Results 101 to 110 of about 48,988 (255)
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
PhosSight is a unified deep‐learning framework for phosphoproteome identification, featured by a phosphorylation‐aware detectability predictor. It improves identification sensitivity in DDA through deep re‐localization and rescoring, accelerates DIA searches by detectability‐guided spectral library pruning, and expands phosphoproteome coverage to ...
Ben Wang +10 more
wiley +1 more source
Adsorption of Forever Chemical Pollutants: The Physical Chemistry of PFAS Near Surfaces
Current adsorption‐based remediation techniques for removing per‐ and polyfluoroalkyl substances (PFAS) from water are limited by knowledge gaps on PFAS behavior near solid surfaces. This review provides a state of the art on theoretical and experimental aspects of PFAS adsorption.
Nada Ben Amor +3 more
wiley +1 more source
Primary Mathematics: Integrating Theory with Practice
Primary Mathematics: Integrating Theory with Practice provides a comprehensive introduction to teaching and learning mathematics in today's classrooms. Closely aligned with the Australian Curriculum: Mathematics, this text covers the core learning areas ...
Serow, Penelope +2 more
core
This perspective proposes a cohesive machine learning strategy to decode microplastic aging. It advocates for Federated Learning to dismantle global data silos and introduces the TRACE framework (TRansport, Aging, Corona, Ecotoxicity). By integrating physics‐informed modeling with causal discovery, this approach bridges the laboratory‐field gap to ...
Yaping Lyu +6 more
wiley +1 more source
AI‐Physics‐Experiment Trinity for Integrated Protein Dynamics Modeling
This review unites experiments, physics‐based simulations, and AI as a synergistic triad for protein dynamics modeling. It highlights integrative strategies, resolves sampling and forcefield bottlenecks, and outlines challenges and future directions for accurate, interpretable conformational ensemble prediction.
Chen Shi +4 more
wiley +1 more source
Learners of mathematics often struggle to balance the apparently conflicting demands for abstract thinking as well as (often simultaneous) concrete cognitive engagement. Conflicting demands of successful mathematical engagement have been addressed in the
Torr, Stuart, Craig, Tracy S
core
This study utilized alloy micro‐galvanic coupling design to regulate the release of essential elements, thereby programming immune responses and promoting regeneration. The sacrificial anodic process of Zn‐0.8Mg reduced Zn2+ release compared to the “large cathode‐small anode” coupling of Zn‐0.8Fe.
Chaoyang Sun +14 more
wiley +1 more source
Electrocatalytic Coupling Conversion of Methane by Dual‐Site Control in Nickel Oxyhydroxide
Electrocatalytic coupling conversion of methane (CH4) is accomplished on the nickel oxyhydroxide (NiOOH), wherein the Ni─O dual‐site is triggered via the proper electronic interaction, synergistically promoting the C─H activation and C─C formation, enabling a selective and efficient C2 product generation route under ambient conditions. ABSTRACT Methane
Kailong Lu +7 more
wiley +1 more source
SPADE integrates spatial transcriptomics with single‐cell RNA sequencing by using cell–cell communications (CCC) as a guide for spatial mapping. It improves cell‐type localization, enhances sparse gene‐expression signals, and reveals CCC programs at single‐spot resolution.
Xinyi Li, Ning Zhang, Zijie Jin
wiley +1 more source

