Results 201 to 210 of about 93,376 (303)

Medical irregular multivariate time series forecasting based on multi-scale temporal-frequency domain patch fusion and dynamic graph. [PDF]

Front Physiol
Liu X, Gong T, Li Y, Li N, Ding S.
europepmc   +1 more source

What Do Large Language Models Know About Materials?

Advanced Engineering Materials, EarlyView.
If large language models (LLMs) are to be used inside the material discovery and engineering process, they must be benchmarked for the accurateness of intrinsic material knowledge. The current work introduces 1) a reasoning process through the processing–structure–property–performance chain and 2) a tool for benchmarking knowledge of LLMs concerning ...
Adrian Ehrenhofer, Thomas Wallmersperger, Gianaurelio Cuniberti   +2 more
wiley   +1 more source

Time series forecasting for bug resolution using machine learning and deep learning models. [PDF]

Front Big Data
Aversano L, Iammarino M, Madau A, Pecorelli F.   +3 more
europepmc   +1 more source

NFDI MatWerk Ontology (MWO): A BFO‐Compliant Ontology for Research Data Management in Materials Science and Engineering

Advanced Engineering Materials, EarlyView.
This article presents the NFDI‐MatWerk Ontology (MWO), a Basic Formal Ontology‐based framework for interoperable research data management in materials science and engineering (MSE). Covering consortium structures, research data management resources, services, and instruments, MWO enables semantic integration, Findable, Accessible, Interoperable, and ...
Hossein Beygi Nasrabadi, Ebrahim Norouzi, Kostiantyn Hubaiev, Jörg Waitelonis, Harald Sack   +4 more
wiley   +1 more source

Quantum-enhanced dual-layer graph attention network for time-series forecasting. [PDF]

Sci Rep
Tang Y, Cai Z, Zhang Y, Gao Z, Yu J.
europepmc   +1 more source

Latvian GDP: The Optimal Time Series Forecasting Algorithm

In this work an algorithm is developed for finding optimal time series model for GDP forecasting. Latvian GDP data with quarterly observation frequency is taken as time series. ARMA Analysis of Latvian GDP time series is performed.
Bezručko, Aleksandrs
core  

Microstructural Evolution and Vacancy Defect Formation in Mn–Mo–Ni RPV Steel Under Low Cycle Fatigue: Insights From EBSD and PALS

Advanced Engineering Materials, EarlyView.
Low‐cycle fatigue damage in Mn–Mo–Ni reactor pressure vessel steel is examined using a combined electron backscatter diffraction and positron annihilation lifetime spectroscopy approach. The study correlates texture evolution, dislocation substructure development, and vacancy‐type defect formation across uniform, necked, and fracture regions, providing
Apu Sarkar, Dhanadeep Dutta, G. Bharat Reddy   +2 more
wiley   +1 more source

Accurate time-series forecasting of floating platform motion via a reinforced fusion CNN-BiLSTM-attention model. [PDF]

PLoS One
Zheng H, Wang S, Li S, Lu K, Wang X, Liu Y, Wu X.   +6 more
europepmc   +1 more source

A Numerical–Experimental Approach for Multi‐Matrix Fiber‐Reinforced Plastics Characterization Using Finite Element Model Updating

Advanced Engineering Materials, EarlyView.
A numerical–experimental framework is developed for characterizing multi‐matrix fiber‐reinforced polymers (MM‐FRPs) combining epoxy and polyurethane matrices. Harmonic bending tests are integrated with finite element model updating (FEMU) to simultaneously identify elastic and viscoelastic material parameters.
Rodrigo M. Dartora, Tales V. Lisbôa, Axel Spickenheuer, Rogério J. Marczak, Jakson M. Vassoler   +4 more
wiley   +1 more source

Foundation Models for Generative AI in Time-Series Forecasting. [PDF]

J Med Internet Res
Beltramin D, Bousquet C.
europepmc   +1 more source