Results 151 to 160 of about 4,816,952 (284)
Early‐life exposure to a high‐fat diet altered intact Achilles tendons in rat offspring, making them thinner, stiffer, and molecularly distinct even without injury. These findings suggest that developmental high‐fat diet exposure may impair tendon quality and increase susceptibility to mechanical overload or tendon injury later in life.
Heyong Yin +3 more
wiley +1 more source
Two-Phase Approach for Fast Topology Optimization of Multi-Resonant MEMS Involving Model Order Reduction. [PDF]
Hu S +4 more
europepmc +1 more source
Genetic Algorithm-Based Model Order Reduction of Aeroservoelastic Systems with Consistent States. [PDF]
Zhu J +4 more
europepmc +1 more source
UiO‐66(Zr) metal–organic frameworks are chemically stable, biocompatible, and highly tunable nanomaterials. Their modular structure enables controlled drug delivery, multimodal bioimaging, and light‐activated photodynamic therapy, supporting integrated diagnostic and therapeutic (theranostic) applications in cancer and biomedical research.
Veronika Huntošová +2 more
wiley +1 more source
A new model order reduction strategy adapted to nonlinear problems in earthquake engineering. [PDF]
Bamer F, Amiri AK, Bucher C.
europepmc +1 more source
Cutaneous Melanoma Drives Metabolic Changes in the Aged Bone Marrow Immune Microenvironment
Melanoma, the deadliest form of skin cancer, increasingly affects older adults. Our study reveals that melanoma induces changes in iron and lipid levels in the bone marrow, impacting immune cell populations and increasing susceptibility to ferroptosis.
Alexis E. Carey +12 more
wiley +1 more source
Calibration-Based ALE Model Order Reduction for Hyperbolic Problems with Self-Similar Travelling Discontinuities. [PDF]
Nonino M, Torlo D.
europepmc +1 more source
Design and Parametric Study of the Magnetic Sensor for Position Detection in Linear Motor Based on Nonlinear Parametric model order reduction. [PDF]
Paul S, Chang J.
europepmc +1 more source
Nonlinear Parametid Model Order Reduction
Model order reduction techniques are a powerful tool to ease the computational burden of simulating complex systems. By reducing the dimensionality of high-fidelity models, while preserving essential system dynamics, model order reduction enables faster and more efficient simulations without compromising accuracy.
openaire +1 more source

