The PRIMA Thesaurus for Materials Science and Engineering
The PRIMA Thesaurus is a structured vocabulary designed to improve how materials science data is described and shared. Developed with input from multiple experts, it enables clear documentation of research workflows, data exchange, and reuse across platforms.
Rossella Aversa +8 more
wiley +1 more source
The last decade of air pollution epidemiology and the challenges of quantitative risk assessment. [PDF]
Forastiere F +3 more
europepmc +1 more source
Training in tools to develop quantitative risk assessment of fresh produce using water reuse systems in Mediterranean production. [PDF]
Papadopoulos T +4 more
europepmc +1 more source
Reproduction of stacking fault energy calculations from literature with a semi‐automated large language model‐assisted extraction procedure: extraction of simulation protocol, atomistic structures, computational parameters, and reported results, ontology alignment, knowledge graph construction and, finally, recomputation forvalidation.
Sepideh Baghaee Ravari +5 more
wiley +1 more source
Exploring frameworks for quantitative risk assessment of antimicrobial resistance along the food chain. [PDF]
Mandel T +3 more
europepmc +1 more source
Quantitative Risk Assessment of African Swine Fever Introduction into Spain by Legal Import of Live Pigs. [PDF]
Muñoz-Pérez C +4 more
europepmc +1 more source
A combined experimental–computational framework identifies energy‐dependent laser absorptivity for NiTi in laser powder‐bed fusion, applicable to conduction and transition modes. Single‐track experiments and thermofluid smoothed particle hydrodynamics simulations are coupled through inverse analysis of melt pool geometry.
Mohamadreza Afrasiabi +3 more
wiley +1 more source
The impact of graded nursing interventions based on quantitative risk assessment on psychological stress responses in patients undergoing resection for primary liver cancer. [PDF]
Zhang L, Ren X, Xu L, Zheng S, Xu Q.
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

