Results 21 to 30 of about 10,891 (200)
Organic Materials of Tomorrow: Horizons of Artificial Intelligence
This review examines machine learning techniques accelerating the discovery of organic semiconductors by linking molecular structure to properties. Key methods include graph neural networks, generative models, and active learning. Applications to organic photovoltaics demonstrate practical impact.
Harold Mena +3 more
wiley +1 more source
RHINO: An Integrative Multi‐Omics Framework Linking Circadian Physiology to Precision Medicine
RHINO (RHythmic Interacting Network for multi‐Omics) is an integrative framework that maps circadian regulation across diverse genetic and disease contexts and prioritizes druggable circadian targets. Released as an AI‐powered interactive web portal, RHINO unifies genetic, regulatory, disease, and drug–target information, enabling context‐specific ...
Ying Chen +12 more
wiley +1 more source
This perspective contrasts the historical, linear progression of early AI with the dynamic, iterative nature of AI 4.0; and it describes the real‐world medical applications and the necessary evolution of laboratory infrastructure brought about by AI 4.0.
Weida Liu, Gary Peltz
wiley +1 more source
Traditional chatbots lack the capability to correctly manage conversations according to the social context. However a dialogue is a joint activity that must consider both individual and social processes. In this work we propose a model of a social chatbot able to choose the most suitable dialogue plans according to what in sociological literature is ...
Augello A +3 more
openaire +5 more sources
This article provides a comprehensive analysis of transition metal phosphides (TMP) as electrocatalysts for urea‐assisted water electrolysis, highlighting the critical role of phosphorus engineering in modulating electronic structure, optimizing active sites, and enhancing catalytic durability.
Shivalingayya Gaddimath +8 more
wiley +1 more source
ABSTRACT This paper examines the determinants of generative AI (GenAI) knowledge and usage among agricultural extension professionals. Drawing on survey data from agricultural extension personnel in Tennessee, we employ regression analyses and latent Dirichlet allocation (LDA) for topic modeling of open‐ended responses to study the knowledge and usage ...
Abdelaziz Lawani +3 more
wiley +1 more source
Is Precision Agriculture Technology Adoption Persistently Overestimated?
ABSTRACT Precision agriculture is sometimes assumed to diffuse steadily over time, and industry planning frequently extrapolates early adoption trends forward. This study evaluates the accuracy of such expectations by comparing agricultural input dealers' forecasts of future service offerings with the actual levels of offerings that dealerships ...
Trey Malone +5 more
wiley +1 more source
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
Large Language Model‐Based Chatbots in Higher Education
The use of large language models (LLMs) in higher education can facilitate personalized learning experiences, advance asynchronized learning, and support instructors, students, and researchers across diverse fields. The development of regulations and guidelines that address ethical and legal issues is essential to ensure safe and responsible adaptation
Defne Yigci +4 more
wiley +1 more source
An agentic AI‐driven decision‐support framework for prosumers is proposed, integrating PV generation, load profiling, and multihorizon optimization within a four‐agent architecture. The approach significantly reduces grid dependence, enhances self‐sufficiency and prevents system oversizing.
Adela BÂRA, Simona‐Vasilica OPREA
wiley +1 more source

