Results 231 to 240 of about 135,368 (311)
Quantifying the global film festival circuit: Networks, diversity, and public value creation. [PDF]
Zemaityte V +4 more
europepmc +1 more source
AI in chemical engineering: From promise to practice
Abstract Artificial intelligence (AI) in chemical engineering has moved from promise to practice: physics‐aware (gray‐box) models are gaining traction, reinforcement learning complements model predictive control (MPC), and generative AI powers documentation, digitization, and safety workflows.
Jia Wei Chew +4 more
wiley +1 more source
Assessing the content and quality of gout-related videos on video platforms: A cross-sectional study. [PDF]
Ye X, Sun H, Jiang W, Zhu Y.
europepmc +1 more source
opXRD: Open Experimental Powder X‐Ray Diffraction Database
We introduce the Open Experimental Powder X‐ray Diffraction Database, the largest openly accessible collection of experimental powder diffractograms, comprising over 92,000 patterns collected across diverse material classes and experimental setups. Our ongoing effort aims to guide machine learning research toward fully automated analysis of pXRD data ...
Daniel Hollarek +23 more
wiley +1 more source
A unified multimodal learning framework for sentiment analysis and mental health indicators from YouTube videos. [PDF]
Satapathy P +7 more
europepmc +1 more source
THE LANGUAGE OF WAR IN THE CONTEMPORARY DOCUMENTARY PROSE
openaire +1 more source
CrossMatAgent is a multi‐agent framework that combines large language models and diffusion‐based generative AI to automate metamaterial design. By coordinating task‐specific agents—such as describer, architect, and builder—it transforms user‐provided image prompts into high‐fidelity, printable lattice patterns.
Jie Tian +12 more
wiley +1 more source
Assessing the quality, reliability, and transparency of YouTube videos on spiritual palliative care: a content analysis. [PDF]
Almobarak F.
europepmc +1 more source
Large Language Model in Materials Science: Roles, Challenges, and Strategic Outlook
Large language models (LLMs) are reshaping materials science. Acting as Oracle, Surrogate, Quant, and Arbiter, they now extract knowledge, predict properties, gauge risk, and steer decisions within a traceable loop. Overcoming data heterogeneity, hallucinations, and poor interpretability demands domain‐adapted models, cross‐modal data standards, and ...
Jinglan Zhang +4 more
wiley +1 more source

