Results 201 to 210 of about 400,456 (327)

Langevin differential equation in frame of ordinary and Hadamard fractional derivatives under three point boundary conditions

, 2021
Yassine Adjabi, Mohammad Esmael Samei, Mohammed M. Matar, Jehad Alzabut   +3 more
openalex   +1 more source

Evaluation of a novel EHR sidecar application to display RA clinical outcomes during clinic visits: results of a stepped‐wedge cluster randomized pragmatic trial

Arthritis Care &Research, Accepted Article.
Objective We developed a novel EHR sidecar application to visualize key rheumatoid arthritis (RA) outcomes, including disease activity, physical function, and pain, via a patient‐facing graphical interface designed for use during outpatient visits (“RA PRO dashboard”).
Gabriela Schmajuk, Jing Li, Cathy Nasrallah, Cherish Wilson, Alicia Hamblin, Christine Hariz, Cammie Young, Stephen Shiboski, Emma Kersey, Mary Nakamura, Judith F. Ashouri, Mehrdad Matloubian, Andrew J. Gross, Jennifer Barton, Beth Berrean, Jonathan Prugh, Jinoos Yazdany   +16 more
wiley   +1 more source

HiDeS: a higher-order-derivative-supervised neural ordinary differential equation for multi-robot systems and opinion dynamics. [PDF]

Front Neurorobot
Li M, Bian W, Chen L, Liu M.
europepmc   +1 more source

Generalized Ordinary Differential Equation Models. [PDF]

J Am Stat Assoc, 2014
Miao H, Wu H, Xue H.
europepmc   +1 more source

Association Between Symptomatic Knee Osteoarthritis and Target Glycemic Control in Individuals with Type 2 Diabetes

Arthritis Care &Research, Accepted Article.
Objective Knee osteoarthritis (OA) commonly affects individuals with Type 2 diabetes (T2DM) and is associated with increased risk of diabetes‐related complications. To better understand potential mechanisms, we examined the association between symptomatic knee OA and glycemic control in individuals with T2DM.
Lauren K. King, Alanna Weisman, Baiju R. Shah, Robert Goldberg, Amish Parikh, Ian Stanaitis, Vivian Hung, Rosane Nisenbaum, Abdalmohsen Almodahka, Lorraine Lipscombe, Gillian A. Hawker   +10 more
wiley   +1 more source

Parameter estimation and forecasting with quantified uncertainty for ordinary differential equation models using QuantDiffForecast: A MATLAB toolbox and tutorial. [PDF]

Stat Med
Chowell G, Bleichrodt A, Luo R.
europepmc   +1 more source

Tailored parameter optimization methods for ordinary differential equation models with steady-state constraints. [PDF]

BMC Syst Biol, 2016
Fiedler A, Raeth S, Theis FJ, Hausser A, Hasenauer J.   +4 more
europepmc   +1 more source

Derivation of BBDF-α for solving ordinary differential equation [PDF]

, 2016
Iskandar Shah Mohd Zawawi, Zarina Bibi İbrahim   +1 more
openalex   +1 more source

Optimized Time–Frequency Analysis for Induction Motor Fault Detection Using Hybrid Differential Evolution and Deep Learning Techniques

International Journal of Adaptive Control and Signal Processing, EarlyView.
Workflow of the parameter optimization process for ITSC fault detection, applying Differential Evolution optimization and the Smooth Pseudo Wigner‐Ville Distribution for signal processing. The optimized parameters are then used in the failure identification pipeline, which combines the signal processing with a YOLO‐based architecture for fault severity
Rafael Martini Silva, Avyner Lorran de Oliveira Vitor, Marcelo Favoretto Castoldi, Alessandro Goedtel, Wesley Angelino Souza   +4 more
wiley   +1 more source

A Comparison of Two-Stage Approaches for Fitting Nonlinear Ordinary Differential Equation Models with Mixed Effects. [PDF]

Multivariate Behav Res, 2016
Chow SM, Bendezú JJ, Cole PM, Ram N.
europepmc   +1 more source