Female bees and wasps demonstrate, through their performance of elaborate learning flights, when and where they memorise features of a significant site. An important feature of these flights is that the insects look back to fixate the site that they are leaving. Females, which forage for nectar and pollen and return with it to the nest, execute learning flights on their initial departure from both their nest and newly discovered flowers. To our knowledge, these flights have so far only been studied in females. Here, we describe and analyse putative learning flights observed in male bumblebees L. Once male bumblebees are mature, they leave their nest for good and fend for themselves. We show that, unlike female foragers, males always fly directly away from their nest, without looking back, in keeping with their indifference to their natal nest. In contrast, after males have drunk from artificial flowers, their flights on first leaving the flowers resemble the learning flights of females, particularly in their fixation of the flowers. These differences in the occurrence of female and male learning flights seem to match the diverse needs of the two sexes to learn about disparate, ecologically relevant places in their surroundings.
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Struct Dyn
November 2024
Second Target Station, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA.
We introduce a computational framework that integrates artificial intelligence (AI), machine learning, and high-performance computing to enable real-time steering of neutron scattering experiments using an edge-to-exascale workflow. Focusing on time-of-flight neutron event data at the Spallation Neutron Source, our approach combines temporal processing of four-dimensional neutron event data with predictive modeling for multidimensional crystallography. At the core of this workflow is the Temporal Fusion Transformer model, which provides voxel-level precision in predicting 3D neutron scattering patterns.
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December 2024
Key Laboratory of Advanced Manufacturing Technology, Ministry of Education, Guizhou University, Guiyang, 550025, Guizhou, China.
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December 2024
Faculty of Science and Technology, Seikei University, 3-3-1 Kichijoji-Kitamachi, Musashino, Tokyo, 180-8633, Japan.
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Air Traffic Management Institute, Civil Aviation Flight University of China, Deyang 618307, China.
This paper proposes an Improved Spider Wasp Optimizer (ISWO) to address inaccuracies in calculating the population (N) during iterations of the SWO algorithm. By innovating the population iteration formula and integrating the advantages of Differential Evolution and the Crayfish Optimization Algorithm, along with introducing an opposition-based learning strategy, ISWO accelerates convergence. The adaptive parameters trade-off probability (TR) and crossover probability (Cr) are dynamically updated to balance the exploration and exploitation phases.
View Article and Find Full Text PDFAnal Chem
December 2024
Department of Chemistry, The University of Kansas, Lawrence, Kansas 66045, United States.
We are developing a unique protein identification method that consists of generating peptides proteolytically from a single protein molecule (i.e., peptide fingerprints) with peptide detection and identification carried out using nanoscale electrochromatography and label-free resistive pulse sensing (RPS).
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