VCformer: Variable-Centric Multi-Scale Transformer for Multivariate Time Series Forecasting. [PDF]
Sensors (Basel)Zhu J +6 more
europepmc +1 more source
Explaining the Origin of Negative Poisson's Ratio in Amorphous Networks With Machine Learning
Advanced Intelligent Discovery, EarlyView.This review summarizes how machine learning (ML) breaks the “vicious cycle” in designing auxetic amorphous networks. By transitioning from traditional “black‐box” optimization to an interpretable “AI‐Physics” closed‐loop paradigm, ML is shown to not only discover highly optimized structures—such as all‐convex polygon networks—but also unveil hidden ...
Shengyu Lu, Xiangying Shen
wiley +1 more source
Composition‐Aware Cross‐Sectional Integration for Spatial Transcriptomics
Advanced Intelligent Discovery, EarlyView.Multi‐section spatial transcriptomics demands coherent cell‐type deconvolution, domain detection, and batch correction, yet existing pipelines treat these tasks separately. FUSION unifies them within a composition‐aware latent framework, modeling reads as cell‐type–specific topics and clustering in embedding space.
Qishi Dong +5 more
wiley +1 more source
MMTransformer: a multivariate time-series resource forecasting model for multi-component applications. [PDF]
Sci RepCui G, Hu T, Zhang W, Bao H.
europepmc +1 more source
An Autonomous Large Language Model‐Agent Framework for Transparent and Local Time Series Forecasting
Advanced Intelligent Discovery, EarlyView.Architecture of the proposed large language model (LLM)‐based agent framework for autonomous time series forecasting in thermal power generation systems. The framework operates through a vertical pipeline initiated by natural language queries from users, which are processed by the LLM Agent Core powered by Llama.cpp and a ReAct loop with persistent ...
William Gouvêa Buratto +5 more
wiley +1 more source
A Critical Assessment of Bonding Descriptors for Predicting Materials Properties
Advanced Intelligent Discovery, EarlyView.The impact of new bonding descriptors in machine learning models for predicting material properties is assessed. Improvements are validated using significance tests, and new, intuitive descriptors for screening lattice thermal conductivity and projected force constants are introduced.
Aakash Ashok Naik +6 more
wiley +1 more source
4D hypercomplex-valued neural network in multivariate time series forecasting. [PDF]
Sci RepKycia R, Niemczynowicz A.
europepmc +1 more source
Advanced Intelligent Discovery, EarlyView.Machine learning serves as a central engine for the intelligent characterization of two‐dimensional materials by integrating multimodal techniques, including optical microscopy, spectroscopy, electron microscopy, and scanning probe microscopy (SPM). This unified framework enables automated, high‐throughput, and quantitative extraction of structural ...
Zhi‐Long Cao, Jia‐Xu Yan
wiley +1 more source
FCP-Former: Enhancing Long-Term Multivariate Time Series Forecasting with Frequency Compensation. [PDF]
Sensors (Basel)Li M +5 more
europepmc +1 more source
Dielectric Elastomer Actuators as Safe and Effective Tools for Mechanostimulation of Human Cells
Advanced Intelligent Systems, EarlyView.Replicating physiological forces is crucial for realistic cell models. Dielectric elastomer actuators (DEAs) offer a soft alternative, though their high voltages raised toxicity concerns. We demonstrate that DEA stimulation causes no cell damage, cell death or cell‐cycle disruption, while activating mechanosensitive responses.
Simon Holzer +6 more
wiley +1 more source