Results 211 to 220 of about 3,016,747 (301)
A hardware-grounded energy taxonomy for comparing deep and Spiking Neural Network inference on edge platforms. [PDF]
El-Hafci M, Sabri MA, Aarab A.
europepmc +1 more source
Predicting extreme defects in additive manufacturing remains a key challenge limiting its structural reliability. This study proposes a statistical framework that integrates Extreme Value Theory with advanced process indicators to explore defect–process relationships and improve the estimation of critical defect sizes. The approach provides a basis for
Muhammad Muteeb Butt +8 more
wiley +1 more source
WVM-UNet: A Wavelet-Vision Mamba Framework for Enhanced Medical Image Segmentation. [PDF]
Yang Y, Gao W, Wu Z, Yang C.
europepmc +1 more source
What Do Large Language Models Know About Materials?
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 +2 more
wiley +1 more source
Assessing scale and predictive diversity in models for single-cell transcriptomics based on Geneformer. [PDF]
Chen J, Schmidt F, Henao R.
europepmc +1 more source
A Workflow to Accelerate Microstructure‐Sensitive Fatigue Life Predictions
This study introduces a workflow to accelerate predictions of microstructure‐sensitive fatigue life. Results from frameworks with varying levels of simplification are benchmarked against published reference results. The analysis reveals a trade‐off between accuracy and model complexity, offering researchers a practical guide for selecting the optimal ...
Luca Loiodice +2 more
wiley +1 more source
WSD-YOLO: A lightweight YOLO-based model with enhanced feature representation for maize pest detection. [PDF]
Qu S, Liu Z, Zhao H, Wu Y, Yang Y.
europepmc +1 more source
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 +4 more
wiley +1 more source

