Results 241 to 250 of about 3,998,759 (349)
REPrise: de novo interspersed repeat detection using inexact seeding. [PDF]
Takeda A+4 more
europepmc +1 more source
Das liest die LIBREAS, Nummer #6 (Winter / Frühling 2020)
Redaktion Libreas
doaj +1 more source
Children's searching behavior on browsing and keyword online catalogs: The Science Library Catalog project [PDF]
Christine L. Borgman+3 more
openalex +1 more source
Bridging Nature and Technology: A Perspective on Role of Machine Learning in Bioinspired Ceramics
Machine learning (ML) is revolutionizing the development of bioinspired ceramics. This article investigates how ML can be used to design new ceramic materials with exceptional performance, inspired by the structures found in nature. The research highlights how ML can predict material properties, optimize designs, and create advanced models to unlock a ...
Hamidreza Yazdani Sarvestani+2 more
wiley +1 more source
Construction of cDNA library of Dalbergia odorifera induced by low temperature stress and screening of low temperature tolerant genes. [PDF]
Li S, An X, Li F, Chen Y, Li X.
europepmc +1 more source
PERGAMON INTERNATIONAL LIBRARY of Science, Technology, Engineering and Social Studise
openalex +1 more source
A Case‐Based Reasoning Approach to Model Manufacturing Constraints for Impact Extrusion
A hybrid modeling approach is presented that combines constraint‐based process modeling and case‐based reasoning. The model formalizes manufacturing constraints and integrates simulation data to model complex manufacturing processes. The approach supports manufacturability analysis during product design through an adaptive modeling environment.
Kevin Herrmann+5 more
wiley +1 more source
Subtelomeric repeat expansion in Hydractinia symbiolongicarpus chromosomes. [PDF]
Kon T, Kon-Nanjo K, Simakov O.
europepmc +1 more source
In this study, the mechanical response of Y‐shaped core sandwich beams under compressive loading is investigated, using deep feed‐forward neural networks (DFNNs) for predictive modeling. The DFNN model accurately captures stress–strain behavior, influenced by design parameters and loading rates.
Ali Khalvandi+4 more
wiley +1 more source