Results 171 to 180 of about 13,233,408 (362)
Jump resonance in a third order nonlinear control system [PDF]
Wellington W. Koepsel
openalex +1 more source
Bandwidth-Controllable Third-Order Band Pass Filter Using Substrate-Integrated Full- and Semi-Circular Cavities. [PDF]
Pradhan NC +3 more
europepmc +1 more source
Objective Foot orthoses are thought to improve pain by potentially modifying internal mechanical forces. To test this, we explored whether foot orthoses can modify patterns of bone marrow lesions (BMLs) in people with midfoot pain. Methods Forty‐two people were recruited with midfoot pain and MRI‐confirmed midfoot BMLs.
Jill Halstead +4 more
wiley +1 more source
Commentary: First-Order Embodiment, Second-Order Embodiment, Third-Order Embodiment
Lisa Quadt
doaj +1 more source
Oscillation criteria for third order nonlinear differential equations [PDF]
Paul Waltman
openalex +1 more source
Third-order motifs are sufficient to fully and uniquely characterize spatiotemporal neural network activity. [PDF]
Deshpande SS, Smith GA, van Drongelen W.
europepmc +1 more source
Objective This systematic review aimed to assess the diagnostic accuracy of algorithms used to identify rheumatoid arthritis (RA) and juvenile idiopathic arthritis (JIA) in electronic health records (EHRs). Methods We searched MEDLINE, Embase, and CENTRAL databases and included studies that validated case definitions against a reference standard such ...
Constanza Saka‐Herrán +10 more
wiley +1 more source
Ablation and optical third-order nonlinearities in Ag nanoparticles
Carlos Torres-Torres1, Néstor Peréa-López2, Jorge Alejandro Reyes-Esqueda3, Luis Rodríguez-Fernández3, Alejandro Crespo-Sosa3, Juan Carlos Cheang-Wong3, Alicia Oliver31Section of Graduate Studies and
Carlos Torres-Torres +3 more
doaj
Retracted: AGTH-Net: Attention-Based Graph Convolution-Guided Third-Order Hourglass Network for Sports Video Classification. [PDF]
Healthcare Engineering JO.
europepmc +1 more source
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 +4 more
wiley +1 more source

