Results 241 to 250 of about 112,710 (299)

Interpretable Machine Learning Framework for Nb─Si Based Alloy Design with Enhanced Fracture Toughness

open access: yesAdvanced Science, EarlyView.
An interpretable machine learning framework integrating SHAP and PDP analysis identifies critical design descriptors from 139 physicochemical features for Nb─Si alloys. The framework achieves <7% prediction error and guides the discovery of Nb38.5Ti38.5Si3Zr18V2 alloy with 22.791 MPa·m1/2 fracture toughness, breaking the 20 MPa·m1/2 barrier.
Dezhi Chen   +7 more
wiley   +1 more source

Automated Extraction of Multicomponent Alloy Data Using Large Language Models for Sustainable Design

open access: yesAdvanced Science, EarlyView.
A large language model (LLM) based pipeline is developed to automatically extract a comprehensive and accurate multicomponent alloy database from literature corpus. The extracted dataset is integrated with sustainability indicators to identify potential alloys that outperform existing industrial benchmark materials in terms of both performance and ...
Aravindan Kamatchi Sundaram   +4 more
wiley   +1 more source

The KLT Kernel in Twistor Space. [PDF]

open access: yesCommun Math Phys
Adamo T, Klisch S.
europepmc   +1 more source

Single‐Cell RNA Editing Identifies T Cell ADAR1 as a Key Regulator of Immune Exhaustion and Anti‐PD‐1 Resistance in Colorectal Cancer

open access: yesAdvanced Science, EarlyView.
Single‐cell RNA editing analysis identifies ADAR1 as a regulator of dysfunctional T cell states in colorectal cancer. Elevated ADAR1 activity promotes T cell exhaustion and impairs antitumor immunity partly through TGF‐β‐SMAD signaling, contributing to anti‐PD‐1 resistance and highlighting T cell ADAR1 as a potential therapeutic target and biomarker ...
Da Kang   +10 more
wiley   +1 more source

Geometric Aspects of Entanglement. [PDF]

open access: yesEntropy (Basel)
De Simone L   +5 more
europepmc   +1 more source

Condition‐Associated Pattern Extraction and Recovery From Multi‐Condition Single‐Cell RNA‐seq Data With CAPER

open access: yesAdvanced Science, EarlyView.
Decoupling biological signals from unwanted variation in multi‑condition single‑cell RNA sequencing data remains challenging. CAPER disentangles condition‑associated biological effects from sample heterogeneity through matrix factorization, producing interpretable latent factors and a batch‑corrected expression matrix.
Ye Li   +6 more
wiley   +1 more source

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