Results 161 to 170 of about 78,741 (265)
LLM‐Based Scientific Assistants for Knowledge Extraction: Which Design Choices Matter?
A comprehensive framework for optimizing Large Language Models in domain‐specific applications is introduced. The LLM Playground integrates Prompt Engineering, knowledge augmentation, and advanced reasoning strategies to enable systematic comparison of architectures and base models.
David Exler +7 more
wiley +1 more source
La voie d'Aquitaine à Baziège (Haute-Garonne) et sa nouvelle borne milliaire
Christian Salmon
doaj +1 more source
Minimal Blood Pressure Effects of Intranasal Etripamil for Paroxysmal Supraventricular Tachycardia. [PDF]
Ip JE +8 more
europepmc +1 more source
Cell Segmentation Beyond 2D—A Review of the State‐of‐the‐Art
Cell segmentation underpins many biological image analysis tasks, yet most deep learning methods remain limited to 2D despite the inherently 3D nature of cellular processes. This review surveys segmentation approaches beyond 2D, comparing 2.5D and fully 3D methods, analyzing 31 models and 32 volumetric datasets, and introducing a unified reference ...
Fabian Schmeisser +6 more
wiley +1 more source
Mediating the Impact of Prenatal Cannabis Exposure on Neurobehavioral Disinhibition: The Role of Delayed Developmental Milestone Attainment. [PDF]
Ikeda AS +4 more
europepmc +1 more source
This article implements a unified human digital twin framework that integrates cutting edge actuation, sensing, simulation, and bidirectional feedback capability. The approach includes integrating multimodal sensing, AI, and biomechanical simulation into one compact system.
Tajbeed Ahmed Chowdhury +4 more
wiley +1 more source
Implementation and assessment of a novel interprofessional educational initiative in the medical intensive care unit: a mixed-methods evaluation. [PDF]
Foshee CM +6 more
europepmc +1 more source
Harnessing Machine Learning to Understand and Design Disordered Solids
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley +1 more source

