Results 171 to 180 of about 1,894,382 (325)

Barriers and Facilitators for Implementing Resilience Coaching for Youth With Chronic Musculoskeletal Pain: Pediatric Rheumatologists’ Perspectives

Arthritis Care &Research, EarlyView.
Objective Promoting Resilience in Stress Management (PRISM) is a resilience coaching program designed for adolescents with chronic illness. We aimed to examine the perceived feasibility, acceptability, and appropriateness of PRISM among pediatric rheumatologists treating adolescents with chronic musculoskeletal pain and obtain recommendations for ...
Sabrina Gmuca, Anyun Chatterjee, Mackenzie McGill, Nellie Butler, Katherine S. Kellom, Jami F. Young, Tonya M. Palermo, Pamela F. Weiss, Abby R. Rosenberg, Peter F. Cronholm   +9 more
wiley   +1 more source

Dynamical systems game theory II [PDF]

, 2002
Eizo Akiyama, Kunihiko Kaneko
openalex   +1 more source

What can one learn about Self-Organized Criticality from Dynamical Systems theory ?

, 1999
Ph. Blanchard, Bruno Cessac, Tyll Krueger   +2 more
openalex   +2 more sources

Homology theory and dynamical systems

Topology, 1975
Shub, M., Sullivan, D.
openaire   +3 more sources

Quorum-Sensing Synchronization of Synthetic Toggle Switches: A Design Based on Monotone Dynamical Systems Theory. [PDF]

PLoS Comput Biol, 2016
Nikolaev EV, Sontag ED.
europepmc   +1 more source

Data‐driven forecasting of ship motions in waves using machine learning and dynamic mode decomposition

International Journal of Adaptive Control and Signal Processing, EarlyView.
Summary Data‐driven forecasting of ship motions in waves is investigated through feedforward and recurrent neural networks as well as dynamic mode decomposition. The goal is to predict future ship motion variables based on past data collected on the field, using equation‐free approaches.
Matteo Diez, Mauro Gaggero, Andrea Serani   +2 more
wiley   +1 more source

Qualitative Theory of Hybrid Dynamical Systems

, 2000
Alexey S. Matveev, Andrey V. Savkin
openalex   +1 more source

A Q‐Learning Algorithm to Solve the Two‐Player Zero‐Sum Game Problem for Nonlinear Systems

International Journal of Adaptive Control and Signal Processing, Volume 39, Issue 3, Page 566-581, March 2025.
A Q‐learning algorithm to solve the two‐player zero‐sum game problem for nonlinear systems. ABSTRACT This paper deals with the two‐player zero‐sum game problem, which is a bounded L2$$ {L}_2 $$‐gain robust control problem. Finding an analytical solution to the complex Hamilton‐Jacobi‐Issacs (HJI) equation is a challenging task.
Afreen Islam, Anthony Siming Chen, Guido Herrmann   +2 more
wiley   +1 more source

Dynamical systems game theory and dynamics of games

, 2000
Eizo Akiyama, Kunihiko Kaneko
openalex   +1 more source

Dynamical system approach to FRW models in higher-order gravity theories [PDF]

, 2003
John Miritzis
openalex   +1 more source