Results 81 to 90 of about 9,506 (241)

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

CauFinder: Steering Cell‐State and Phenotype Transitions by Causal Disentanglement Learning

Advanced Science, EarlyView.
CauFinder combines causal disentanglement modeling and network control to prioritize causal drivers of cell‐state transitions from observational transcriptomic data. The framework separates transition‐relevant signals from spurious associations, nominates intervention targets across biological and disease contexts, and identifies DAAM1 as an actionable
Chengming Zhang, Zexi Chen, Yuanxiang Miao, Zuolin Shen, Deyu Cai, Shijie Tang, Yun Xue, Weifeng Guo, Hongbin Ji, Jian Liu, Kazuyuki Aihara, Luonan Chen   +11 more
wiley   +1 more source

An Artificial Neural Network for Compensation of MTPA Algorithm in Permanent Magnet Synchronous Machines

This study introduces a new method to calculate the most efficient point of operation of a Permanent Magnet Synchronous Machine (PMSM). The proposed method combines the characteristics of an analytical solution known as the Maximum Torque Per Ampere (MTPA) curve with compensation through an Artificial Neural Network (ANN).
Huerta, Guillermo, Huerta, Miguel, Norambuena, Margarita, Angulo, Alejandro   +3 more
openaire   +1 more source

Output Voltage Control of a Synchronous Buck DC/DC Converter Using Artificial Neural Networks

Algorithms
This article presents a neural network-based control method for maintaining the required output voltage of a synchronous buck converter. The goal was to replace a traditional PID controller with a neural network that calculates the duty cycle based on real-time data. Several versions of the neural network were tested.
Juraj Simko, Michal Prazenica, Roman Konarik, Slavomír Kascák, Peter Klco   +4 more
openaire   +1 more source

Causal‐Guided Ultra‐Long‐Term Time Series Forecasting Via Anticipated Covariates

Advanced Science, EarlyView.
Often treated as unknown, information from the future remains underutilized.We demonstrate that in a coupled dynamical system, providing the future state of the effect enables accurate forecasting of the cause for a long timesteps. A time series forecasting paradigm that introduces anticipated covariates to represent such known future states is ...
Jintong Zhao, Yufei Liu, Ruixi Huang, Zhongxue Gan, Siyang Leng   +4 more
wiley   +1 more source

Polarization Dynamics in Ferroelectrics: Insights Enabled by Machine Learning Molecular Dynamics

Advanced Science, EarlyView.
Machine learning molecular dynamics is presented as a route to capture polarization switching, domain wall kinetics, topological polar textures, and polar mechanical coupling beyond the limits of conventional atomistic methods. This Perspective surveys recent progress and identifies key methodological directions, including long‐range electrostatics ...
Dongyu Bai, Ri He, Junxian Liu, Liangzhi Kou   +3 more
wiley   +1 more source

Palmitoylation of PSD‐95 Orchestrates Learning‐Dependent Metaplasticity in the Amygdala and Fear Memory

Advanced Science, EarlyView.
This study reveals that fear learning specifically triggers PICK1/DHHC2‐dependent palmitoylation of PSD‐95 in the lateral amygdala. Fear conditioning induces the dissociation of DHHC5 from PSD‐95 and the association of PSD‐95 with DHHC2. DHHC2‐mediated palmitoylation of PSD‐95 is required for synaptic transmission and underlies fear learning–induced ...
Zu‐Cheng Shen, Yu‐Xuan Weng, Zhi‐Xuan Xia, Yue‐Ling Zhao, Qian‐Qian Ding, Si‐Ying Wang, Ting Cao, Xuan‐Ying Chen, Wu‐Cheng Tao, Shen Lin, Zhou Chen, Yi‐Xiao Luo   +11 more
wiley   +1 more source

Assessing Strengths and Limitations of Magnetoencephalography Source Imaging With Intracerebral EEG

Advanced Science, EarlyView.
Simultaneous MEG and stereotactic EEG (SEEG) recordings provide a direct validation framework for MEG source imaging in focal epilepsy. Virtual SEEG signals derived from MEG reconstructions reveal significant agreement with intracranial measures of spike localization, resting‐state oscillations, and functional connectivity, while also identifying ...
Jawata Afnan, Maria Fratello, Francesca Bonini, Samuel Medina Villalon, Zhengchen Cai, Jean‐Marc Lina, Jean‐Michel Badier, Fabrice Bartolomei, Jean Gotman, Christian G. Bénar, Christophe Grova   +10 more
wiley   +1 more source

Fully Programmable Slow Light Based on a Spinor Representation of Generalized Coupled‐Resonator‐Induced Transparency

Advanced Science, EarlyView.
A programmable coupled‐resonator‐induced transparency (CRIT) platform is proposed, generalizing classical electromagnetically induced transparency (EIT) via a spinor representation. By utilizing dual‐channel gauge fields and universal unitary operations, this architecture enables dynamically engineered, reconfigurable slow‐light bands on a silicon ...
Seungkyun Park, Beomjoon Chae, Hyungchul Park, Sunkyu Yu, Xianji Piao, Namkyoo Park   +5 more
wiley   +1 more source

Discovering Early‐Stage Gas Generation Kinetics Enables Thermal Runaway Early Warning in Lithium‐Ion Batteries

Advanced Science, EarlyView.
Operando gas diagnostics reveal a previously unrecognized chemical activation (CA) stage during thermal runaway in lithium‐ion batteries. A physics‐informed gas generation kinetics network (GGKNet) is developed to reconstruct reaction pathways and physical models automatically.
Jiabo Zhang, Qianzhen Guo, Shuaiqi Liu, Shuai Li, Xianghao Kong, Dong Han, Zhen Huang   +6 more
wiley   +1 more source