Results 111 to 120 of about 16,379,809 (253)
BackgroundInvasive fractional flow reserve (FFR) is the gold standard for guiding coronary revascularization. Angiography-derived FFR techniques such as the quantitative flow ratio (QFR) provide a less invasive alternative.
András Ágoston +16 more
doaj +1 more source
Non-hydrostatic Conditions in High-pressure Devices: Analysis of Plastic Deformation with EDX
The effect of different pressure devices and pressure media on the compression behavior of elastically anisotropic polycrystals (Cu3Au, Ni3Al) is investigated with energy-dispersive X-ray diffraction (EDX) correlating information from both line profiles ...
Frommeyer, G. +2 more
core +1 more source
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park +19 more
wiley +1 more source
The aim of this paper is to examine the influence of high hydrostatic pressure treatment on the rheological and thermophisical properties of β-lactoglobulin and carragenan model systems. Suspensions of β-lactoglobulin were treated with a high hydrostatic
Edita Juraga +3 more
doaj
Hadal trenches are extreme environments situated over 6000 m below sea surface, where enormous hydrostatic pressure affects the biochemical cycling of elements.
Na Yang +4 more
doaj +1 more source
The effects of changing climate on faunal depth distributions determine winners and losers
Changing climate is predicted to impact all depths of the global oceans, yet projections of range shifts in marine faunal distributions in response to changing climate seldom evaluate potential shifts in depth distribution.
Sven Thatje +3 more
core +1 more source
Phonons‐informed machine‐learning predictive models are propitious for reproducing thermal effects in computational materials science studies. Machine learning (ML) methods have become powerful tools for predicting material properties with near first‐principles accuracy and vastly reduced computational cost.
Pol Benítez +4 more
wiley +1 more source
The role of sarcoplasmic protein in hydrostatic pressure-induced myofibrillar protein denaturation
ArticleTo observe the role of sarcoplasmic protein (SP) on myofibrillar protein (MP) denaturation under a hydrostatic pressure (HP), MP isolated from bovine muscle was treated with 300 MPa by increasing concentrations of SP (0, 0.8, 1.6, and 3.2 mg/ml ...
3071 +11 more
core +1 more source
A Two‐Stage Characterization Pipeline and Open‐Source Framework for Reproducible Tactile Sensing
The same soft tactile sensor returns different numbers when embodied in different robots. This is an Embodiment Gap that no shared framework currently captures transparently. A two‐stage characterization pipeline, paired with a FAIR open‐source digital datasheet, decouples intrinsic sensor behavior from embodiment effects and condenses cross‐laboratory
Matteo Lo Preti +6 more
wiley +1 more source
Robust attachment of hydrogels to solid substrates is critical to success in cell culture where hydrostatic and contractile forces can contribute to delamination of weakly adhered gels. This novel approach forms a nanometer‐scale, reagent‐free hydrogel primer layer to provide improved resistance to delamination of subsequent bulk hydrogel layers in a ...
Sophia C. Franklin +7 more
wiley +1 more source

