Results 151 to 160 of about 661 (227)

Model predictive control with inline parameter adaptation for direct crystal growth rate regulation

open access: yesAIChE Journal, EarlyView.
Abstract In batch cooling crystallization, many interactive factors, including supersaturation, reactor dimensions, and operating conditions, govern crystal growth and significantly influence product properties. Variables such as temperature or refractive index are used as surrogate control variables but have limitations in capturing growth rate ...
Huitian Yu, Jiewen Zhao, Heiko Briesen
wiley   +1 more source

Subclinical Auditory Neural Deficits in Patients With Type 1 Diabetes Mellitus. [PDF]

open access: yesEar Hear, 2020
AlJasser A   +3 more
europepmc   +1 more source

Lumped modeling of bubbling fluidized beds for high‐temperature processes

open access: yesAIChE Journal, EarlyView.
Abstract Bubbling fluidized beds (BFBs) have been a source of study for multiple decades. Among other applications, they can be used as scalable reactors for the conversion of biomass and waste resources to fuels. Several experimental and computational studies have been devoted to correlating the interplay of hydrodynamics and heat and mass transfer in
Ravi Ramesh   +3 more
wiley   +1 more source

LLM‐Based Scientific Assistants for Knowledge Extraction: Which Design Choices Matter?

open access: yesAdvanced Intelligent Discovery, EarlyView.
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

Autonomous AI‐Driven Design for Skin Product Formulations

open access: yesAdvanced Intelligent Discovery, EarlyView.
This review presents a comprehensive closed‐loop framework for autonomous skin product formulation design. By integrating artificial intelligence‐driven experiment selection with automated multi‐tiered assays, the approach shifts development from trial‐and‐error to intelligent optimisation.
Yu Zhang   +5 more
wiley   +1 more source

AI‐Driven Cancer Multi‐Omics: A Review From the Data Pipeline Perspective

open access: yesAdvanced Intelligent Discovery, EarlyView.
The exponential growth of cancer multi‐omics data brings opportunities and challenges for precision oncology. This review systematically examines AI's role in addressing these challenges, covering generative models, integration architectures, Explainable AI for clinical trust, clinical applications, and key directions for clinical translation.
Shilong Liu, Shunxiang Li, Kun Qian
wiley   +1 more source

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