Results 181 to 190 of about 1,552,062 (375)
Explorative Synthetic Biology in AI: Criteria of Relevance and a Taxonomy for Synthetic Models of Living and Cognitive Processes [PDF]
Luisa Damiano, Pasquale Stano
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This article offers a comprehensive review of topic modeling techniques, tracing their evolution from inception to recent developments. It explores methods such as latent Dirichlet allocation, latent semantic analysis, non‐negative matrix factorization, probabilistic latent semantic analysis, Top2Vec, and BERTopic, highlighting their strengths ...
Pratima Kumari +6 more
wiley +1 more source
There is a knowledge gap about the quantitative aspects of mycorrhizal fungi’s influence on ecological succession on tailings. Here, we demonstrate that inoculating mine tailings with 2% fungi yields significantly better results in terms of plant biomass
Aurora Neagoe +7 more
doaj +1 more source
Biology and External Morphology of the Immature Stages of the ButterflyCallicore pygas eucale, with Comments on the Taxonomy of the GenusCallicore(Nymphalidae: Biblidinae) [PDF]
Fernando Maia Silva Dias +2 more
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A Taxonomy-Agnostic Approach to Targeted Microbiome Therapeutics—Leveraging Principles of Systems Biology [PDF]
Kyle D. Brumfield +3 more
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The study presents a prototype novel workflow that utilizes artificial intelligence for the digitization of natural history collections, which would fit within a robotic‐integrated system. The work focuses on a collaborative‐robot (cobot) inspired workflow to digitize one of the largest collections housed at the Natural History Museum UK: pinned ...
Naifeng Zhang +4 more
wiley +1 more source
Taxonomy, biology, and clinical aspects of Fusarium species
P. Nelson, M. Dignani, E. Anaissie
semanticscholar +1 more source
A Taxonomic Index, with Names of Descriptive Authorities of Termite Genera and Species: An Accompaniment toBiology of Termites: A Modern Synthesis(Bignell DE, Roisin Y, Lo N, Editors. 2011. Springer, Dordrecht. 576 pp.) [PDF]
David E. Bignell, David Jones
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This review outlines how recurrent neural networks model multisensory integration by capturing temporal and probabilistic features of sensory input. Key developments, challenges, and future directions are summarized, providing insights into biologically inspired AI. Multisensory integration (MSI) is a core brain function underlying perception, learning,
Ehsan Bolhasani +2 more
wiley +1 more source

