Multi-spin Order and Hadamard Encoding

Chinese Journal of Magnetic Resonance
One-dimensional proton nuclear magnetic resonance (NMR) spectroscopy is a high-resolution, non-invasive technique widely used for structure elucidation and composition analysis.
LI Xueting, LIANG Wei, ZHANG Xintong, CUI Mengqi, LIN Yulan   +4 more
doaj   +1 more source

A Phase‐Resolved Geometric Deep Learning Framework Maps Structural Determinants of Disease‐Associated Protein Aggregation and Guides Suppressor Design

Advanced Science, EarlyView.
SKALE 2.0 maps disease‐associated protein aggregation as a phase‐resolved structural process, linking mutation‐induced geometric perturbations to nucleation, elongation, and suppressor design. Across neurodegenerative proteins, the framework reveals cryptic aggregation vulnerabilities, separates phase‐concordant and phase‐switching mutations, and ...
Jia Shen Sio, Wei Xuan Wilson Loo, Yan Shan Loo, Wen Xin Tan, Hui Xuan Lim, Huitao Liu, Chen Seng Ng   +6 more
wiley   +1 more source

Mechanisms of Alkali Ionic Transport in Amorphous Oxyhalides Solid State Conductors

Advanced Energy Materials, EarlyView.
Large‐scale machine learning‐based molecular dynamics simulations are used to investigate isovalent amorphous oxyhalides, revealing a remarkable chemically independent ionic conductivity. A rigorous analysis of alkali residence times across different metal–anion environments identifies divalent anions as key diffusion bottlenecks.
Luca Binci, KyuJung Jun, Bowen Deng, Gerbrand Ceder   +3 more
wiley   +1 more source

A family of optimal constant-composition codes

, 2005
Constant-composition codes are a special class of constant-weight codes with very strong constraints. It is hard to construct optimal constant-composition codes. There are only a few classes of such optimal codes in the literature. In this correspondence,
Ding, Cunsheng, Yuan, Jin
core   +1 more source

Exploring Quantum Support Vector Regression for Predicting Hydrogen Storage Capacity of Nanoporous Materials

Advanced Intelligent Discovery, EarlyView.
In this study we employed support vector regressor and quantum support vector regressor to predict the hydrogen storage capacity of metal–organic frameworks using structural and physicochemical descriptors. This study presents a comparative analysis of classical support vector regression (SVR) and quantum support vector regression (QSVR) in predicting ...
Chandra Chowdhury
wiley   +1 more source

METOD FOR INCREASING THE SPECTRAL EFFICIENCY OF TROPOSCATTER COMMUNICATION BASED ON THE USE OF COMPOSITE SIGNALS IN THE WALSH BASIS

Information and Telecommunication Sciences
Background. In modern telecommunications, troposcatter communication systems organize long-distance communication. These systems allow communication beyond the line of sight.
Сергій Капштик, Теодор Наритник   +1 more
doaj   +1 more source

Deep Learning Prediction of Surface Roughness in Multi‐Stage Microneedle Fabrication: A Long Short‐Term Memory‐Recurrent Neural Network Approach

Advanced Intelligent Discovery, EarlyView.
A sequential deep learning framework is developed to model surface roughness progression in multi‐stage microneedle fabrication. Using real‐world experimental data from 3D printing, molding, and casting stages, an long short‐term memory‐based recurrent neural network captures the cumulative influence of geometric parameters and intermediate outputs ...
Abdollah Ahmadpour, Sara Soltanabadi Farshi, Tuanna Ozcan, Amir Mohammad Ketabchi, Alper Kiraz, Savas Tasoglu   +5 more
wiley   +1 more source