Results 221 to 230 of about 15,373 (263)

Integrated Microfluidic Platform for High‐Throughput Generation of Intestinal Organoids in Hydrogel Droplets

Advanced Science, EarlyView.
ABSTRACT Organoid research offers valuable insights into human biology and disease, but reproducibility and scalability remain significant challenges, particularly for epithelial organoids. Here, we present an integrated microfluidic platform that addresses these limitations by enabling high‐throughput generation of uniform hydrogel microparticles ...
Barbora Lavickova, Hannah Kronabitter, Mar Cervera‐Negueruela, Eylul Ceylan, Laura Benito‐Zarza, Rubén López‐Sandoval, Irineja Cubela, J. Gray Camp, Jose L. Garcia‐Cordero   +8 more
wiley   +1 more source

Nanodiamond Regulated Electrolyte Enhances Thermal, Chemical and Structural Properties for Highly Reversible Zn Metal Anodes

Advanced Science, EarlyView.
Nanodiamond additives are dispersed in the aqueous electrolyte to organize water molecules, suppress gas evolution and metal corrosion, and guide zinc to deposit more uniformly. Together with enhanced thermal conductivity for fast heat removal, this strategy reduces temperature rise and degradation, enabling safer, more durable rechargeable zinc metal ...
Jiayan Zhu, Xuan Gao, Nan Gao, Shaoheng Cheng, Junsong Liu, Yuhang Dai, Zhengxiao Guo, Hongdong Li   +7 more
wiley   +1 more source

Inside the Microreactor: In Situ Real‐Time Observation of Vapor–Liquid–Solid Growth of Monolayer TMDCs

Advanced Science, EarlyView.
In situ observation of VLS growth in a confined microreactor reveals various growth modes of monolayer TMDCs, including abnormal ribbon growth and particle‐driven growth. The confined space and precursor balance influence droplet behavior and growth dynamics, offering new insights into the controlled synthesis of TMDCs via the VLS mechanism.
Hiroo Suzuki, Yutaro Senda, Kaoru Hisama, Shuhei Aso, Yuta Takahashi, Shun Fujii, Yasuhiko Hayashi   +6 more
wiley   +1 more source

Arbitrary 3D Organic Mixed Ionic‐Electronic Conductor Architectures via Self‐Fusion of PEDOT:PSS Microfibers

Advanced Science, EarlyView.
A general fabricating strategy for arbitrary 3D organic mixed ionic‐electronic conductor architectures is reported using PEDOT:PSS microfiber building blocks. A water‐assisted self‐fusion process is successfully developed in which adhesion can be modulated as reversible (PSS‐rich) or irreversible (PEDOT‐rich) self‐fusion depending on the post‐treatment
Youngseok Kim, Jongwon Lee, Jiwoong Kim, Jung Il Yoo, Junggeon Park, Jaeyoung Lee, Heung Cho Ko, HyungJu Ahn, Myung‐Han Yoon   +8 more
wiley   +1 more source

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Tensor Decompositions and Applications

SIAM Review, 2009
Summary: This survey provides an overview of higher-order tensor decompositions, their applications, and available software. A tensor is a multidimensional or \(N\)-way array. Decompositions of higher-order tensors (i.e., \(N\)-way arrays with \(N \geq 3\)) have applications in psycho-metrics, chemometrics, signal processing, numerical linear algebra ...
Tamara G Kolda, Brett W Bader
exaly   +3 more sources

Time-Aware Tensor Decomposition for Sparse Tensors

2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA), 2021
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Dawon Ahn, Jun-Gi Jang, U Kang
openaire   +2 more sources

Random Tensor Theory for Tensor Decomposition

Proceedings of the AAAI Conference on Artificial Intelligence, 2022
We propose a new framework for tensor decomposition based on trace invariants, which are particular cases of tensor networks. In general, tensor networks are diagrams/graphs that specify a way to "multiply" a collection of tensors together to produce another tensor, matrix or scalar.
Ouerfelli, Mohamed, Tamaazousti, Mohamed, Rivasseau, Vincent   +2 more
openaire   +2 more sources

Tensor Decomposition Via Core Tensor Networks

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021
Tensor decomposition (TD) has shown promising performance in image completion and denoising. Existing methods always aim to decompose one tensor into latent factors or core tensors by optimizing a particular cost function based on a specific tensor model.
Jianfu ZHANG, ZERUI TAO, LIQING ZHANG, QIBIN ZHAO   +3 more
openaire   +1 more source

Multiscale tensor decomposition

2016 50th Asilomar Conference on Signals, Systems and Computers, 2016
Large datasets usually contain redundant information and summarizing these datasets is important for better data interpretation. Higher-order data reduction is usually achieved through low-rank tensor approximation which assumes that the data lies near a linear subspace across each mode.
Alp Ozdemir, Mark A. Iwen, Selin Aviyente   +2 more
openaire   +1 more source

Randomized Tensor Wheel Decomposition

SIAM Journal on Scientific Computing
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Mengyu Wang, Yajie Yu, Hanyu Li
openaire   +1 more source