Results 201 to 210 of about 31,122 (308)
This review explores advances in wearable and lab‐on‐chip technologies for breast cancer detection. Covering tactile, thermal, ultrasound, microwave, electrical impedance tomography, electrochemical, microelectromechanical, and optical systems, it highlights innovations in flexible electronics, nanomaterials, and machine learning.
Neshika Wijewardhane +4 more
wiley +1 more source
Bilevel multiobjective control enhances arterial performance via spatiotemporal optimization of presignalized intersections. [PDF]
Pan J, Yang Q, Li P.
europepmc +1 more source
Transducers convert physical signals into electrical and optical representations, yet each mechanism is bounded by intrinsic trade‐offs across bandwidth, sensitivity, speed, and energy. This review maps transduction mechanisms across physical scale and frequency, showing how heterogeneous integration and multiphysics co‐design transform isolated ...
Aolei Xu +8 more
wiley +1 more source
A simplified proof of a cosmological singularity theorem. [PDF]
Galloway GJ, Ling E.
europepmc +1 more source
ABSTRACT Real‐time insight into local chemistry is critical for reliable part quality in additive manufacturing, especially laser powder bed fusion (PBF‑LB/M), where rapid thermal cycles and localized evaporation can undermine part performance. Optical emission spectroscopy (OES) offers non‑intrusive, in situ plume monitoring, but detection geometry ...
Philipp Gabriel +4 more
wiley +1 more source
The key technologies of a computer-aided design system for removable partial denture frameworks. [PDF]
Ma G +5 more
europepmc +1 more source
A custom shape memory polymer material selection process relates quantitative application criteria (e.g., compression garment yarns) to material characterization information. The selected materials are manufactured into yarn geometries, which expand the design space by creating structural stress‐strain profiles beyond the nominal material stress‐strain
Michaela Andrews +2 more
wiley +1 more source
IRN2Vec: A representation learning model for road network intersections by integrating geospatial attributes and travel behaviors. [PDF]
Yang X.
europepmc +1 more source

