Climate-stressed crop mycobiomes as pre-harvest sentinels of human mycotoxin exposure: an AI-enabled One Health framework linking dysbiosis, exposomics, and toxicopathology. [PDF]
Das SK, Karak P, Parvatikar P.
europepmc +1 more source
Effects of Myristica fragrans and Alpinia conchigera oils against Callosobruchus maculatus
Suthisut, Duangsamorn +4 more
doaj +1 more source
Building machine‐readable vocabularies for materials science is slow, expert‐driven work. This study benchmarks 13 large language models on two of its first steps: finding candidate terms in engineering articles and deciding where they belong in a class hierarchy.
Thomas Bjarsch +3 more
wiley +1 more source
African Crop Calendar: A geo-referenced dataset of crop growing seasons across diverse agro-ecological regions. [PDF]
Waha K +5 more
europepmc +1 more source
Symmetry‐Guided Multifunctional Acoustic System Based on Mechanically Actuated Sonic Crystals
This study presents the design, simulation, and experimental validation of amultifunctional acoustic metamaterial based on rotationally engineered sonic crystals.By tuning cylinder orientations, controllable band gaps and six distinct functionalities—including switching, topological insulation, beam splitting, and logic operations—areachieved ...
Yuanyan Zhao +2 more
wiley +1 more source
Assessing cotton boll-opening concentration for harvest decision-making via foundation model-enhanced cross-scale phenotyping. [PDF]
Chen M +12 more
europepmc +1 more source
Low‐voltage FIB‐SEM tomography combined with a image preprocessing pipeline improves phase contrast and enables reliable machine‐learning segmentation of conductive networks in lithium‐ion battery electrodes. Structural descriptors are extracted from segmented images, done semimanually and automated, and compared.
Lisa Beran +6 more
wiley +1 more source
HARVEST: A General-Purpose Platform for Mean-Field and Full-Field Composite Micromechanics and Its Validation with Polymer-Based Nanocomposites. [PDF]
Tüfekci M.
europepmc +1 more source
A Practical Noise2Noise Denoising Pipeline for High‐Throughput Raman Spectroscopy
A lightweight and reproducible denoising pipeline for high‐throughput Raman spectroscopy is introduced, based on a 1D convolutional autoencoder trained with a Noise2Noise strategy. Using only repeated short‐exposure acquisitions, the method suppresses stochastic noise without reference spectra, enabling reliable spectral reconstruction while preserving
David Martin‐Calle +5 more
wiley +1 more source

