Results 251 to 260 of about 46,112 (290)

Smart Exploration of Perovskite Photovoltaics: From AI Driven Discovery to Autonomous Laboratories

Advanced Energy Materials, EarlyView.
In this review, we summarize the fundamentals of AI in automated materials science, and review AI applications in perovskite solar cells. Then, we sum up recent progress in AI‐guided manufacturing optimization, and highlight AI‐driven high‐throughput and autonomous laboratories.
Wenning Chen, Zheyu Zhang, Kelvian T. Mularso, Bonghyun Jo, Hyun Suk Jung   +4 more
wiley   +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

Harnessing Large Language Models to Advance Microbiome Research: From Sequence Analysis to Clinical Applications

Advanced Intelligent Discovery, EarlyView.
Large language models are transforming microbiome research by enabling advanced sequence profiling, functional prediction, and association mining across complex datasets. They automate microbial classification and disease‐state recognition, improving cross‐study integration and clinical diagnostics.
Jieqi Xing, He Wang, Yangyang Sun, Xiaoquan Su, Shunyao Wu   +4 more
wiley   +1 more source

Personalized mapping of inhibitory spinal cord circuits in vivo via non-invasive neural decoding and in silico modelling

Pascual-Valdunciel A, Cónsul NT, Brownstone RM, Beato M, Farina D, Nascimento F, Özyurt MG.   +6 more
europepmc   +1 more source

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Recording and decoding for neural prostheses

Proceedings of the IEEE, 2016
This paper reviews technologies and signal processing algorithms for decoding peripheral nerve and electrocorticogram signals to interpret human intent and control prosthetic arms. The review includes a discussion of human motor system physiology and physiological signals that can be used to decode motor intent, electrode technology for acquiring ...
David J Warren, Spencer Kellis, Henrique Dantas   +2 more
exaly   +3 more sources

Neural Information Bottleneck Decoding

2020 14th International Conference on Signal Processing and Communication Systems (ICSPCS), 2020
Receiver-sided channel decoding is a crucial, but computationally very demanding task. Recently, information-bottleneck-based decoding received considerable attention in the literature, as it achieves very good performance with coarse quantization and low complexity.
Stark, Maximilian, Lewandowsky, Jan, Bauch, Gerhard   +2 more
core   +3 more sources

Neural decoding

, 2019
(A) Neural decoding accuracies were analyzed as the probability of correct decoding across time points along the stimulus duration and stimulus presentations. The black arrows schematically indicate the subsequent time point (left) and probability level (
David S. Vicario (7326128), Efe Soyman (7326125)   +1 more
openaire   +2 more sources

Neural Decoding: A Predictive Viewpoint

Neural Computation, 2017
Decoding in the context of brain-machine interface is a prediction problem, with the aim of retrieving the most accurate kinematic predictions attainable from the available neural signals. While selecting models that reduce the prediction error is done to various degrees, decoding has not received the attention that the fields of statistics and ...
Sonia Todorova, Valérie Ventura
openaire   +3 more sources

Decoding the neural correlates of consciousness

Current Opinion in Neurology, 2010
Multivariate pattern analysis (MVPA) is an emerging technique for analysing functional imaging data that is capable of a much closer approximation of neuronal activity than conventional methods. This review will outline the advantages, applications and limitations of MVPA in understanding the neural correlates of consciousness.MVPA has provided ...
Rimona S, Weil, Geraint, Rees
openaire   +2 more sources

On Robust Deep Neural Decoders

2019 53rd Asilomar Conference on Signals, Systems, and Computers, 2019
The design of neural-based decoders is well understood for classical point to point channels such as the Additive White Gaussian Noise (AWGN) channel, the Binary Symmetric Channel (BSC) and the Binary Erasure Channel (BEC). For such channels, an optimal training noise distribution allows the neural decoder to generalize to other channel parameters ...
Meryem Benammar, Pablo Piantanida
openaire   +1 more source