Feedforward Control of Plant Nitrate Transporter NRT1.1 Biphasic Adaptive Activity. [PDF]
Rashid M +7 more
europepmc +1 more source
Machine Learning for Predictive Modeling in Nanomedicine‐Based Cancer Drug Delivery
The integration of AI/ML into nanomedicine offers a transformative approach to therapeutic design and optimization. Unlike conventional empirical methods, AI/ML models (such as classification, regression, and neural networks) enable the analysis of complex clinical and formulation datasets to predict optimal nanoparticle characteristics and therapeutic
Rohan Chand Sahu +3 more
wiley +1 more source
Hysteresis Modelling and Feedforward Control of Piezoelectric Actuator Based on Simplified Interval Type-2 Fuzzy System. [PDF]
Li PZ +4 more
europepmc +1 more source
In this work, three prediction machine learning (ML) models (MLP, RBF, BP) are developed to predict the ultimate tensile strength (UTS) and elongation (EL) of the AFSDed Al2219 samples. ABSTRACT Additive friction stir deposition (AFSD) is an effective method for fabricating high‐performance deposits, with process parameters directly influencing the ...
Chan Wa Tam +10 more
wiley +1 more source
Feedforward control with personalized nursing measures on respiratory function and psychological status in patients with advanced lung cancer. [PDF]
Dou Y, Guo J, Chen F.
europepmc +1 more source
Tuning of feedforward control enables stable muscle force-length dynamics after loss of autogenic proprioceptive feedback. [PDF]
Gordon JC +3 more
europepmc +1 more source
Predefined‐Time Dynamic Surface Control of Tank Horizontal Stabilization System With Disturbance
ABSTRACT Modern new tanks are difficult to achieve high precision control of the horizontal stabilization system due to electromechanical coupling and road surface disturbance during high‐speed combat. A predefined‐time dynamic surface controller (DSCPT) is proposed for the fast stabilization and strong disturbance problems of the tank horizontal ...
Zhicheng Fan +5 more
wiley +1 more source
Integrating recombinase-based feedback and feedforward control for optimal resource decoupling. [PDF]
Zhang R, Zhang R, Tian XJ.
europepmc +1 more source

