From Droplet to Diagnosis: Spatio‐Temporal Pattern Recognition in Drying Biofluids
This article integrates machine learning (ML) with the spatio‐temporal evolution of biofluid droplets to reveal how drying and self‐assembly encode distinctive compositional fingerprints. By leveraging textural features and interpretable ML, it achieves robust classification of blood abnormalities with over 95% accuracy.
Anusuya Pal +2 more
wiley +1 more source
Mapping the prosthodontic workforce in Saudi Arabia: Patterns, training backgrounds, professional ranks, and regional distributions. [PDF]
Almalki A +5 more
europepmc +1 more source
Aristotle, US Public Diplomacy, and the Cold War: The Work of Carnes Lord [PDF]
A. Alexiev +25 more
core +2 more sources
Illustration of text data mining of rare earth mineral thermodynamic parameters with the large language model‐powered LMExt. A dataset is built with mined thermodynamic properties. Subsequently, a machine learning model is trained to predict formation enthalpy from the dataset.
Juejing Liu +6 more
wiley +1 more source
Dural-Based Plasmacytoma Presenting As the Initial Sign of Multiple Myeloma. [PDF]
Alghamdi KT +5 more
europepmc +1 more source
Female desert locusts dig underground to lay their eggs. They displace soil, rather than removing it, to create a tunnel. We analyze burrowing dynamics and 3D kinematics and design a locust‐inspired hybrid soft–stiff robot that reproduces this mechanism. The results show the natural strategy minimizes energy, whereas alternative patterns raise costs up
Shai Sonnenreich +2 more
wiley +1 more source
Medical specialists in LMICs: a systematic review and best-fit framework synthesis of the evidence on their roles and contribution to health systems. [PDF]
Russo G +5 more
europepmc +1 more source
Workshop on manpower development and training in toxicology and chemical safety. Luxembourg, 28 November - 2 December 1983. Industrial health and safety. EUR 9619 EN [PDF]
Berlin, A., Smith, E., Tarkowski, S.
core
Feature Disentangling and Combination Implemented by Spin–Orbit Torque Magnetic Tunnel Junctions
Spin–orbit torque magnetic tunnel junctions (SOT‐MTJs) enable efficient feature disentangling and integration in image data. A proposed algorithm leverages SOT‐MTJs as true random number generators to disentangle and recombine features in real time, with experimental validation on emoji and facial datasets.
Xiaohan Li +15 more
wiley +1 more source

